title: FOTS: Fast Oriented Text Spotting with a Unified Network

abstract: Incidental scene text spotting is considered one of the most difficult and valuable challenges in the document analysis community. Most existing methods treat text detection and recognition as separate tasks. In this work, we propose a unified end-to-end trainable Fast Oriented Text Spotting (FOTS) network for simultaneous detection and recognition, sharing computation and visual information among the two complementary tasks. Specially, RoIRotate is introduced to share convolutional features between detection and recognition. Benefiting from convolution sharing strategy, our FOTS has little computation overhead compared to baseline text detection network, and the joint training method learns more generic features to make our method perform better than these two-stage methods. Experiments on ICDAR 2015, ICDAR 2017 MLT, and ICDAR 2013 datasets demonstrate that the proposed method outperforms state-of-the-art methods significantly, which further allows us to develop the first real-time oriented text spotting system which surpasses all previous state-of-the-art results by more than 5% on ICDAR 2015 text spotting task while keeping 22.6 fps.


title: YAGO3: A Knowledge Base from Multilingual Wikipedias

abstract: We present YAGO3, an extension of the YAGO knowledge base that combines the information from the Wikipedias in multiple languages. Our technique fuses the multilingual information with the English WordNet to build one coherent knowledge base. We make use of the categories, the infoboxes, and Wikidata, and learn the meaning of infobox attributes across languages. We run our method on 10 different languages, and achieve a precision of 95%-100% in the attribute mapping. Our technique enlarges YAGO by 1m new entities and 7m new facts.


title: Multi-User Computation Offloading in Mobile Edge Computing: A Behavioral Perspective

abstract: By providing cloud computing capabilities at the network edge in proximity of mobile device users, mobile edge computing offers an effective solution to help mobile devices with computation- intensive and delay-sensitive tasks. In this article, we investigate the multi-user computation offloading problem in an uncertain wireless environment. Most of the existing works assume that mobile device users are rational and make offloading decisions to maximize their expected objective utilities. However, in practice, users tend to have subjective perceptions under uncertainty, such that their behavior deviates considerably from the conventional rationality assumption. Drawing on the framework of prospect theory (PT), we formulate users' decision making of whether to offload or not as a PT-based non-cooperative game. We propose a distributed computation offloading algorithm to achieve the Nash equilibrium of the game. Numerical results assess the impact of mobile device users' behavioral biases on offloading decision making.


title: The Password Life Cycle

abstract: Managing passwords is a difficult task for users, who must create, remember, and keep track of large numbers of passwords. In this work, we investigated users’ coping strategies for password management. Through a series of interviews, we identified a “life cycle” of password use and find that users’ central task in coping with their passwords is rationing their effort to best protect their important accounts. We followed up this work by interviewing experts about their password management practices and found that experts rely on the same kinds of coping strategies as non-experts, but that their increased situation awareness of security allows them to better ration their effort into protecting their accounts. Finally, we conducted a survey study to explore how the life cycle model generalizes to the larger population and find that the life cycle and rationing patterns can be seen in the broader population, but that survey respondents were less likely to characterize security management as a challenging task.


title: A program differencing algorithm for verilog HDL

abstract: During code review tasks, comparing two versions of a hardware design description using existing program differencing tools such as diff is inherently limited because existing program differencing tools implicitly assume sequential execution semantics, while hardware description languages are designed to model concurrent computation. We designed a position-independent differencing algorithm to robustly handle language constructs whose relative orderings do not matter. This paper presents Vdiff, an instantiation of this position-independent differencing algorithm for Verilog HDL. To help programmers reason about the differences at a high-level, Vdiff outputs syntactic differences in terms of Verilog-specific change types. We evaluated Vdiff on two open source hardware design projects. The evaluation result shows that Vdiff is very accurate, with overall 96.8% precision and 97.3% recall when using manually classified differences as a basis of comparison.


title: Wavelet-Based Capacity Configuration and Coordinated Control of Hybrid Energy Storage System for Smoothing Out Wind Power Fluctuations

abstract: Stochastically fluctuating wind power has a negative impact on power grid operations. This paper presents a wind power filtering approach to mitigate short- and long-term fluctuations using a hybrid energy storage system (HESS), and a novel wavelet-based capacity configuration algorithm to properly size the HESS. A frequency distribution allocates wind power fluctuations to the different HESS components to more easily satisfy 1-min and 30-min fluctuation mitigation requirements (FMR). An ultra-capacitor bank (UC) mitigates short-term fluctuations. In the HESS, and a lithium-ion battery bank (LB) minimizes long-term fluctuations. This paper also proposes a novel online-wavelet based coordination control scheme for the HESS, consisting of primary filtering (PF) and secondary filtering (SF) stages. The PF stage obtains a combined power output that fully satisfies the FMRs, while the SF stage provides additional smoothing of the wind power output fluctuations after the PF stage. A remaining energy level (REL) feedback control maintains the REL of the battery bank within its proper range. Case studies demonstrate that the proposed wavelet-based algorithm is more efficient than other published algorithms, and needs a lower energy storage capacity to satisfy 1-min and 30-min FMRs.


Predicting The Occurrence of Weather And Volume Related Ground Delay Programs


title: Behavioral and neural correlates of perceived and imagined musical timbre

abstract: The generality of findings implicating secondary auditory areas in auditory imagery was tested by using a timbre imagery task with fMRI. Another aim was to test whether activity in supplementary motor area (SMA) seen in prior studies might have been related to subvocalization. Participants with moderate musical background were scanned while making similarity judgments about the timbre of heard or imagined musical instrument sounds. The critical control condition was a visual imagery task. The pattern of judgments in perceived and imagined conditions was similar, suggesting that perception and imagery access similar cognitive representations of timbre. As expected, judgments of heard timbres, relative to the visual imagery control, activated primary and secondary auditory areas with some right-sided asymmetry. Timbre imagery also activated secondary auditory areas relative to the visual imagery control, although less strongly, in accord with previous data. Significant overlap was observed in these regions between perceptual and imagery conditions. Because the visual control task resulted in deactivation of auditory areas relative to a silent baseline, we interpret the timbre imagery effect as a reversal of that deactivation. Despite the lack of an obvious subvocalization component to timbre imagery, some activity in SMA was observed, suggesting that SMA may have a more general role in imagery beyond any motor component. © 2004 Elsevier Ltd. All rights reserved.


title: Robust repair of polygonal models

abstract: We present a robust method for repairing arbitrary polygon models. The method is guaranteed to produce a closed surface that partitions the space into disjoint internal and external volumes. Given any model represented as a polygon soup, we construct an inside/outside volume using an octree grid, and reconstruct the surface by contouring. Our novel algorithm can efficiently process large models containing millions of polygons and is capable of reproducing sharp features in the original geometry.


title: Active Inference and Learning in the Cerebellum

abstract: This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and delay or trace conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme's anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the extrinsic and intrinsic connectivity of the cerebellar eyeblink conditioning system. Finally, we try to establish predictive validity by reproducing selective failures of delay conditioning, trace conditioning, and extinction using simulated and reversible focal lesions. Although rather metaphorical, the ensuing scheme can account for a remarkable range of anatomical and neurophysiological aspects of cerebellar circuitry-and the specificity of lesion-deficit mappings that have been established experimentally. From a computational perspective, this work shows how conditioning or learning can be formulated in terms of minimizing variational free energy or maximizing Bayesian model evidence using exactly the same principles that underlie predictive coding in perception.


title: DFD: Efficient Functional Dependency Discovery

abstract: The discovery of unknown functional dependencies in a dataset is of great importance for database redesign, anomaly detection and data cleansing applications. However, as the nature of the problem is exponential in the number of attributes none of the existing approaches can be applied on large datasets. We present a new algorithm DFD for discovering all functional dependencies in a dataset following a depth-first traversal strategy of the attribute lattice that combines aggressive pruning and efficient result verification. Our approach is able to scale far beyond existing algorithms for up to 7.5 million tuples, and is up to three orders of magnitude faster than existing approaches on smaller datasets.


title: Process Monitoring and Modeling using the Self-Organizing Map

abstract: The Self-Organizing Map (SOM) is a powerful neural network method for analysis and visualization of high-dimensional data. It maps nonlinear statistical dependencies between high-dimensional measurement data into simple geometric rela- tionships on a usually two-dimensional grid. The mapping roughly preserves the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. The need for visualization and clustering occurs, for instance, in the analysis of various engineering problems. In this paper, the SOM has been applied in monitoring and modeling of complex industrial processes. Case studies, including pulp process, steel production, and paper industry are described.


title: A system for rapid exploration of shader optimization choices

abstract: We present Spire, a shading language and compiler framework that facilitates rapid exploration of shader optimization choices (such as frequency reduction and algorithmic approximation) afforded by modern real-time graphics engines. Our design combines ideas from rate-based shader programming with new language features that expand the scope of shader execution beyond traditional GPU hardware pipelines, and enable a diverse set of shader optimizations to be described by a single mechanism: overloading shader terms at various spatio-temporal computation rates provided by the pipeline. In contrast to prior work, neither the shading language's design, nor our compiler framework's implementation, is specific to the capabilities of any one rendering pipeline, thus Spire establishes architectural separation between the shading system and the implementation of modern rendering engines (allowing different rendering pipelines to utilize its services). We demonstrate use of Spire to author complex shaders that are portable across different rendering pipelines and to rapidly explore shader optimization decisions that span multiple compute and graphics passes and even offline asset preprocessing. We further demonstrate the utility of Spire by developing a shader level-of-detail library and shader auto-tuning system on top of its abstractions, and demonstrate rapid, automatic re-optimization of shaders for different target hardware platforms.


title: Broadcast Encryption

abstract: We introduce new theoretical measures for the qualitative and quantitative assessment of encryption schemes designed for broadcast transmissions. The goal is to allow a central broadcast site to broadcast secure transmissions to an arbitrary set of recipients while minimizing key management related transmissions. We present several schemes that allow a center to broadcast a secret to any subset of privileged users out of a universe of size n so that coalitions of k users not in the privileged set cannot learn the secret. The most interesting scheme requires every user to store O(klog klog n) keys and the center to broadcast O(k2 log2 k log n) messages regardless of the size of the privileged set. This scheme is resilient to any coalition of k users. We also present a scheme that is resilient with probability p against a random subset of k users. This scheme requires every user to store O(log k log(l/p)) keys and the center to broadcast O(klog2 fclog(l/p)) messages.


title: A general bootstrap performance diagnostic

abstract: As datasets become larger, more complex, and more available to diverse groups of analysts, it would be quite useful to be able to automatically and generically assess the quality of estimates, much as we are able to automatically train and evaluate predictive models such as classifiers. However, despite the fundamental importance of estimator quality assessment in data analysis, this task has eluded highly automatic solutions. While the bootstrap provides perhaps the most promising step in this direction, its level of automation is limited by the difficulty of evaluating its finite sample performance and even its asymptotic consistency. Thus, we present here a general diagnostic procedure which directly and automatically evaluates the accuracy of the bootstrap's outputs, determining whether or not the bootstrap is performing satisfactorily when applied to a given dataset and estimator. We show that our proposed diagnostic is effective via an extensive empirical evaluation on a variety of estimators and simulated and real datasets, including a real-world query workload from Conviva, Inc. involving 1.7TB of data (i.e., approximately 0.5 billion data points).


Finding motifs in time series


title: Joint Example-Based Depth Map Super-Resolution

abstract: The fast development of time-of-flight (ToF) cameras in recent years enables capture of high frame-rate 3D depth maps of moving objects. However, the resolution of depth map captured by ToF is rather limited, and thus it cannot be directly used to build a high quality 3D model. In order to handle this problem, we propose a novel joint example-based depth map super-resolution method, which converts a low resolution depth map to a high resolution depth map, using a registered high resolution color image as a reference. Different from previous depth map SR methods without training stage, we learn a mapping function from a set of training samples and enhance the resolution of the depth map via sparse coding algorithm. We further use a reconstruction constraint to make object edges sharper. Experimental results show that our method outperforms state-of-the-art methods for depth map super-resolution.


title: Stressful Work, Psychological Job Strain, and Turnover: A 2-Year Prospective Cohort Study of Truck Drivers.

abstract: Based on a model that combines existing organizational stress theory and job transition theory, this 2-year longitudinal study examined antecedents and consequences of turnover among Dutch truck drivers. For this purpose, self-reported data on stressful work (job demands and control), psychological strain (need for recovery after work and fatigue), and turnover were obtained from 820 drivers in 1998 and 2000. In agreement with the model, the results showed that strain mediates the influence of stressful work on voluntary turnover. Also in conformity with the model, job movement to any job outside the trucking industry (i.e., interoccupational turnover) resulted in a larger strain reduction as compared to job movement within the trucking industry (intraoccupational turnover). Finally, strain was found to stimulate interoccupational turnover more strongly than it stimulated intraoccupational turnover. These findings provide a thorough validation of existing turnover theory and give new insights into the turnover (decision) process.


title: Non-rigid Object Tracking via Deformable Patches Using Shape-Preserved KCF and Level Sets

abstract: Part-based trackers are effective in exploiting local details of the target object for robust tracking. In contrast to most existing part-based methods that divide all kinds of target objects into a number of fixed rectangular patches, in this paper, we propose a novel framework in which a set of deformable patches dynamically collaborate on tracking of non-rigid objects. In particular, we proposed a shape-preserved kernelized correlation filter (SP-KCF) which can accommodate target shape information for robust tracking. The SP-KCF is introduced into the level set framework for dynamic tracking of individual patches. In this manner, our proposed deformable patches are target-dependent, have the capability to assume complex topology, and are deformable to adapt to target variations. As these deformable patches properly capture individual target subregions, we exploit their photometric discrimination and shape variation to reveal the trackability of individual target subregions, which enables the proposed tracker to dynamically take advantage of those subregions with good trackability for target likelihood estimation. Finally the shape information of these deformable patches enables accurate object contours to be computed as the tracking output. Experimental results on the latest public sets of challenging sequences demonstrate the effectiveness of the proposed method.


title: Stadium Hashing: Scalable and Flexible Hashing on GPUs

abstract: Hashing is one of the most fundamental operations that provides a means for a program to obtain fast access to large amounts of data. Despite the emergence of GPUs as many-threaded general purpose processors, high performance parallel data hashing solutions for GPUs are yet to receive adequate attention. Existing hashing solutions for GPUs not only impose restrictions (e.g., inability to concurrently execute insertion and retrieval operations, limitation on the size of key-value data pairs) that limit their applicability, their performance does not scale to large hash tables that must be kept out-of-core in the host memory. In this paper we present Stadium Hashing (Stash) that is scalable to large hash tables and practical as it does not impose the aforementioned restrictions. To support large out-of-core hash tables, Stash uses a compact data structure named ticket-board that is separate from hash table buckets and is held inside GPU global memory. Ticket-board locally resolves significant portion of insertion and lookup operations and hence, by reducing accesses to the host memory, it accelerates the execution of these operations. Split design of the ticket-board also enables arbitrarily large keys and values. Unlike existing methods, Stash naturally supports concurrent insertions and retrievals due to its use of double hashing as the collision resolution strategy. Furthermore, we propose Stash with collaborative lanes (clStash) that enhances GPU's SIMD resource utilization for batched insertions during hash table creation. For concurrent insertion and retrieval streams, Stadium hashing can be up to 2 and 3 times faster than GPU Cuckoo hashing for in-core and out-of-core tables respectively.


title: Multi-oriented Scene Text Detection via Corner Localization and Region Segmentation

abstract: Previous deep learning based state-of-the-art scene text detection methods can be roughly classified into two categories. The first category treats scene text as a type of general objects and follows general object detection paradigm to localize scene text by regressing the text box locations, but troubled by the arbitrary-orientation and large aspect ratios of scene text. The second one segments text regions directly, but mostly needs complex post processing. In this paper, we present a method that combines the ideas of the two types of methods while avoiding their shortcomings. We propose to detect scene text by localizing corner points of text bounding boxes and segmenting text regions in relative positions. In inference stage, candidate boxes are generated by sampling and grouping corner points, which are further scored by segmentation maps and suppressed by NMS. Compared with previous methods, our method can handle long oriented text naturally and doesn't need complex post processing. The experiments on ICDAR2013, ICDAR2015, MSRA-TD500, MLT and COCO-Text demonstrate that the proposed algorithm achieves better or comparable results in both accuracy and efficiency. Based on VGG16, it achieves an F-measure of 84.3% on ICDAR2015 and 81.5% on MSRA-TD500.


The Application of Neural Networks for Facial Landmarking on Mobile Devices


title: FIG: A Prototype Tool for Online Verification of Recovery

abstract: Network applications of the future will require advanced mechanisms for automatic failure recovery in order to provide an acceptable quality of service. Because of this requirement, there is a need for tools that can inject simulated faults to verify a system’s fault detection and recovery methods. We present a set of objectives that such a fault injection tool must meet, and describe a prototype we have developed that meets these criteria. This tool, FIG, is capable of selectively introducing and logging errors at the application/library boundary on a running system. Our expectation is that FIG will be used for software development and verification, as well as for on-site testing of production systems. We use a working version of this tool to test the behavior of several common UNIX applications under simulated failures and offer suggestions from these tests on how to develop software with a higher degree of failure recoverability.


title: Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes

abstract: Recently, models based on deep neural networks have dominated the fields of scene text detection and recognition. In this paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images. An end-to-end trainable neural network model for scene text spotting is proposed. The proposed model, named as Mask TextSpotter, is inspired by the newly published work Mask R-CNN. Different from previous methods that also accomplish text spotting with end-to-end trainable deep neural networks, Mask TextSpotter takes advantage of simple and smooth end-to-end learning procedure, in which precise text detection and recognition are acquired via semantic segmentation. Moreover, it is superior to previous methods in handling text instances of irregular shapes, for example, curved text. Experiments on ICDAR2013, ICDAR2015 and Total-Text demonstrate that the proposed method achieves state-of-the-art results in both scene text detection and end-to-end text recognition tasks.


title: On the effectiveness of machine and deep learning for cyber security

abstract: Machine learning is adopted in a wide range of domains where it shows its superiority over traditional rule-based algorithms. These methods are being integrated in cyber detection systems with the goal of supporting or even replacing the first level of security analysts. Although the complete automation of detection and analysis is an enticing goal, the efficacy of machine learning in cyber security must be evaluated with the due diligence. We present an analysis, addressed to security specialists, of machine learning techniques applied to the detection of intrusion, malware, and spam. The goal is twofold: to assess the current maturity of these solutions and to identify their main limitations that prevent an immediate adoption of machine learning cyber detection schemes. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic.


title: Soft grasping with wet adhesion: Preliminary evaluation

abstract: Locking two surfaces with minimum normal force may result in safe grasping of objects in soft robotic hands. This paper presents a preliminary approach on design and analysis of a bio-inspired soft pad that enhances the adhesion with the environment by morphological design of its surface at micro-scale. The design principle is originated from the biological wet attachment of a tree-frog toes with the surrounding environment, caused by capillary force and surface tension of a secretion film between the toe and the surface. Especially, the tree-frog's toe has a network of polygonal cells (or blocks) with grooves among them, which act as liquid reservoirs and capillary tubes. We conducted some analysis on this wet adhesion principle, showing that total normal force increases with the grooved pattern compared to the that of the flat one in wet condition. We then fabricated a micro-patterned mold, using e-beam technology, for casting grooved surface onto a silicon substrate. We also conducted preliminary investigation of the adhesion strength of the fabricated soft pad with measurement of normal force under wet and dry condition. This is the first time wet adhesion was considered in soft robotic grasping, and this research is expected to be applied in wet and high-moisture environment.


title: Domain2Vec: Deep Domain Generalization

abstract: We address the problem of domain generalization where a decision function is learned from the data of several related domains, and the goal is to apply it on an unseen domain successfully. It is assumed that there is plenty of labeled data available in source domains (also called as training domain), but no labeled data is available for the unseen domain (also called a target domain or test domain). We propose a novel neural network architecture, Domain2Vec (D2V) that learns domain-specific embedding and then uses this embedding to generalize the learning across related domains. The proposed algorithm, D2V extends the idea of distribution regression and kernelized domain generalization to the neural networks setting. We propose a neural network architecture to learn domain-specific embedding and then use this embedding along with the data point specific features to label it. We show the effectiveness of the architecture by accurately estimating domain to domain similarity. We evaluate our algorithm against standard domain generalization datasets for image classification and outperform other state of the art algorithms.


title: Robust Multi-Frame Super-Resolution Based on Spatially Weighted Half-Quadratic Estimation and Adaptive BTV Regularization

abstract: Multi-frame image super-resolution focuses on reconstructing a high-resolution image from a set of low-resolution images with high similarity. Combining image prior knowledge with fidelity model, the Bayesian-based methods have been considered as an effective technique in super-resolution. The minimization function derived from maximum a posteriori probability (MAP) is composed of a fidelity term and a regularization term. In this paper, based on the MAP estimation, we propose a novel initialization method for super-resolution imaging. For the fidelity term in our proposed method, the half-quadratic estimation is used to choose error norm adaptively instead of using fixed $L_{1}$ and $L_{2}$ norms. Besides, a spatial weight matrix is used as a confidence map to scale the estimation result. For the regularization term, we propose a novel regularization method based on adaptive bilateral total variation (ABTV). Both the fidelity term and the ABTV regularization guarantee the robustness of our framework. The fidelity term is mainly responsible for dealing with misregistration, blur, and other kinds of large errors, while the ABTV regularization aims at edge preservation and noise removal. The proposed scheme is tested on both synthetic data and real data. The experimental results illustrate the superiority of our proposed method in terms of edge preservation and noise removal over the state-of-the-art algorithms.


title: Implementation of image processing for detection of brain tumors

abstract: Processing of magnetic resonance images (MRI) is one among the parts of the image processing in medical field, which is the most emerging field from past few days. The tumor detection is often a preliminary phase. This paper describes the detection of the brain tumor by thresholding method. The proposed technique can be efficiently applied to detect and extract the brain tumor from MRI images obtained from patient data base. It proves to be handy tool for the physicians working in this field.


title: JetScope: Reliable and Interactive Analytics at Cloud Scale

abstract: Interactive, reliable, and rich data analytics at cloud scale is a key capability to support low latency data exploration and experimentation over terabytes of data for a wide range of business scenarios. Besides the challenges in massive scalability and low latency distributed query processing, it is imperative to achieve all these requirements with effective fault tolerance and efficient recovery, as failures and fluctuations are the norm in such a distributed environment. ::: ::: We present a cloud scale interactive query processing system, called JetScope, developed at Microsoft. The system has a SQL-like declarative scripting language and delivers massive scalability and high performance through advanced optimizations. In order to achieve low latency, the system leverages various access methods, optimizes delivering first rows, and maximizes network and scheduling efficiency. The system also provides a fine-grained fault tolerance mechanism which is able to efficiently detect and mitigate failures without significantly impacting the query latency and user experience. JetScope has been deployed to hundreds of servers in production at Microsoft, serving a few million queries every day.


title: Multi-view unsupervised feature selection by cross-diffused matrix alignment

abstract: Multi-view high-dimensional data become increasingly popular in the big data era. Feature selection is a useful technique for alleviating the curse of dimensionality in multi-view learning. In this paper, we study unsupervised feature selection for multi-view data, as class labels are usually expensive to obtain. Traditional feature selection methods are mostly designed for single-view data and cannot fully exploit the rich information from multi-view data. Existing multi-view feature selection methods are usually based on noisy cluster labels which might not preserve sufficient information from multi-view data. To better utilize multi-view information, we propose a method, CDMA-FS, to select features for each view by performing alignment on a cross diffused matrix. We formulate it as a constrained optimization problem and solve it using Quasi-Newton based method. Experiments results on four real-world datasets show that the proposed method is more effective than the state-of-the-art methods in multi-view setting.


title: State of charge (SoC) estimation of LiFePO4 battery module using support vector regression

abstract: In this research work, we demonstrate state-of-charge (SoC) estimation using support vector regression (SVR) approach for a high capacity Lithium Ferro Phosphate (LiFePO4) battery module. The proposed SoC estimator in this work is extracted from open circuit voltage (OCV)-SoC lookup table which is obtained from the battery module discharging and charging testing cycles, using voltage and current as independent variables. The SoC estimation based on SVR gives a perfectly linear curve fitting with its reference within the range of 37.5% to 90% while the rest hysteresis due to the discharging and charging process is compensated using OCV-SoC curve as the training data set. The SVR estimates the battery module SoC with RMSE of 2.3% over the whole test and the maximum positive and negative error is 4%, which means that it shows good accuracy.


title: Next-generation of virtual personal assistants (Microsoft Cortana, Apple Siri, Amazon Alexa and Google Home)

abstract: One of the goals of Artificial intelligence (AI) is the realization of natural dialogue between humans and machines. in recent years, the dialogue systems, also known as interactive conversational systems are the fastest growing area in AI. Many companies have used the dialogue systems technology to establish various kinds of Virtual Personal Assistants(VPAs) based on their applications and areas, such as Microsoft's Cortana, Apple's Siri, Amazon Alexa, Google Assistant, and Facebook's M. However, in this proposal, we have used the multi-modal dialogue systems which process two or more combined user input modes, such as speech, image, video, touch, manual gestures, gaze, and head and body movement in order to design the Next-Generation of VPAs model. The new model of VPAs will be used to increase the interaction between humans and the machines by using different technologies, such as gesture recognition, image/video recognition, speech recognition, the vast dialogue and conversational knowledge base, and the general knowledge base. Moreover, the new VPAs system can be used in other different areas of applications, including education assistance, medical assistance, robotics and vehicles, disabilities systems, home automation, and security access control.


title: DroidDolphin: a dynamic Android malware detection framework using big data and machine learning

abstract: Smartphones are getting more and more popular nowadays with various kinds of applications to make our lives more convenient. Unfortunately, malicious applications, also known as malware, arises as well. A user is often tempted into install a malware without any awareness, and the malware steals the users' personal information. Some malware would send SMS or make phone calls, which result in additional charges. Thus, detection of malware is critical to protect smartphone users. In this paper, we proposed DroidDolphin, a dynamic malware analysis framework which leverages the technologies of GUI-based testing, big data analysis, and machine learning to detect malicious Android applications. Based on our automatic testing tools, we were able to extract useful static and dynamic features from a training dataset composed with 32,000 benign and 32,000 malicious applications. Our preliminary results showed that the prediction accuracy reaches 86.1% and F-score reaches 0.857. As the dataset increases, the accuracy of detection increases significantly, which makes this methodology promising.


title: Convolutional Attention Networks for Scene Text Recognition

abstract: In this article, we present Convoluitional Attention Networks (CAN) for unconstrained scene text recognition. Recent dominant approaches for scene text recognition are mainly based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), where the CNN encodes images and the RNN generates character sequences. Our CAN is different from these methods; our CAN is completely built on CNN and includes an attention mechanism. The distinctive characteristics of our method include (i) CAN follows encoder-decoder architecture, in which the encoder is a deep two-dimensional CNN and the decoder is a one-dimensional CNN; (ii) the attention mechanism is applied in every convolutional layer of the decoder, and we propose a novel spatial attention method using average pooling; and (iii) position embeddings are equipped in both a spatial encoder and a sequence decoder to give our networks a sense of location. We conduct experiments on standard datasets for scene text recognition, including Street View Text, IIIT5K, and ICDAR datasets. The experimental results validate the effectiveness of different components and show that our convolutional-based method achieves state-of-the-art or competitive performance over prior works, even without the use of RNN.


title: UIT-ANPR: toward an open framework for automatic number plate recognition on smartphones

abstract: The automatic number-plate recognition (ANPR) problem is attracting attention of many research scientists. However, there are not many researches focusing on building an ANPR system for smart-phones to recognize the motorcycles number plate dynamically on the street. In addition, the mobility and sensor rich of mobile device has not been much exploited. This paper introduces an innovative approach for building an ANPR software on Android smart-phones using neural network which can be applied to the mobile sensing to support intelligent traffic monitoring. Our approach is divided into two main parts: i) building an ANPR software on mobile phone using OpenCV and neural network; ii) evaluating the software on real traffic. The ANPR development includes three main steps: using AdaBoots classification method to locate the area which contains the number plate, applying morphology method to separate characters within the number plate, and using neural network for characters recognition. For evaluation the software, we take the location and speed of recognized number plate into account. This evaluation raises some research challenges in the area of building mobile ANPR software.


title: A General Framework for Describing Creative Agents

abstract: Computational creativity is a subfield of AI focused on developing and studying creative systems. Few academic studies analysing the behaviour of creative agents from a theoretical viewpoint have been proposed. The proposed frameworks are vague and hard to exploit; moreover, such works are focused on a notion of creativity tailored for humans. ::: In this paper we introduce General Creativity, which extends that traditional notion. General Creativity provides the basis for a formalised theoretical framework, that allows one to univocally describe any creative agent, and their behaviour within societies of creative systems. Given the growing number of AI creative systems developed over recent years, it is of fundamental importance to understand how they could influence each other as well as how to gauge their impact on human society. In particular, in this paper we exploit the proposed framework for (i) identifying different forms of creativity; (ii) describing some typical creative agents behaviour, and (iii) analysing the dynamics of societies in which both human and non-human creative systems coexist.


GraphNet : Recommendation system based on language and network structure


title: Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog

abstract: Goal-oriented dialog has been given attention due to its numerous applications in artificial intelligence. Goal-oriented dialogue tasks occur when a questioner asks an action-oriented question and an answerer responds with the intent of letting the questioner know a correct action to take. To ask the adequate question, deep learning and reinforcement learning have been recently applied. However, these approaches struggle to find a competent recurrent neural questioner, owing to the complexity of learning a series of sentences. Motivated by theory of mind, we propose "Answerer in Questioner's Mind" (AQM), a novel information theoretic algorithm for goal-oriented dialog. With AQM, a questioner asks and infers based on an approximated probabilistic model of the answerer. The questioner figures out the answerer's intention via selecting a plausible question by explicitly calculating the information gain of the candidate intentions and possible answers to each question. We test our framework on two goal-oriented visual dialog tasks: "MNIST Counting Dialog" and "GuessWhat?!". In our experiments, AQM outperforms comparative algorithms by a large margin.


title: Beyond prediction: Using big data for policy problems

abstract: Machine-learning prediction methods have been extremely productive in applications ranging from medicine to allocating fire and health inspectors in cities. However, there are a number of gaps between making a prediction and making a decision, and underlying assumptions need to be understood in order to optimize data-driven decision-making.


title: Differences in emoji sentiment perception between readers and writers

abstract: Previous research has traditionally analyzed emoji sentiment from the point of view of the reader of the content not the author. Here, we analyze emoji sentiment from the point of view of the author and present an emoji sentiment benchmark that was built from an employee happiness dataset where emoji happen to be annotated with daily happiness of the author of the comment. The data spans over 3 years, and 4k employees of 56 companies based in Barcelona. We compare sentiment of writers to readers. Results indicate that, there is an 82% agreement in how emoji sentiment is perceived by readers and writers. The disagreement concentrates in negative emoji, where the authors report to feel 26% worse than perceived by readers. Emoji use was not found to be correlated with author moodiness. Authors that use emoji are happier than authors that never use emoji.


title: The association between pelvic girdle pain and sick leave during pregnancy; a retrospective study of a Norwegian population

abstract: The incidence of pelvic girdle pain (PGP) in pregnancy is wide ranged depending on definition, the utilised diagnostic means, and the design of the studies. PGP during pregnancy has negative effects on activities of daily living and causes long sick leave, which makes it a major public health issue. Our objectives were to explore the frequency of sick leave in pregnancy due to PGP, assess the relationship between different types of pain-related activities of daily living, examine physical workload, type of work in relation to sick leave, and to explore factors that make women less likely to take sick leave for PGP. All women giving birth at the maternity ward of Stavanger University Hospital, Norway, were asked to participate and complete a questionnaire on demographic features, PGP, pain-related activities of daily living, sick leave in general and for PGP, frequency of exercising before and during pregnancy. Drawings of pelvic girdle and low back area were used for the localization of pain. PGP intensity was then rated retrospectively on a numerical rating scale. Non-parametric tests, multinomial logistic regression and sequential linear regression analysis were used in the statistical analysis. PGP is a frequent and major cause of sick leave during pregnancy among Norwegian women, which is also reflected in activities of daily living as measured with scores on all Oswestry disability index items. In the multivariate analysis of factors related to sick leave and PGP we found that work satisfaction, problems with lifting and sleeping, and pain intensity were risk factors for sick leave. In addition, women with longer education, higher work satisfaction and fewer problems with sitting, walking and standing, were less likely to take sick leave in pregnancy, despite the same pain intensity as women being on sick leave. A coping factor in pregnant women with PGP was discovered, most likely dependant on education, associated with work situation and/or work posture, which decreases sick leave. We recommend these issues to be further examined in a prospective longitudinal study since it may have important implications for sick leave frequency during pregnancy.


title: Credit risk assessment with a multistage neural network ensemble learning approach

abstract: In this study, a multistage neural network ensemble learning model is proposed to evaluate credit risk at the measurement level. The proposed model consists of six stages. In the first stage, a bagging sampling approach is used to generate different training data subsets especially for data shortage. In the second stage, the different neural network models are created with different training subsets obtained from the previous stage. In the third stage, the generated neural network models are trained with different training datasets and accordingly the classification score and reliability value of neural classifier can be obtained. In the fourth stage, a decorrelation maximization algorithm is used to select the appropriate ensemble members. In the fifth stage, the reliability values of the selected neural network models (i.e., ensemble members) are scaled into a unit interval by logistic transformation. In the final stage, the selected neural network ensemble members are fused to obtain final classification result by means of reliability measurement. For illustration, two publicly available credit datasets are used to verify the effectiveness of the proposed multistage neural network ensemble model.


title: Robust Model Predictive Control of DC-DC Floating Interleaved Boost Converter under Uncertainty

abstract: DC-DC Floating Interleaved Boost Converter (FIBC) is a new converter commonly used for converting the low level voltage generated by a renewable energy source to a high-level voltage required for AC inverters. Although a desired voltage is expected at the output, designing a proper voltage gain for FIBC is challenging due to different types of uncertainties. For instance, the voltage generated by the renewable energy source may be affected by a variety of parameters including external load. The input voltage of the FIBC is, therefore, uncertain. In addition, the physical elements of a boost converter are also uncertain. Their value may vary from one product to another within a given range. This results in an uncertain voltage gain for a fixed switching duty cycle. In this work, the robust control of this boost converter under uncertain conditions is studied. Specifically, a Robust Model Predictive Control (RMPC) is employed for this purpose. The designed controller is able to keep the output voltage at the desired level despite the uncertainties.


title: Suprapedicular Foraminal Endoscopic Approach to Lumbar Lateral Recess Decompression Surgery to Treat Degenerative Lumbar Spinal Stenosis

abstract: Background ::: To discuss the strategy of suprapedicular foraminal endoscopic approach to lumbar lateral recess decompression and evaluate the safety and effectiveness of this strategy.


title: Survey study on the methods of bird vocalization classification

abstract: The technologies holds the ability to change the world. Current digital era is a product of the evolutionary technologies. It created the necessity to increase the Human Computer Interaction (HCI) and it became one of the most emerging research areas of the decade. HCI is an interface between the users and the system to improve the interaction. HCI concept came into existence in early 1980's. One of the emerging new research area in HCI is Context Aware System (CAS). The technological advancements in HCI created a new outlook in the research of CAS. CAS is a system which understand the user, their surroundings, and location. CAS make this possible by processing the environmental and bio-acoustic. Sound is one of the important media for both humans and animals to communicate and understand information. Bird sound, vehicle sound, wind sound etc. are some of the environmental and bio acoustics. Processing these sounds or signals will help us to create a better performing CAS. This paper profiles a survey study on bird sound classification and identification. Automatic identification of bird sound is one among the difficult task in signal processing. Also, the paper will profile the previous research works on various phases in bird vocalization processing; such as preprocessing, feature selection and classification.


title: A PUF-based paradigm for IoT security

abstract: Physically Unclonable Functions or PUFs have emerged as a promising lightweight authentication alternative to traditional cryptography. This made them a promising candidate for constrained IoT devices. These PUFs however have been found to be vulnerable to modeling attacks to varying degrees. The security of different PUFs designs against these modeling attacks have been evaluated in multiple research efforts. However, a more accurate evaluation that incorporates the area footprints of the different PUFs is missing from the literature. Such evaluation is rather essential in order to determine the most suitable PUF design for the different constrained IoT devices. To perform this evaluation, we implemented several strong delay-based PUFs. The results showed that when factoring in the PUFs implementation area costs, these PUFs compare differently as some enhanced PUF designs turned out to have inferior efficiency compared to their simpler counterpart. We recognized the most effective design elements in the implemented PUFs. Based on the efficiency figures obtained, we recommended the optimal PUF design for the different security schemes used in constrained IoT devices.


title: DNA barcoding of an invasive mammal species , the small Indian mongoose ( Herpestes javanicus ; E . Geoffroy Saint-Hillaire 1818 ) in the Caribbean and Hawaiian Islands

abstract: Background and aim: The use of DNA barcodes has been proposed as a promising tool for identifying species. The efficacy of this tool for invasive species requires further exploration. The species status of the small Indian mongoose, an exotic invasive in several parts of the world, has been contentious due to morphological similarity with its congeners in its natural habitat. Although the small Indian mongoose is recognized as Herpestes javanicus, this nomenclature has been used interchangeably with Herpestes auropunctatus.Materials and methods: Here, we demonstrate the utility of using DNA barcoding approaches with mtDNA cytochrome b to discriminate between the two species and other sympatric members of the genus Herpestes (Herpestes naso, Herpestes urva, and Herpestes edwardsii). Using the diagnostic DNA positions we obtain, we can identity specimens of nonnative populations of the small Indian mongoose from the Caribbean and Hawaiian Islands to their species of origin.Results: A singe diagnostic site a...


title: BALANCING MULTIPLE ROLES AMONG A GROUP OF URBAN MIDLIFE AMERICAN INDIAN WORKING WOMEN

abstract: Presented are the results of a secondary analysis of group data from a study of a six-week role conflict reduction intervention among a group of urban American Indian women (n =8). The specific aim of this researcher was to understand the process of balancing multiple roles as expressed in the participants' daily lived experiences as mothers, wives, and workers. A construction of the process of balancing multiple roles was accomplished through the use of narratives. Balancing multiple roles represented a major current attempt on the part of the participants to integrate and balance traditional and contemporary feminine strengths in a positive, culturally consistent manner. The study themes included: traditional sex role expectation conflicts, family guilt, guilt management, transitioning inner conflict and stress, breaking the silence?learning to say no, and healing the spirit to reclaim the self. Further support for retraditionalization of roles for this group of Indian women was maintained as an effecti...


title: A Compact Circularly Polarized Slotted Patch Antenna for GNSS Applications

abstract: A compact circularly polarized (CP) square-ring slotted patch antenna with vias is proposed for global navigation satellite systems (GNSS). Four square-ring-shaped slots are cut symmetrically onto a square patch radiator along its diagonals for wide-angle CP radiation and miniaturization. The antenna size is further reduced by grounding the central patches surrounded by the square-ring-shaped slots. An antenna prototype with an overall size of 60 mm 60 mm 5m m shows a measured 10-dB return loss bandwidth of 90.0 MHz (1.565-1.655 GHz) with a maximum gain of 4.65 dBic. The measured 3-dB axial ratio (AR) bandwidth is 35.0 MHz (1.57-1.605 GHz) with a 3-dB AR beamwidth of more than 140 across the bandwidth. Index Terms—Circularly polarized antenna, global navigation satellite systems (GNSS), miniaturization, patch antenna, slotted patch radiator, via.


title: Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation

abstract: We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on a resource that links two types of sense-aware lexical networks: one is induced from a corpus using distributional semantics, the other is manually constructed. The combination of two networks reduces the sparsity of sense representations used for WSD. We evaluate these enriched representations within two lexical sample sense disambiguation benchmarks. Our results indicate that (1) features extracted from the corpus-based resource help to significantly outperform a model based solely on the lexical resource; (2) our method achieves results comparable or better to four state-of-the-art unsupervised knowledge-based WSD systems including three hybrid systems that also rely on text corpora. In contrast to these hybrid methods, our approach does not require access to web search engines, texts mapped to a sense inventory, or machine translation systems.


title: AN ONTOLOGY-BASED TOURISM RECOMMENDER SYSTEM BASED ON SPREADING ACTIVATION MODEL

abstract: A tourist has time and budget limitations; hence, he needs to select points of interest (POIs) optimally. Since the available information about POIs is overloading, it is difficult for a tourist to select the most appreciate ones considering preferences. In this paper, a new travel recommender system is proposed to overcome information overload problem. A recommender system (RS) evaluates the overwhelming number of POIs and provides personalized recommendations to users based on their preferences. A content-based recommendation system is proposed, which uses the information about the user’s preferences and POIs and calculates a degree of similarity between them. It selects POIs, which have highest similarity with the user’s preferences. The proposed content-based recommender system is enhanced using the ontological information about tourism domain to represent both the user profile and the recommendable POIs. The proposed ontology-based recommendation process is performed in three steps including: ontology-based content analyzer, ontology-based profile learner, and ontology-based filtering component. User’s feedback adapts the user’s preferences using Spreading Activation (SA) strategy. It shows the proposed recommender system is effective and improves the overall performance of the traditional content-based recommender systems.


title: Hardware Architecture for HOG Feature Extraction

abstract: Pedestrian recognition on embedded systems is a challenging problem since accurate recognition requires extensive computation. To achieve real-time pedestrian recognition on embedded systems, we propose hardware architecture suitable for HOG feature extraction, which is a popular method for high-accuracy pedestrian recognition. To reduce computational complexity toward efficient hardware architecture, this paper proposes several methods to simplify the computation of HOG feature extraction, such as conversion of the division, square root, arctangent to more simple operations. To show that such simplifications do not spoil the recognition accuracy, the detection performance is also evaluated using a support vector machine. Moreover, we implement the proposed architecture on an ALTERA Stratix II FPGA using Verilog HDL to evaluate the circuit size and the processing performance of the proposed architecture. Implementation results show that real-time processing for 30 fps VGA video can be achieved if 10 instances of the proposed hardware are used in parallel.


title: Applicability assessment of Semantic Web technologies

abstract: The Semantic Web is one of the fastest developing fields within the Information and Communication Technology sector and, as such, under constant examination by scientists and IT professionals. This article aims to provide a better understanding of the applicability of Semantic Web tools and technologies in practice. This aim will be achieved by surveying the recommended and emerging W3C standards, presenting an overview of the state-of-the-art in the Semantic Web research in the European Union, analysing the W3C collection of Case studies and Use Cases, and discussing the extent of adoption of Semantic Web technologies. The overall technology maturity level assessment has shown that Semantic Web technologies are finding their ways into real-world applications, and that, rather than being merely a fashionable research issue, the Semantic Web, slowly but surely, becomes our reality.


title: Exposure to appetitive food stimuli markedly activates the human brain

abstract: Abstract Objective: The increased incidence of obesity most likely reflects changes in the environment that had made food more available and palatable. Here we assess the response of the human brain to the presentation of appetitive food stimuli during food presentation using PET and FDG. Method: Metabolic changes in response to food presentation were done in 12 healthy normal body weight subjects who were food deprived before the study. Results: Food presentation significantly increased metabolism in the whole brain (24%, P


Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator


When sales and marketing align: impact on performance


title: Application Architecture for the Internet of Cities: Blueprints for Future Smart City Applications

abstract: As smart cities evolve toward the Internet of Cities, it's essential to consider common problems and how to address them. Three representative types of smart city applications are outlined here, identifying key requirements and architectural guidelines for implementation.


title: Representing General Relational Knowledge in ConceptNet 5

abstract: ConceptNet is a knowledge representation project, providing a large semantic graph that describes general human knowledge and how it is expressed in natural language. This paper presents the latest iteration, ConceptNet 5, including its fundamental design decisions, ways to use it, and evaluations of its coverage and accuracy.


title: Implementation and Comparison of M2M Protocols for Internet of Things

abstract: The backbone of Internet of Things (IoT) is the communication protocols which seamlessly integrate thousands of nodes and enable a light weight data transfer process. This research is to analyze the efficiency and applicability of different M2M protocols that are available for IoT communication. For the purpose of evaluating the protocols a Raspberry-Pi with Raspbian OS and a temperature sensor are used. Every environment of IoT need a protocol with different capabilities, which makes it really specific of what protocol has to be used for a given number of nodes, communication range, power requirements and reliability. Upon research on today's IoT development, the major protocols that fit every IoT environment are MQTT and CoAP. These protocols are light weight in terms of operation and data transfer hence make into the markets of IoT. This research will highlight the outcomes, anomalies and benefits of each of these protocols. Previously the existence of advanced protocols did not exist and that made huge IoT networks very difficult to maintain as the communication costs were very high. This analysis will help it make better understand the need of a lightweight protocol. There are many protocols that can be used in IoT such as XMPP, DDS, AMQP, HTTP, REST etc. The deeper understanding of these protocols and application requirements is necessary to properly select which protocol is most suitable for the application at hand. This research paper is to discuss and analyze the efficiency, usage and requirements of the two major protocols namely MQTT, CoAP.


title: A cognitive model of drug urges and drug-use behavior: role of automatic and nonautomatic processes.

abstract: Contemporary urge models assume that urges are necessary but not sufficient for the production of drug use in ongoing addicts, are responsible for the initiation of relapse in abstinent addicts, and can be indexed across 3 classes of behavior: verbal report, overt behavior, and somatovisceral response. A review of available data does not provide strong support for these assumptions. An alternative cognitive model of drug use and drug urges is proposed that hypothesizes that drug use in the addict is controlled by automatized action schemata. Urges are conceptualized as responses supported by nonautomatic cognitive processes activated in parallel with drug-use action schemata either in support of the schema or in support of attempts to block the execution of the schema. The implications of this model for the assessment of urge responding and drug-use behavior are presented. Language: en


title: Multi-factor EEG-based user authentication

abstract: Electroencephalography (EEG) signal has been used widely in health and medical fields. It is also used in brain-computer interface (BCI) systems for humans to continuously control mobile robots and wheelchairs. Recently, the research communities successfully explore the potential of using EEG as a new type of biometrics in user authentication. EEG-based user authentication systems have the combined advantages of both password-based and biometric-based authentication systems, yet without their drawbacks. In this paper, we propose to take the advantage of rich information, such as age and gender, carried by EEG signals for user authentication in multi-level security systems. Our experiments showed very promising results for the proposed multi-factor EEG-based authentication method.


title: Characterization of In-Body Radio Channels for Wireless Implants

abstract: Body-centric wireless communication, which is accepted as an important part of the fourth generation mobile communications systems, provides a prospective communication solution for implantable devices in personal health care. In this paper, an inhomogeneous human body model was proposed to study the in-body radio channels for wireless implants, and numerical simulations were carried out with the finite-difference time-domain method. Specifically, parameters, such as channel gain and group delay, were studied for different links according to implant locations, namely, from intra-brain to the surface of chest, abdomen, upper arm, and thigh, respectively, over different frequencies (from 5 to 100 MHz). The influences of electrode size and type were also studied at 45 MHz. The results showed that all channels had the maximum gain of more than −12 dB at 5 MHz. Group delay was almost independent of channels. The model was verified by experimental measurements. The results showed that the simulations were in good agreement with the measurements.


title: Toward a framework for detecting privacy policy violations in android application code

abstract: Mobile applications frequently access sensitive personal informa- tion to meet user or business requirements. Because such informa- tion is sensitive in general, regulators increasingly require mobile- app developers to publish privacy policies that describe what infor- mation is collected. Furthermore, regulators have fined companies when these policies are inconsistent with the actual data practices of mobile apps. To help mobile-app developers check their pri- vacy policies against their apps’ code for consistency, we propose a semi-automated framework that consists of a policy terminology- API method map that links policy phrases to API methods that pro- duce sensitive information, and information flow analysis to detect misalignments. We present an implementation of our framework based on a privacy-policy-phrase ontology and a collection of map- pings from API methods to policy phrases. Our empirical eval- uation on 477 top Android apps discovered 341 potential privacy policy violations.


title: Bayesian bot detection based on DNS traffic similarity

abstract: Bots often are detected by their communication with a command and control (C&C) infrastructure. To evade detection, botmasters are increasingly obfuscating C&C communications, e.g., by using fastflux or peer-to-peer protocols. However, commands tend to elicit similar actions in bots of a same botnet. We propose and evaluate a Bayesian approach for detecting bots based on the similarity of their DNS traffic to that of known bots. Experimental results and sensitivity analysis suggest that the proposed method is effective and robust.


title: Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network

abstract: Estimation of human emotions from Electroencephalogram (EEG) signals plays a vital role in developing robust Brain-Computer Interface (BCI) systems. In our research, we used Deep Neural Network (DNN) to address EEG-based emotion recognition. This was motivated by the recent advances in accuracy and efficiency from applying deep learning techniques in pattern recognition and classification applications. We adapted DNN to identify human emotions of a given EEG signal (DEAP dataset) from power spectral density (PSD) and frontal asymmetry features. The proposed approach is compared to state-of-the-art emotion detection systems on the same dataset. Results show how EEG based emotion recognition can greatly benefit from using DNNs, especially when a large amount of training data is available.


title: Context-Awareness for Mobile Sensing: A Survey and Future Directions

abstract: The evolution of smartphones together with increasing computational power has empowered developers to create innovative context-aware applications for recognizing user-related social and cognitive activities in any situation and at any location. The existence and awareness of the context provide the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze, and share local sensory knowledge in the purpose for a large-scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects and also assist individuals. However, many open challenges remain, which are mostly arisen because the middleware services provided in mobile devices have limited resources in terms of power, memory, and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved and, at the same time, better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991–2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlightens them by proposing possible solutions.


title: A Predetermined LMS Digital Background Calibration Technique for Pipelined ADCs

abstract: A digital background calibration technique for pipelined analog-to-digital converters (ADCs) is proposed to correct the capacitor mismatch, finite dc gain, and nonlinearity of residue amplifiers. It divides the pipelined ADC into two equal channels and changes the decision points of sub-ADCs with a pseudorandom sequence to perform the digital background calibration. The difference between the digital outputs of the channels is used to drive the least mean square (LMS) machine to correct the mentioned errors and also the mismatch between the channels. In order to speed up the error correction, an accurate estimation for the errors is identified. The estimation is done by utilizing a piecewise linear model and a slope mismatch measurement technique in the digital domain. Behavioral simulations of a 12-bit 100-MS/s split pipelined ADC show that the convergence time of the proposed LMS calibration technique is significantly reduced in comparison with the conventional LMS algorithm for the same signal-to-noise-and-distortion ratio.


title: License plate localization using a Naïve Bayes classifier

abstract: This paper presents a probabilistic technique to localize license plates regions for cars adhering to the standard set by the Malaysian Road Transport Department. Images of the front/rear-view of cars displaying their license plates are firstly preprocessed, followed by features extraction generated from connected components analysis. These features are then used to train a Naive Bayes classifier for the final task of license plates localization. Experimental results conducted on 144 images have shown that considering two candidates with the highest posterior probabilities better guarantees license plates regions are properly localized, with a recall of 0.98.


title: Stochastic Gradient Descent Tricks

abstract: Chapter 1 strongly advocates the stochastic back-propagation method to train neural networks. This is in fact an instance of a more general technique called stochastic gradient descent (SGD). This chapter provides background material, explains why SGD is a good learning algorithm when the training set is large, and provides useful recommendations.


title: RDE: Replay DEbugging for Diagnosing Production Site Failures

abstract: Online service failures in production computing environments are notoriously difficult to debug. One of the key challenges is to allow the developer to replay the failure execution within an interactive debugging tool such as GDB. Previous work has proposed in-situ approaches to inferring the production-run failure path within the production environment. However, those tools may sometimes suggest failure execution paths that are infeasible to reach by any program inputs. Moreover, production site often does not record or provide failure-triggering inputs due to the user privacy concern. In this paper, we present RDE, a Replay DEbug system that can replay a production-site failure at the development site within an interactive debugging environment without requiring user inputs. RDE takes an inferred production failure path as input and performs execution synthesis using a new guided symbolic execution technique. RDE can tolerate imprecise or inaccurate failure path information by navigating the symbolic execution along a set of selected paths. RDE synthesizes an input from the selected symbolic execution path which can be fed to a debugging tool to replay the failure. We have implemented an initial prototype of RDE and tested it with a set of coreutils bugs. The results show that RDE can successfully replay all the tested bugs within GDB.


title: Automatic Room Detection and Room Labeling from Architectural Floor Plans

abstract: This paper presents an automatic system for analyzing and labeling architectural floor plans. In order to detect the locations of the rooms, the proposed systems extracts both, structural and semantic information from given floor plans. Furthermore, OCR is applied on the text layer to retrieve the meaningful room labeling. Finally, a novel post-processing is proposed to split rooms into several sub-regions if several semantic rooms share the same physical room. Our fully automatic system is evaluated on a publicly available dataset of architectural floor plans. In our experiments, we could clearly outperform other state-of-the-art approaches for room detection.


title: Concept Drift Visualization

abstract: Mining data stream are facing many challenges now, one of them is concept drift problem. In many practical applications, concept drift usually affects the classification performance for data stream, or even make the classifier failed. However, most of the proposed methods are mainly focusing on solving concept drift from the data value point of view, and very little attention has been focused on mining the knowledge in the data concept level. Motivated by this, in this paper, we use Kullback-Leibler divergence (KL-divergence) algorithm to detect concept drift dynamically. Meanwhile, we also construct a concept pool to reserve distinct concepts in data stream and analyze the concept transformation information. Experimental studies on two real-world data sets demonstrate that the proposed concept visualization method and concept transformation map could effectively and efficiently mine concept drifts relationship from the noisy streaming data.


title: Y-Shape Dielectric Dual-Mode Resonator

abstract: In this paper, a new concept for a dual-mode dielectric resonator is presented, which is based on TM-mode operation in dielectric rods. By arranging the dielectric rods in Y-shape configuration, new fundamental eigenmode resonances are found to be present. The eigenmodes are coupled parallel to the input and output of the filter to result in a doublet coupling scheme. The filter was laid out by the global eigenmode approach. For filter configurations showing E-plane/H-plane mirror symmetry, the required parameters are determined and optimized by an eigenmode solver. A nonplanar Y-shape monoblock ceramic was constructed to achieve bandpass operation at 2 GHz. Measurement results of the constructed dual-mode filters are presented for the first time. The setup is suitable to demonstrate the zero-shifting property of the doublet by slightly adjusting the position of the ceramic inside the cavity. An unloaded quality factor of 4000 was extracted from the measurements. Good agreement between measured and computed results is obtained.


title: Segmentation and Clustering of Car-Following Behavior: Recognition of Driving Patterns

abstract: Driving behavior can be influenced by many factors that are not feasible to collect in driving behavior studies. The research presented in this paper investigates the characteristics of a wide range of driving behaviors linking driving states to the drivers' actions. The proposed methodology is structured such that a known state can be linked to multiple actions, thus accounting for the effects of unknown driving state factors. A two-step algorithm was developed and used for the segmentation and clustering of driving behaviors. The algorithm segments and clusters car-following behaviors based on eight state–action variables: the longitudinal acceleration, the lateral acceleration, the yaw rate, the vehicle speed, the lane offset, the yaw angle, the range, and the range rate. The results of this methodology are state–action clusters that define the driving pattern of drivers. The sample used in this paper included 20 different drivers, i.e., ten car and ten truck drivers. The results revealed that behavior patterns were different between car drivers but not between truck drivers. The results also show that car drivers exhibit behaviors that are unique to each driver, whereas truck drivers show a common driving pattern. The characteristics and frequency of recognized driving patterns are provided in this paper, along with the corresponding modeling parameters of each pattern.


title: Simulation-Based Algorithms for Markov Decision Processes

abstract: Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: innovative material on MDPs, both in constrained settings and with uncertain transition properties; game-theoretic method for solving MDPs; theories for developing roll-out based algorithms; and details of approximation stochastic annealing, a population-based on-line simulation-based algorithm. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research.


Parallel Corpora , Terminology Extraction and Machine Translation


title: A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation

abstract: This study proposes a novel method for multichannel image gray level co-occurrence matrix (GLCM) texture representation. It is well known that the standard procedure for the automatic extraction of GLCM textures is based on a mono-spectral image. In real applications, however, the GLCM texture feature extraction always refers to multi/hyperspectral images. The widely used strategy to deal with this issue is to calculate the GLCM from the first principal component or the panchromatic band, which do not include all the useful information. Accordingly, in this study, we propose to represent the multichannel textures for multi/hyperspectral imagery by the use of: (1) clustering algorithms; and (2) sparse representation, respectively. In this way, the multi/hyperspectral images can be described using a series of quantized codes or dictionaries, which are more suitable for multichannel texture representation than the traditional methods. Specifically, K-means and fuzzy c-means methods are adopted to generate the codes of an image from the clustering point of view, while a sparse dictionary learning method based on two coding rules is proposed to produce the texture primitives. The proposed multichannel GLCM textural extraction methods were evaluated with four multi/hyperspectral datasets: GeoEye-1 and QuickBird multispectral images of the city of Wuhan, the well-known AVIRIS hyperspectral dataset from the Indian Pines test site, and the HYDICE airborne hyperspectral dataset from the Washington DC Mall. The results show that both the clustering-based and sparsity-based GLCM textures outperform the traditional method (extraction based on the first principal component) in terms of classification accuracies in all the experiments.


title: Operations research in global health: a scoping review with a focus on the themes of health equity and impact

abstract: BACKGROUND ::: Operations research (OR) is a discipline that uses advanced analytical methods (e.g. simulation, optimisation, decision analysis) to better understand complex systems and aid in decision-making. Herein, we present a scoping review of the use of OR to analyse issues in global health, with an emphasis on health equity and research impact. A systematic search of five databases was designed to identify relevant published literature. A global overview of 1099 studies highlights the geographic distribution of OR and common OR methods used. From this collection of literature, a narrative description of the use of OR across four main application areas of global health - health systems and operations, clinical medicine, public health and health innovation - is also presented. The theme of health equity is then explored in detail through a subset of 44 studies. Health equity is a critical element of global health that cuts across all four application areas, and is an issue particularly amenable to analysis through OR. Finally, we present seven select cases of OR analyses that have been implemented or have influenced decision-making in global health policy or practice. Based on these cases, we identify three key drivers for success in bridging the gap between OR and global health policy, namely international collaboration with stakeholders, use of contextually appropriate data, and varied communication outlets for research findings. Such cases, however, represent a very small proportion of the literature found. ::: ::: ::: CONCLUSION ::: Poor availability of representative and quality data, and a lack of collaboration between those who develop OR models and stakeholders in the contexts where OR analyses are intended to serve, were found to be common challenges for effective OR modelling in global health.


title: Deep Adversarial Metric Learning

abstract: Learning an effective distance metric between image pairs plays an important role in visual analysis, where the training procedure largely relies on hard negative samples. However, hard negatives in the training set usually account for the tiny minority, which may fail to fully describe the distribution of negative samples close to the margin. In this paper, we propose a deep adversarial metric learning (DAML) framework to generate synthetic hard negatives from the observed negative samples, which is widely applicable to supervised deep metric learning methods. Different from existing metric learning approaches which simply ignore numerous easy negatives, the proposed DAML exploits them to generate potential hard negatives adversarial to the learned metric as complements. We simultaneously train the hard negative generator and feature embedding in an adversarial manner, so that more precise distance metrics can be learned with adequate and targeted synthetic hard negatives. Extensive experimental results on three benchmark datasets including CUB-200-2011, Cars196 and Stanford Online Products show that DAML effectively boosts the performance of existing deep metric learning approaches through adversarial learning.


title: The role of calcitonin gene–related peptide in peripheral and central pain mechanisms including migraine

abstract: Calcitonin gene-related peptide (CGRP) is a 37-amino acid peptide found primarily in the C and Aδ sensory fibers arising from the dorsal root and trigeminal ganglia, as well as the central nervous system. Calcitonin gene-related peptide was found to play important roles in cardiovascular, digestive, and sensory functions. Although the vasodilatory properties of CGRP are well documented, its somatosensory function regarding modulation of neuronal sensitization and of enhanced pain has received considerable attention recently. Growing evidence indicates that CGRP plays a key role in the development of peripheral sensitization and the associated enhanced pain. Calcitonin gene-related peptide is implicated in the development of neurogenic inflammation and it is upregulated in conditions of inflammatory and neuropathic pain. It is most likely that CGRP facilitates nociceptive transmission and contributes to the development and maintenance of a sensitized, hyperresponsive state not only of the primary afferent sensory neurons but also of the second-order pain transmission neurons within the central nervous system, thus contributing to central sensitization as well. The maintenance of a sensitized neuronal condition is believed to be an important factor underlying migraine. Recent successful clinical studies have shown that blocking the function of CGRP can alleviate migraine. However, the mechanisms through which CGRP may contribute to migraine are still not fully understood. We reviewed the role of CGRP in primary afferents, the dorsal root ganglion, and in the trigeminal system as well as its role in peripheral and central sensitization and its potential contribution to pain processing and to migraine.


title: Supervised learning and evaluation of KITTI's cars detector with DPM

abstract: This paper carries out a discussion on the supervised learning of a car detector built as a Discriminative Part-based Model (DPM) from images in the recently published KITTI benchmark suite as part of the object detection and orientation estimation challenge. We present a wide set of experiments and many hints on the different ways to supervise and enhance the well-known DPM on a challenging and naturalistic urban dataset as KITTI. The evaluation algorithm and metrics, the selection of a clean but representative subset of training samples and the DPM tuning are key factors to learn an object detector in a supervised fashion. We provide evidence of subtle differences in performance depending on these aspects. Besides, the generalization of the trained models to an independent dataset is validated by 5-fold cross-validation.


title: Human movement data for malaria control and elimination strategic planning

abstract: Recent increases in funding for malaria control have led to the reduction in transmission in many malaria endemic countries, prompting the national control programmes of 36 malaria endemic countries to set elimination targets. Accounting for human population movement (HPM) in planning for control, elimination and post-elimination surveillance is important, as evidenced by previous elimination attempts that were undermined by the reintroduction of malaria through HPM. Strategic control and elimination planning, therefore, requires quantitative information on HPM patterns and the translation of these into parasite dispersion. HPM patterns and the risk of malaria vary substantially across spatial and temporal scales, demographic and socioeconomic sub-groups, and motivation for travel, so multiple data sets are likely required for quantification of movement. While existing studies based on mobile phone call record data combined with malaria transmission maps have begun to address within-country HPM patterns, other aspects remain poorly quantified despite their importance in accurately gauging malaria movement patterns and building control and detection strategies, such as cross-border HPM, demographic and socioeconomic stratification of HPM patterns, forms of transport, personal malaria protection and other factors that modify malaria risk. A wealth of data exist to aid filling these gaps, which, when combined with spatial data on transport infrastructure, traffic and malaria transmission, can answer relevant questions to guide strategic planning. This review aims to (i) discuss relevant types of HPM across spatial and temporal scales, (ii) document where datasets exist to quantify HPM, (iii) highlight where data gaps remain and (iv) briefly put forward methods for integrating these datasets in a Geographic Information System (GIS) framework for analysing and modelling human population and Plasmodium falciparum malaria infection movements.


title: The OWL Reasoner Evaluation (ORE) 2015 Competition Report

abstract: The OWL Reasoner Evaluation competition is an annual competition (with an associated workshop) that pits OWL 2 compliant reasoners against each other on various standard reasoning tasks over naturally occurring problems. The 2015 competition was the third of its sort and had 14 reasoners competing in six tracks comprising three tasks (consistency, classification, and realisation) over two profiles (OWL 2 DL and EL). In this paper, we discuss the design, execution and results of the 2015 competition with particular attention to lessons learned for benchmarking, comparative experiments, and future competitions.


title: A review on image segmentation techniques

abstract: Many image segmentation techniques are available in the literature. Some of these techniques use only the gray level histogram, some use spatial details while others use fuzzy set theoretic approaches. Most of these techniques are not suitable for noisy environments. Some works have been done using the Markov Random Field (MRF) model which is robust to noise, but is computationally involved. Neural network architectures which help to get the output in real time because of their parallel processing ability, have also been used for segmentation and they work fine even when the noise level is very high. The literature on color image segmentation is not that rich as it is for gray tone images. This paper critically reviews and summarizes some of these techniques. Attempts have been made to cover both fuzzy and non-fuzzy techniques including color image segmentation and neural network based approaches. Adequate attention is paid to segmentation of range images and magnetic resonance images. It also addresses the issue of quantitative evaluation of segmentation results.


title: A Method for the Analytical Extraction of the Single-Diode PV Model Parameters

abstract: Determination of PV model parameters usually requires time consuming iterative procedures, prone to initialization and convergence difficulties. In this paper, a set of analytical expressions is introduced to determine the five parameters of the single-diode model for crystalline PV modules at any operating conditions, in a simple and straightforward manner. The derivation of these equations is based on a newly found relation between the diode ideality factor and the open circuit voltage, which is explicitly formulated using the temperature coefficients. The proposed extraction method is robust, cost-efficient, and easy-to-implement, as it relies only on datasheet information, while it is based on a solid theoretical background. Its accuracy and computational efficiency is verified and compared to other methods available in the literature through both simulation and outdoor measurements.


title: Emergent Communication through Negotiation

abstract: Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems. In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction. We introduce two communication protocols -- one grounded in the semantics of the game, and one which is \textit{a priori} ungrounded and is a form of cheap talk. We show that self-interested agents can use the pre-grounded communication channel to negotiate fairly, but are unable to effectively use the ungrounded channel. However, prosocial agents do learn to use cheap talk to find an optimal negotiating strategy, suggesting that cooperation is necessary for language to emerge. We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation.


title: A comparison of code similarity analysers

abstract: Copying and pasting of source code is a common activity in software engineering. Often, the code is not copied as it is and it may be modified for various purposes; e.g. refactoring, bug fixing, or even software plagiarism. These code modifications could affect the performance of code similarity analysers including code clone and plagiarism detectors to some certain degree. We are interested in two types of code modification in this study: pervasive modifications, i.e. transformations that may have a global effect, and local modifications, i.e. code changes that are contained in a single method or code block. We evaluate 30 code similarity detection techniques and tools using five experimental scenarios for Java source code. These are (1) pervasively modified code, created with tools for source code and bytecode obfuscation, and boiler-plate code, (2) source code normalisation through compilation and decompilation using different decompilers, (3) reuse of optimal configurations over different data sets, (4) tool evaluation using ranked-based measures, and (5) local + global code modifications. Our experimental results show that in the presence of pervasive modifications, some of the general textual similarity measures can offer similar performance to specialised code similarity tools, whilst in the presence of boiler-plate code, highly specialised source code similarity detection techniques and tools outperform textual similarity measures. Our study strongly validates the use of compilation/decompilation as a normalisation technique. Its use reduced false classifications to zero for three of the tools. Moreover, we demonstrate that optimal configurations are very sensitive to a specific data set. After directly applying optimal configurations derived from one data set to another, the tools perform poorly on the new data set. The code similarity analysers are thoroughly evaluated not only based on several well-known pair-based and query-based error measures but also on each specific type of pervasive code modification. This broad, thorough study is the largest in existence and potentially an invaluable guide for future users of similarity detection in source code.


Plate Recognition System


title: Describing function analysis of Dahl model friction

abstract: Mechanical systems subject to friction hysteresis exhibit amplitude dependent frequency responses. This paper derives the describing function (DF) of the Dahl friction model subject to a sinusoidal input. This DF is useful for analysis and modeling of systems subject to friction hysteresis from frequency response data.


title: Proximal Algorithms in Statistics and Machine Learning

abstract: Proximal algorithms are useful for obtaining solutions to difficult ::: optimization problems, especially those involving nonsmooth or composite ::: objective functions. A proximal algorithm is one whose basic iterations ::: involve the proximal operator of some function, whose evaluation requires ::: solving a specific optimization problem that is typically easier than the original ::: problem. Many familiar algorithms can be cast in this form, and this ::: “proximal view” turns out to provide a set of broad organizing principles ::: for many algorithms useful in statistics and machine learning. In this paper, ::: we show how a number of recent advances in this area can inform modern ::: statistical practice. We focus on several main themes: (1) variable splitting ::: strategies and the augmented Lagrangian; (2) the broad utility of envelope (or ::: variational) representations of objective functions; (3) proximal algorithms ::: for composite objective functions; and (4) the surprisingly large number of ::: functions for which there are closed-form solutions of proximal operators. ::: We illustrate our methodology with regularized Logistic and Poisson regression ::: incorporating a nonconvex bridge penalty and a fused lasso penalty. We ::: also discuss several related issues, including the convergence of nondescent ::: algorithms, acceleration and optimization for nonconvex functions. Finally, ::: we provide directions for future research in this exciting area at the intersection ::: of statistics and optimization.


title: PBIRCH: A Scalable Parallel Clustering algorithm for Incremental Data

abstract: We present a parallel version of BIRCH with the objective of enhancing the scalability without compromising on the quality of clustering. The incoming data is distributed in a cyclic manner (or block cyclic manner if the data is bursty) to balance the load among processors. The algorithm is implemented on a message passing share-nothing model. Experiments show that for very large data sets the algorithm scales nearly linearly with the increasing number of processors. Experiments also show that clusters obtained by PBIRCH are comparable to those obtained using BIRCH.


title: The Spreadsheet Space: Eliminating the Boundaries of Data Cross-Referencing

abstract: In the Spreadsheet Space, a virtual space for tabular data sharing, users in diverse administrative domains reference each others' spreadsheets at the cell level as well as share data from a variety of platforms and databases. Spreadsheet Space users enjoy the unlimited scope of cloud collaboration with the security of a more limited environment.


title: Reverse ontology matching

abstract: Ontology Matching aims to find the semantic correspondences between ontologies that belong to a single domain but that have been developed separately. However, there are still some problem areas to be solved, because experts are still needed to supervise the matching processes and an efficient way to reuse the alignments has not yet been found. We propose a novel technique named Reverse Ontology Matching, which aims to find the matching functions that were used in the original process. The use of these functions is very useful for aspects such as modeling behavior from experts, performing matching-by-example, reverse engineering existing ontology matching tools or compressing ontology alignment repositories. Moreover, the results obtained from a widely used benchmark dataset provide evidence of the effectiveness of this approach.


Sustainable Building Services


Phonetic Classification in TensorFlow