title: Building user profiles from topic models for personalised search

abstract: Personalisation is an important area in the field of IR that attempts to adapt ranking algorithms so that the results returned are tuned towards the searcher's interests. In this work we use query logs to build personalised ranking models in which user profiles are constructed based on the representation of clicked documents over a topic space. Instead of employing a human-generated ontology, we use novel latent topic models to determine these topics. Our experiments show that by subtly introducing user profiles as part of the ranking algorithm, rather than by re-ranking an existing list, we can provide personalised ranked lists of documents which improve significantly over a non-personalised baseline. Further examination shows that the performance of the personalised system is particularly good in cases where prior knowledge of the search query is limited.


Autophagy in Ovarian Follicular Development and Atresia


title: Probiotics in the intestinal tract of juvenile whiteleg shrimp Litopenaeus vannamei: modulation of the bacterial community.

abstract: Molecular analysis of the 16S rDNA of the intestinal microbiota of whiteleg shrimp Litopenaeus vannamei was examined to investigate the effect of a Bacillus mix (Bacillus endophyticus YC3-b, Bacillus endophyticus C2-2, Bacillus tequilensisYC5-2) and the commercial probiotic (Alibio(®)) on intestinal bacterial communities and resistance to Vibrio infection. PCR and single strain conformation polymorphism (SSCP) analyses were then performed on DNA extracted directly from guts. Injection of shrimp with V. parahaemolyticus at 2.5 × 10(5) CFU g(-1) per shrimp followed 168 h after inoculation with Bacillus mix or the Alibio probiotic or the positive control. Diversity analyses showed that the bacterial community resulting from the Bacillus mix had the highest diversity and evenness and the bacterial community of the control had the lowest diversity. The bacterial community treated with probiotics mainly consisted of α- and γ-proteobacteria, fusobacteria, sphingobacteria, and flavobacteria, while the control mainly consisted of α-proteobacteria and flavobacteria. Differences were grouped using principal component analyses of PCR-SSCP of the microbiota, according to the time of inoculation. In Vibrio parahaemolyticus-infected shrimp, the Bacillus mix (~33 %) induced a significant increase in survival compared to Alibio (~21 %) and the control (~9 %). We conclude that administration of the Bacillus mix induced modulation of the intestinal microbiota of L. vannamei and increased its resistance to V. parahaemolyticus.


title: Performance evaluation of Low Density Spreading Multiple Access

abstract: In this paper, we evaluate the performance of Multicarrier-Low Density Spreading Multiple Access (MC-LDSMA) as a multiple access technique for mobile communication systems. The MC-LDSMA technique is compared with current multiple access techniques, OFDMA and SC-FDMA. The performance is evaluated in terms of cubic metric, block error rate, spectral efficiency and fairness. The aim is to investigate the expected gains of using MC-LDSMA in the uplink for next generation cellular systems. The simulation results of the link and system-level performance evaluation show that MC-LDSMA has significant performance improvements over SC-FDMA and OFDMA. It is shown that using MC-LDSMA can considerably reduce the required transmission power and increase the spectral efficiency and fairness among the users.


title: Random forests for classification in ecology.

abstract: Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.


title: Learning a non-linear knowledge transfer model for cross-view action recognition

abstract: This paper concerns action recognition from unseen and unknown views. We propose unsupervised learning of a non-linear model that transfers knowledge from multiple views to a canonical view. The proposed Non-linear Knowledge Transfer Model (NKTM) is a deep network, with weight decay and sparsity constraints, which finds a shared high-level virtual path from videos captured from different unknown viewpoints to the same canonical view. The strength of our technique is that we learn a single NKTM for all actions and all camera viewing directions. Thus, NKTM does not require action labels during learning and knowledge of the camera viewpoints during training or testing. NKTM is learned once only from dense trajectories of synthetic points fitted to mocap data and then applied to real video data. Trajectories are coded with a general codebook learned from the same mocap data. NKTM is scalable to new action classes and training data as it does not require re-learning. Experiments on the IXMAS and N-UCLA datasets show that NKTM outperforms existing state-of-the-art methods for cross-view action recognition.


title: Combining Local and Global Features for 3D Face Tracking

abstract: In this paper, we propose to combine local and global features in a carefully designed convolutional neural network for 3D face alignment. We firstly adopt a part heatmap regression network to predict the landmark points on a local granularity by generating a series of heatmaps for each 3D landmark point. To enhance the ability of local feature representation, we incorporate the designed network with a part attention module, which transfers the convolution opereation into a channelwise attention opereation. Additionally, we take all these heatmaps alongside the input image as the input of another shape regression network in order to model the feature representations from local discrete regions to a global semantically continuous space. Extensive experiments on challenging datasets, AFLW2000-3D, 300VW and the Menpo Benchmark, show the effectiveness of both the global consistency and local description in our model, and the proposed algorithm outperforms state-of-the-art baselines.


title: Short-Term Plasticity and Synaptic Filtering Emulated in Electrolyte-Gated IGZO Transistors

abstract: Artificial synaptic devices have attracted a broad interest for hardware implementation of brain-inspired neuromorphic systems. In this letter, a short-term plasticity simulation in an indium–gallium–zinc oxide (IGZO) electric-double-layer (EDL) transistor is investigated. For synaptic facilitation and depression function emulation, three-terminal EDL transistor is reduced to a two-terminal synaptic device with two modified connection schemes. Furthermore, high-pass and low-pass filtering characteristics are also successfully emulated not only for fixed-rate spike train but also for Poisson-like spike train. Our results suggest that IGZO-based EDL transistors operated in two terminal mode can be used as the building blocks for brain-like chips and neuromorphic systems.


title: Sleep Problems in Urban, Minority, Early-School-Aged Children More Prevalent Than Previously Recognized

abstract: OBJECTIVES ::: To use the children's sleep habits questionnaire (CSHQ) to characterize sleep problems in a group of 5- to 6-year-old minority children living in urban communities and to compare our findings with data from 5- to 6-year-old children in the original CSHQ validation study. ::: ::: ::: METHODS ::: A cross-sectional study design was used to collect sleep data from parents using the CSHQ. ::: ::: ::: RESULTS ::: The CSHQ was completed by 160 parents; 150 (94%) scored ≥41, indicating a sleep problem. The prevalence of having sleep problems for our minority community sample was significantly higher than the original community sample (94% vs. 23%, P < .001). The minority sample also had significantly higher mean total CSHQ scores (51.5 vs 37.9, P < .001) and higher scores across all 8 subscales of the CSHQ (P < .001 for all comparisons). ::: ::: ::: CONCLUSIONS ::: The results suggest that sleep problems may be more prevalent in urban, early-school-aged minority children than previously reported.


title: A Guide to Differential Privacy Theory in Social Network Analysis

abstract: Privacy of social network data is a growing concern which threatens to limit access to this valuable data source. Analysis of the graph structure of social networks can provide valuable information for revenue generation and social science research, but unfortunately, ensuring this analysis does not violate individual privacy is difficult. Simply anonymizing graphs or even releasing only aggregate results of analysis may not provide sufficient protection. Differential privacy is an alternative privacy model, popular in data-mining over tabular data, which uses noise to obscure individuals' contributions to aggregate results and offers a very strong mathematical guarantee that individuals' presence in the data-set is hidden. Analyses that were previously vulnerable to identification of individuals and extraction of private data may be safely released under differential-privacy guarantees. We review two existing standards for adapting differential privacy to network data and analyse the feasibility of several common social-network analysis techniques under these standards. Additionally, we propose out-link privacy, a novel standard for differential privacy over network data, and introduce two powerful out-link private algorithms for common network analysis techniques that were infeasible to privatize under previous differential privacy standards.


title: Real-Time Gaze Estimation Using a Kinect and a HD Webcam

abstract: In human-computer interaction, gaze orientation is an important and promising source of information to demonstrate the attention and focus of users. Gaze detection can also be an extremely useful metric for analysing human mood and affect. Furthermore, gaze can be used as an input method for human-computer interaction. However, currently real-time and accurate gaze estimation is still an open problem. In this paper, we propose a simple and novel estimation model of the real-time gaze direction of a user on a computer screen. This method utilises cheap capturing devices, a HD webcam and a Microsoft Kinect. We consider that the gaze motion from a user facing forwards is composed of the local gaze motion shifted by eye motion and the global gaze motion driven by face motion. We validate our proposed model of gaze estimation and provide experimental evaluation of the reliability and the precision of the method.


title: Energy Efficient Convolutional Neural Networks for EEG Artifact Detection

abstract: This paper proposes an energy efficient Convolutional Neural Network based architecture for detecting different types of artifacts in multi-channel EEG signals. Our method achieves an average artifact detection accuracy of 74% and precision of 92% across seven different artifact types which outperforms existing techniques in terms of classification accuracy as well as the more common ICA based solution in terms of computational complexity and memory requirements. We designed a minimal neural network processor whose Verilog HDL is configurable for implementing 2n processing engines (PEs). We deployed our CNN on the processor, placed and routed on Artix-7 FPGA and examined different number of PEs at different operating frequencies. Our experiments indicate that utilizing 4 PEs operating at a clock frequency of 11.1 MHz is the optimal configuration for our hardware to yield the least classification energy consumption of 32 mJ accomplished in the maximum allowed prediction time of 1 Sec. We also implemented our CNN on TX2 NVIDIA platform and, by tweaking the CPU and the GPU frequencies, explored a least power configuration and another least energy consuming configuration. Our FPGA results indicate that the 4-PE implementation outperforms the low power config. of TX2 by 65× in terms of power, and the low energy config. of TX2 by 2× in terms of energy per classification. Our CNN-based FPGA implementation method also outperforms the ICA method by 11× in terms of energy consumption per classification.


title: Telemedicine adoption by different groups of physicians

abstract: This study addresses the factors that could affect the intention of physicians to adopt telemedicine technology. Based on the theoretical foundations of technology adoption models, a revised model is proposed and tested via a questionnaire with two groups of physicians that were, at the time of the survey, just about to use telemedicine technology. Group A is composed of physicians from a large urban healthcare provider institution involved in clinical, teaching, and research activities, and will soon use a telemedicine intranet solution. Group B is composed of physicians from rural areas who will eventually be linked to a telemedicine network. Results analyzed with PLS indicate that in both cases, physicians' perception of usefulness of telemedicine is positively related to their intention to adopt this technology. This is the only common result between the two groups. Other significant yet different results indicate that the reactions of two types of potential adopters of telemedicine are influenced by their background and environment. This revised model helps in distinguishing the shades in the intention of adopting telemedicine between two distinct groups of physicians.


title: Cross-Modal and Hierarchical Modeling of Video and Text

abstract: Visual data and text data are composed of information at multiple granularities. A video can describe a complex scene that is composed of multiple clips or shots, where each depicts a semantically coherent event or action. Similarly, a paragraph may contain sentences with different topics, which collectively conveys a coherent message or story. In this paper, we investigate the modeling techniques for such hierarchical sequential data where there are correspondences across multiple modalities. Specifically, we introduce hierarchical sequence embedding (HSE), a generic model for embedding sequential data of different modalities into hierarchically semantic spaces, with either explicit or implicit correspondence information. We perform empirical studies on large-scale video and paragraph retrieval datasets and demonstrated superior performance by the proposed methods. Furthermore, we examine the effectiveness of our learned embeddings when applied to downstream tasks. We show its utility in zero-shot action recognition and video captioning.


title: Key-Word-Aware Network for Referring Expression Image Segmentation

abstract: Referring expression image segmentation aims to segment out the object referred by a natural language query expression. Without considering the specific properties of visual and textual information, existing works usually deal with this task by directly feeding a foreground/background classifier with cascaded image and text features, which are extracted from each image region and the whole query, respectively. On the one hand, they ignore that each word in a query expression makes different contributions to identify the desired object, which requires a differential treatment in extracting text feature. On the other hand, the relationships of different image regions are not considered as well, even though they are greatly important to eliminate the undesired foreground object in accordance with specific query. To address aforementioned issues, in this paper, we propose a key-word-aware network, which contains a query attention model and a key-word-aware visual context model. In extracting text features, the query attention model attends to assign higher weights for the words which are more important for identifying object. Meanwhile, the key-word-aware visual context model describes the relationships among different image regions, according to corresponding query. Our proposed method outperforms state-of-the-art methods on two referring expression image segmentation databases.


title: Force Estimation Method for a Magnetic Lead-Screw-Driven Linear Actuator

abstract: Force-controllable actuators are essential for guaranteeing safety in human–robot interactions. Magnetic lead screws (MLSs) transfer force without requiring contact between parts. These devices can drive the parts with high efficiency and no frictional contact, and they are force limited when overloaded. We have developed a novel MLS that does not include spiral permanent magnets and an MLS-driven linear actuator (MLSDLA) that uses this device. This simple structure reduces the overall size of the device and improves productivity because it is constructed by a commonly used machined screw as a screw. The actuator can drive back against an external force and it moves flexibly based on the magnetic spring effect. In this paper, we propose a force estimation method for the MLSDLA that does not require separate sensors. The magnetic phase difference, as measured from the angular and linear displacements of the actuator, is used for this calculation. The estimated force is then compared against measurements recorded with a load sensor in order to verify the effectiveness of the proposed method.


title: Local jet pattern: a robust descriptor for texture classification

abstract: Methods based on local image features have recently shown promise for texture classification tasks, especially in the presence of large intra-class variation due to illumination, scale, and viewpoint changes. Inspired by the theories of image structure analysis, this paper presents a simple, efficient, yet robust descriptor namely local jet pattern (LJP) for texture classification. In this approach, a jet space representation of a texture image is derived from a set of derivatives of Gaussian (DtGs) filter responses up to second order, so called local jet vectors (LJV), which also satisfy the Scale Space properties. The LJP is obtained by utilizing the relationship of center pixel with the local neighborhood information in jet space. Finally, the feature vector of a texture region is formed by concatenating the histogram of LJP for all elements of LJV. All DtGs responses up to second order together preserves the intrinsic local image structure, and achieves invariance to scale, rotation, and reflection. This allows us to develop a texture classification framework which is discriminative and robust. Extensive experiments on five standard texture image databases, employing nearest subspace classifier (NSC), the proposed descriptor achieves 100%, 99.92%, 99.75%, 99.16%, and 99.65% accuracy for Outex_TC-00010 (Outex_TC10), and Outex_TC-00012 (Outex_TC12), KTH-TIPS, Brodatz, CUReT, respectively, which are outperforms the state-of-the-art methods.


title: Metamemory as evidence of animal consciousness: the type that does the trick

abstract: The question of whether non-human animals are conscious is of fundamental importance. There are already good reasons to think that many are, based on evolutionary continuity and other considerations. However, the hypothesis is notoriously resistant to direct empirical test. Numerous studies have shown behaviour in animals analogous to consciously-produced human behaviour. Fewer probe whether the same mechanisms are in use. One promising line of evidence about consciousness in other animals derives from experiments on metamemory. A study by Hampton (Proc Natl Acad Sci USA 98(9):5359–5362, 2001) suggests that at least one rhesus macaque can use metamemory to predict whether it would itself succeed on a delayed matching-to-sample task. Since it is not plausible that mere meta-representation requires consciousness, Hampton’s study invites an important question: what kind of metamemory is good evidence for consciousness? This paper argues that if it were found that an animal had a memory trace which allowed it to use information about a past perceptual stimulus to inform a range of different behaviours, that would indeed be good evidence that the animal was conscious. That functional characterisation can be tested by investigating whether successful performance on one metamemory task transfers to a range of new tasks. The paper goes on to argue that thinking about animal consciousness in this way helps in formulating a more precise functional characterisation of the mechanisms of conscious awareness.


title: R.: QUASY: Quantitative Synthesis Tool

abstract: We present the tool QUASY, a quantitative synthesis tool. QUASY takes qualitative and quantitative specifications and automatically constructs a system that satisfies the qualitative specification and optimizes the quantitative specification, if such a system exists. The user can choose between a system that satisfies and optimizes the specifications (a) under all possible environment behaviors or (b) under the most-likely environment behaviors given as a probability distribution on the possible input sequences. QUASY solves these two quantitative synthesis problems by reduction to instances of 2-player games and Markov Decision Processes (MDPs) with quantitative winning objectives. QUASY can also be seen as a game solver for quantitative games.Most notable, it can solve lexicographic mean-payoff games with 2 players, MDPs with mean-payoff objectives, and ergodic MDPs with mean-payoff parity objectives.


title: Crime Data Analysis and Prediction of Perpetrator Identity Using Machine Learning Approach

abstract: Crime is one of the most predominant and alarming aspects in our society and its prevention is a vital task. Crime analysis is a systematic way of detecting and investigating patterns and trends in crime. The aim of this model is to increase the efficiency of crime investigation systems. This model detects crime patterns from inferences collected from the crime scene and predicts the description of the perpetrator who is likely suspected to commit the crime. This work has two major aspects: Crime Analysis and Prediction of perpetrator identity. The Crime Analysis phase identifies the number of unsolved crimes, and analyses the influence of various factors like year, month, and weapon on the unsolved crimes. The prediction phase estimates the description of the perpetrators like, their age, sex and relationship with the victim. These predictions are done based on the evidences collected from the crime scene. The system predicts the description of the perpetrator using algorithms like, Multilinear Regression, K- Neighbors Classifier and Neural Networks. It was trained and tested using San Francisco Homicide dataset (1981–2014) and implemented using python.


title: Airgap-less electric motor: A solution for high-torque low-speed applications

abstract: This paper proposes a new category of electric motors able to generate high torque with reduced volume, i.e., high torque density. Such a new design is denominated as an airgapless electrical motor since the rotor touches the stator as it spins. Due to its zero airgap, the proposed motor will maximize the generated torque, allowing such type of motors to be competitive in applications where hydraulic motors are prevalent, i.e., low-speed and high-torque requirements. Unlike hydraulic motor systems that face two major problems with their braking system and also with low efficiency due to a large number of energy conversion stages (i.e., motor-pump, hydraulic connections and the hydraulic motor itself), the proposed electric motor converts electrical energy directly to mechanical energy with no extra braking system necessary and with higher efficiency. A proof-of-concept electric motor prototype along with its drive system is built to validate the theoretical assumptions proposed in this paper.


title: Situation and development tendency of indoor positioning

abstract: This paper introduces the significance of indoor positioning and analyzes the related problems. The latest research on indoor positioning is introduced. Further, the positioning accuracy and the cost of typical local and wide area indoor positioning systems are compared. The results of the comparison show that Time & Code Division-Orthogonal Frequency Division Multiplexing (TC-OFDM) is a system that can achieve real-time meter-accuracy of indoor positioning in a wide area. Finally, in this paper, we indicate that the seamless high-accuracy indoor positioning in a wide area is the development trend of indoor positioning. The seamless Location Based Services (LBS) architecture based on a heterogeneous network, key technologies in indoor positioning for decimeter-accuracy and seamless outdoor and indoor Geographic Information System (GIS) are elaborated as the most important research fields of future indoor positioning.


title: Scaling Up Stochastic Dual Coordinate Ascent

abstract: Stochastic Dual Coordinate Ascent (SDCA) has recently emerged as a state-of-the-art method for solving large-scale supervised learning problems formulated as minimization of convex loss functions. It performs iterative, random-coordinate updates to maximize the dual objective. Due to the sequential nature of the iterations, it is typically implemented as a single-threaded algorithm limited to in-memory datasets. In this paper, we introduce an asynchronous parallel version of the algorithm, analyze its convergence properties, and propose a solution for primal-dual synchronization required to achieve convergence in practice. In addition, we describe a method for scaling the algorithm to out-of-memory datasets via multi-threaded deserialization of block-compressed data. This approach yields sufficient pseudo-randomness to provide the same convergence rate as random-order in-memory access. Empirical evaluation demonstrates the efficiency of the proposed methods and their ability to fully utilize computational resources and scale to out-of-memory datasets.


title: Ontology mapping for querying heterogeneous information

abstract: Ontology mapping is becoming a crucial aspect in providing the background knowledge required for solving heterogeneity problems between semantically described data sources, and accessing distributed information repositories. Developing such ontology mapping has been a core issue of recent ontology research. In this paper we present a three-layer framework to (semi-)automatically discovering and using ontology mapping. We show how such resulting mapping is used for resolving semantic interrogation tasks, and enabling runtime semantic interoperability across heterogeneous information systems using semantic web technologies.


title: Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions

abstract: Social tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. This paper presents an overview of the field of social tagging systems which can be used for extending the capabilities of recommender systems. Various limitations of the current generation of social tagging systems and possible extensions that can provide better recommendation capabilities are also considered.


Image Processing Based Automatic Visual Inspection System for PCBs


title: Automatic Feature Extraction for Classifying Audio Data

abstract: Today, many private households as well as broadcasting or film companies own large collections of digital music plays. These are time series that differ from, e.g., weather reports or stocks market data. The task is normally that of classification, not prediction of the next value or recognizing a shape or motif. New methods for extracting features that allow to classify audio data have been developed. However, the development of appropriate feature extraction methods is a tedious effort, particularly because every new classification task requires tailoring the feature set anew. ::: ::: This paper presents a unifying framework for feature extraction from value series. Operators of this framework can be combined to feature extraction methods automatically, using a genetic programming approach. The construction of features is guided by the performance of the learning classifier which uses the features. Our approach to automatic feature extraction requires a balance between the completeness of the methods on one side and the tractability of searching for appropriate methods on the other side. In this paper, some theoretical considerations illustrate the trade-off. After the feature extraction, a second process learns a classifier from the transformed data. The practical use of the methods is shown by two types of experiments: classification of genres and classification according to user preferences.


title: Circularly Polarized Ellipse-Loaded Circular Slot Array for Millimeter-Wave WPAN Applications

abstract: A circularly polarized slot array suitable for millimeter wave WPAN applications is presented. The array uses a new type of circularly polarized element, comprising a circular slot loaded with an elliptical patch, and is fed by a microstrip line. The array is implemented on liquid crystal polymer (LCP), which is both low-loss and low-cost. A total of 8 array elements are used in a 4 times 2 configuration to achieve a maximum gain target of greater than 14 dBic. Sequential rotation of the elements within each of the 2 times 2 sub-arrays is used to increase the axial ratio bandwidth. Other novel features of the array are the finite ground plane, and open cavity structure used to support the LCP substrate of the slot array above a reflector plate. Measured and computed results presented for the packaged array confirm its wide impedance, axial ratio, and gain bandwidths. The effect of the packaged array back-short on the measured axial ratio is also discussed.


No . 066 June 23 , 2017 Theory of Deep Learning II : Landscape of the Empirical Risk in Deep Learning by


title: Textual description-based video summarization for video blogs

abstract: Recent popularization of camera devices, including action cams and smartphones, enables us to record videos in everyday life and share them through the Internet. Video blog is a recent approach for sharing videos, in which users enjoy expressing themselves in blog posts with attractive videos. Generating such videos, however, requires users to review vast amount of raw videos and edit them appropriately, which keeps users away from doing so. In this paper, we propose a novel video summarization method for helping users to create a video blog post. Unlike typical video summarization methods, the proposed method utilizes the text, which is written for a video blog post, and makes the video summary consistent with the content of the text. For this, we perform video summarization by solving an optimization problem, in which an objective function involves the content similarity between the summarized video and the text. Our user study with 20 participants has demonstrated that our proposed method is suitable to create video blog posts compared with conventional methods for video summarization.


title: Requirements engineering tools: Capabilities, survey and assessment

abstract: Context: There is a significant number of requirements engineering (RE) tools with different features and prices. However, existing RE tool lists do not provide detailed information about the features of the tools that they catalogue. It would therefore be interesting for both practitioners and tool developers to be aware of the state-of-the-art as regards RE tools. Objective: This paper presents the results of a survey answered by RE tool vendors. The purpose of the survey was to gain an insight into how current RE tools support the RE process by means of concrete capabilities, and to what degree. Method: The ISO/IEC TR 24766:2009 is a framework for assessing RE tools' capabilities. A 146-item questionnaire based principally on the features covered by this international guideline was sent to major tool vendors worldwide. A descriptive statistical study was then carried out to provide comparability, and bivariate correlation tests were also applied to measure the association between different variables. A sample of the tools was subjected to neutral assessment and an interrater reliability analysis was performed to ensure the reliability of the results. Results: The 38 participants sent back their answers. Most tools are delivered under a proprietary license, and their licenses are not free. A growing number of them facilitate Web access. Moreover, requirements elicitation exemplifies the best supported category of features in this study, whereas requirements modeling and management are the most badly supported categories. Conclusion: The RE process seems to be well covered by current RE tools, but there is still a certain margin for amelioration, principally with regard to requirements modeling, open data model and data integration features. These subjects represent areas for improvement for RE tool developers. Practitioners might also obtain useful ideas from the study to be taken into account when selecting an appropriate RE tool to be successfully applied to their work.


title: A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images

abstract: Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions.


title: A Wideband Differential-Fed Slot Antenna Using Integrated Compact Balun With Matching Capability

abstract: This communication proposes a wideband differential-fed slot antenna that works in its dominant mode ( λ/2 mode). The antenna has a near omnidirectional radiation pattern with vertical polarization, which is designed for near-ground wireless sensor node applications. A compact integrated T-slot balun and a loop to T-slot feeding structure are introduced to realize the differential feeding; the bandwidth is expanded to 30.7% (2.2 GHz-3.0 GHz) with a 3.8 dB peak gain. A detailed matching method without using lumped elements is proposed, which can be applied to various types of RFIC chips. A prototype is built and measured. Its differential impedance, antenna patterns, and gain are consistent with the simulation results.


title: Effects of Early and Late Nocturnal Sleep on Declarative and Procedural Memory

abstract: Recall of paired-associate lists (declarative memory) and mirror-tracing skills (procedural memory) was assessed after retention intervals defined over early and late nocturnal sleep. In addition, effects of sleep on recall were compared with those of early and late retention intervals filled with wakefulness. Twenty healthy men served as subjects. Saliva cortisol concentrations were determined before and after the retention intervals to determine pituitary-adrenal secretory activity. Sleep was determined somnopolygraphically. Sleep generally enhanced recall when compared with the effects of corresponding retention intervals of wakefulness. The benefit from sleep on recall depended on the phase of sleep and on the type of memory: Recall of paired-associate lists improved more during early sleep, and recall of mirror-tracing skills improved more during late sleep. The effects may reflect different influences of slow wave sleep (SWS) and rapid eye movement (REM) sleep since time in SWS was 5 times longer during the early than late sleep retention interval, and time in REM sleep was twice as long during late than early sleep (p < 0.005). Changes in cortisol concentrations, which independently of sleep and wakefulness were lower during early retention intervals than late ones, cannot account for the effects of sleep on memory. The experiments for the first time dissociate specific effects of early and late sleep on two principal types of memory, declarative and procedural, in humans.


title: Modeling the EXIF-Image correlation for image manipulation detection

abstract: EXchangeable Image File format (EXIF) is a metadata header containing shot-related camera settings such as aperture, exposure time, ISO speed etc. These settings can affect the photo content in many ways. In this paper, we investigate the underlying EXIF-Image correlation and propose a novel model, which correlates image statistical noise features with several commonly used EXIF features. By formulating each EXIF feature as a weighted combination of different image statistical noise features, we first select a compact image statistical noise feature set using sequential floating forward selection. The underlying correlation as a set of regression weights is then solved using a least squares solution. When applying our learned correlation to detect image manipulation, we achieve average test accuracies of 94.6%, 94.1% and 94.9% in three different cameras to detect the presence of common image brightness and contrast adjustment.


title: Seed Crystal Homogeneity Controls Lateral and Vertical Heteroepitaxy of Monolayer MoS2 and WS2.

abstract: Heteroepitaxy between transition-metal dichalcogenide (TMDC) monolayers can fabricate atomically thin semiconductor heterojunctions without interfacial contamination, which are essential for next-generation electronics and optoelectronics. Here we report a controllable two-step chemical vapor deposition (CVD) process for lateral and vertical heteroepitaxy between monolayer WS2 and MoS2 on a c-cut sapphire substrate. Lateral and vertical heteroepitaxy can be selectively achieved by carefully controlling the growth of MoS2 monolayers that are used as two-dimensional (2D) seed crystals. Using hydrogen as a carrier gas, we synthesize ultraclean MoS2 monolayers, which enable lateral heteroepitaxial growth of monolayer WS2 from the MoS2 edges to create atomically coherent and sharp in-plane WS2/MoS2 heterojunctions. When no hydrogen is used, we obtain MoS2 monolayers decorated with small particles along the edges, inducing vertical heteroepitaxial growth of monolayer WS2 on top of the MoS2 to form vertical WS2/MoS2 heterojunctions. Our lateral and vertical atomic layer heteroepitaxy steered by seed defect engineering opens up a new route toward atomically controlled fabrication of 2D heterojunction architectures.


title: Predicting good probabilities with supervised learning

abstract: We examine the relationship between the predictions made by different learning algorithms and true posterior probabilities. We show that maximum margin methods such as boosted trees and boosted stumps push probability mass away from 0 and 1 yielding a characteristic sigmoid shaped distortion in the predicted probabilities. Models such as Naive Bayes, which make unrealistic independence assumptions, push probabilities toward 0 and 1. Other models such as neural nets and bagged trees do not have these biases and predict well calibrated probabilities. We experiment with two ways of correcting the biased probabilities predicted by some learning methods: Platt Scaling and Isotonic Regression. We qualitatively examine what kinds of distortions these calibration methods are suitable for and quantitatively examine how much data they need to be effective. The empirical results show that after calibration boosted trees, random forests, and SVMs predict the best probabilities.


title: Common Gynecologic Problems in Prepubertal Girls

abstract: 1. Naomi F. Sugar, MD* ::: 2. Elinor A. Graham, MD, MPH† ::: ::: ::: ::: ::: 1. *Clinical Associate Professor, Department of Pediatrics, Division of General Pediatrics, University of Washington, Seattle, Wash ::: ::: 2. †Associate Professor, Department of Pediatrics, Division of General Pediatrics, University of Washington, Seattle, Wash ::: ::: After completing this article, readers should be able to: ::: ::: 1. Describe techniques to perform an adequate and gentle genital examination in a school-age girl. ::: ::: 2. Discuss the differential diagnosis for vaginal bleeding in the prepubertal girl. ::: ::: 3. Identify a strategy to diagnose and treat vaginitis in the prepubertal child. ::: ::: Assessing gynecologic symptoms and signs in prepubertal children can be a challenge for pediatricians. Adult pelvic examinations are a standard part of medical school education, and learning objectives for the American Board of Pediatrics emphasize knowledge of adolescent gynecology. Pediatric practice standards recommend routine external examinations for girls of all ages at health supervision examinations. Unfortunately, pediatricians often lack training in examination techniques, determination of normal findings, and gynecologic pathology of infants and prepubertal children. ::: ::: In the past decade, a high level of concern for child sexual abuse and emphasis on the sentinel role of pediatricians in recognizing abnormalities due to child abuse paradoxically has left pediatricians and other child health clinicians more anxious and uncomfortable in performing these examinations. However, although child sexual abuse is common, it rarely is diagnosed primarily by physical complaints. It is much more likely that a girl who is brought to the physician because of genital symptoms has either normal variant findings or a nontraumatic disorder. (1) The aim of this article is to review normal anatomy and physiology in infants and prepubertal children, emphasizing developmental changes, normal variants, and the differential diagnosis for common signs and symptoms. ::: ::: ### Infant and Toddler ::: ::: The newborn girl is affected by maternal estrogens that cross the placenta. Breast buds are usual, and a milky discharge from the nipples is not uncommon. Like her mother, the neonate may develop mastitis. Vaginal discharge is common in newborns, as are long hymenal tags that frequently are visible, extending outside the vestibule. The vaginal epithelium also demonstrates the estrogen …


title: Real-Time Big Data Stream Processing Using GPU with Spark Over Hadoop Ecosystem

abstract: In this technological era, every person, authorities, entrepreneurs, businesses, and many things around us are connected to the internet, forming Internet of thing (IoT). This generates a massive amount of diverse data with very high-speed, termed as big data. However, this data is very useful that can be used as an asset for the businesses, organizations, and authorities to predict future in various aspects. However, efficiently processing Big Data while making real-time decisions is a quite challenging task. Some of the tools like Hadoop are used for Big Datasets processing. On the other hand, these tools could not perform well in the case of real-time high-speed stream processing. Therefore, in this paper, we proposed an efficient and real-time Big Data stream processing approach while mapping Hadoop MapReduce equivalent mechanism on graphics processing units (GPUs). We integrated a parallel and distributed environment of Hadoop ecosystem and a real-time streaming processing tool, i.e., Spark with GPU to make the system more powerful in order to handle the overwhelming amount of high-speed streaming. We designed a MapReduce equivalent algorithm for GPUs for a statistical parameter calculation by dividing overall Big Data files into fixed-size blocks. Finally, the system is evaluated while considering the efficiency aspect (processing time and throughput) using (1) large-size city traffic video data captured by static as well as moving vehicles’ cameras while identifying vehicles and (2) large text-based files, like twitter data files, structural data, etc. Results show that the proposed system working with Spark on top and GPUs under the parallel and distributed environment of Hadoop ecosystem is more efficient and real-time as compared to existing standalone CPU-based MapReduce implementation.


title: Key Influences of Cyberbullying for University Students

abstract: It is hard to imagine a world without mobile phones, PDAs, and wireless/Broadband access on PCs. With the prevalence of electronic communication tools, people (including students) are finding it harder to live without them. Not surprisingly, these electronic communication tools provide benefits that enrich students’ scholarly and social experiences. However, evidence also shows that the usage of electronic communication tools can result in negative behaviour such as cyberbullying. This study, building on prior research findings, develops a conceptual model to explore influences other than psychological factors, which lead to individuals displaying cyberbullying behaviour. We collected data from 134 university students, and our analysis indicates that 62% of the representative sample had experienced cyberbullying in the past year, and 40% of our respondents have conducted actions which can be constituted as cyberbullying behaviour in the past year. Our study also suggests that technology usage and social pressure are additional factors that influence a person to conduct cyberbullying related actions. Results from this study can provide directions to concerned parties, to develop strategies and policies to reduce this social problem to a minimum.


title: Survey Paper: Constrained model predictive control: Stability and

abstract: Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon open-loop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and the first control in this sequence is applied to the plant. An important advantage of this type of control is its ability to cope with hard constraints on controls and states. It has, therefore, been widely applied in petro-chemical and related industries where satisfaction of constraints is particularly important because efficiency demands operating points on or close to the boundary of the set of admissible states and controls. In this review, we focus on model predictive control of constrained systems, both linear and nonlinear and discuss only briefly model predictive control of unconstrained nonlinear and/or time-varying systems. We concentrate our attention on research dealing with stability and optimality; in these areas the subject has developed, in our opinion, to a stage where it has achieved sufficient maturity to warrant the active interest of researchers in nonlinear control. We distill from an extensive literature essential principles that ensure stability and use these to present a concise characterization of most of the model predictive controllers that have been proposed in the literature. In some cases the finite horizon optimal control problem solved on-line is exactly equivalent to the same problem with an infinite horizon; in other cases it is equivalent to a modified infinite horizon optimal control problem. In both situations, known advantages of infinite horizon optimal control accrue.


title: Tracing Cross Border Web Tracking

abstract: A tracking flow is a flow between an end user and a Web tracking service. We develop an extensive measurement methodology for quantifying at scale the amount of tracking flows that cross data protection borders, be it national or international, such as the EU28 border within which the General Data Protection Regulation (GDPR) applies. Our methodology uses a browser extension to fully render advertising and tracking code, various lists and heuristics to extract well known trackers, passive DNS replication to get all the IP ranges of trackers, and state-of-the art geolocation. We employ our methodology on a dataset from 350 real users of the browser extension over a period of more than four months, and then generalize our results by analyzing billions of web tracking flows from more than 60 million broadband and mobile users from 4 large European ISPs. We show that the majority of tracking flows cross national borders in Europe but, unlike popular belief, are pretty well confined within the larger GDPR jurisdiction. Simple DNS redirection and PoP mirroring can increase national confinement while sealing almost all tracking flows within Europe. Last, we show that cross boarder tracking is prevalent even in sensitive and hence protected data categories and groups including health, sexual orientation, minors, and others.


title: Uncertainty principles for signals defined on graphs: Bounds and characterizations

abstract: The classical uncertainty principle provides a fundamental tradeoff in the localization of a signal in the time and frequency domains. In this paper we describe a similar tradeoff for signals defined on graphs. We describe the notions of “spread” in the graph and spectral domains, using the eigenvectors of the graph Laplacian as a surrogate Fourier basis. We then describe how to find signals that, among all signals with the same spectral spread, have the smallest graph spread about a given vertex. For every possible spectral spread, the desired signal is the solution to an eigenvalue problem. Since localization in graph and spectral domains is a desirable property of the elements of wavelet frames on graphs, we compare the performance of some existing wavelet transforms to the obtained bound.


title: To feel or not to feel:The role of affect in human–computer interaction

abstract: The past decade has witnessed an unprecedented growth in user interface and human-computer interaction (HCI) technologies and methods. The synergy of technological and methodological progress on the one hand, and changing user expectations on the other, are contributing to a redefinition of the requirements for effective and desirable human-computer interaction. A key component of these emerging requirements, and of effective HCI in general, is the ability of these emerging systems to address user affect. The objective of this special issue is to provide an introduction to the emerging research area of affective HCI, some of the available methods and techniques, and representative systems and applications.


title: Using TLA+ in the Development of a Safety-Critical Fault-Tolerant Middleware

abstract: Creating and implementing fault-tolerant distributed algorithms is a challenging task in highly safety-critical industries. Using formal methods supports design and development of complex algorithms. However, formal methods are often perceived as an unjustifiable overhead.This paper presents the experience and insights when using TLA+ and PlusCal to model and develop fault-tolerant and safety-critical modules for TAS Control Platform, a platform for railway control applications up to safety integrity level (SIL) 4.We show how formal methods helped us improve the correctness of the algorithms, improved development efficiency and how part of the gap between model and implementation has been closed by translation to C code. Additionally, we describe how we gained trust in the formal model and tools by following a specific design process called property-driven design, which also implicitly addresses software quality metrics such as code coverage metrics.


title: Stochastics Models, Estimation, and Control: Introduction

abstract: A hopper stores a quantity of dry, particulate animal feed and is partially closed at the bottom by a feed guide plate having downwardly curved sides and a central circular orifice. The orifice extends into an agitator chamber having generally conical wall surfaces and a flat circular floor. The conical walls of the agitator chamber include a discharge opening which leads to a delivery chute. A motor driven agitator blade having an upstanding feeder shaft extending into the hopper orifice is rotatably mounted above the floor of the agitator chamber. When the motor is energized rotation of the agitator blade, including the feeder shaft, causes feed to flow from the hopper orifice into the path of the agitator blade. The rotating blade fluidizes the feed particles and centrifugally deflects them in a circular path about the chamber walls so that the feed flows through the discharge opening down the delivery chute and into an eating bowl.


title: Using while moving: HCI issues in fieldwork environments

abstract: “Using while moving” is the basic ability fieldwork users require of a mobile computer system. These users come from a wide range of backgrounds but have in common an extremely mobile and dynamic workplace. We identify four specific characteristics of this class of users: dynamic user configuration, limited attention capacity, high-speed interaction, and context dependency. A prototype is then presented that was designed to assist fieldworkers in data collection tasks and to explore the HCI design issues involved. The prototype was used in an extensive field trial by a group of ecologists observing giraffe behavior in Kenya. Following this trial, improvements were made to the prototype interface which in turn was tested in a subsequent field trial with another group of ecologists. From this experience, we have formulated our resulting ideas about interface design for fieldworkers into two general principles: Minimal Attention User Interfaces (MAUIs) and context awareness. The MAUI seeks to minimize the attention, though not necessarily the number of interactions, required from the user in operating a device. Context awareness enables the mobile device to provide assistance based on a knowledge of its environment.


title: Hierarchical document categorization with support vector machines

abstract: Automatically categorizing documents into pre-defined topic hierarchies or taxonomies is a crucial step in knowledge and content management. Standard machine learning techniques like Support Vector Machines and related large margin methods have been successfully applied for this task, albeit the fact that they ignore the inter-class relationships. In this paper, we propose a novel hierarchical classification method that generalizes Support Vector Machine learning and that is based on discriminant functions that are structured in a way that mirrors the class hierarchy. Our method can work with arbitrary, not necessarily singly connected taxonomies and can deal with task-specific loss functions. All parameters are learned jointly by optimizing a common objective function corresponding to a regularized upper bound on the empirical loss. We present experimental results on the WIPO-alpha patent collection to show the competitiveness of our approach.


title: Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning

abstract: Internet traffic classification has become more important with rapid growth of current Internet network and online applications. There have been numerous studies on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a \emph{deep learning} based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called "Deep Packet," can handle both \emph{traffic characterization} in which the network traffic is categorized into major classes (\eg, FTP and P2P) and application identification in which end-user applications (\eg, BitTorrent and Skype) identification is desired. Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. After an initial pre-processing phase on data, packets are fed into Deep Packet framework that embeds stacked autoencoder and convolution neural network in order to classify network traffic. Deep packet with CNN as its classification model achieved recall of $0.98$ in application identification task and $0.94$ in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset.


title: An enhanced e-learning ecosystem based on an integration between cloud computing and Web2.0

abstract: What we know is less important than our capacity to continue to learn more until e-learning appeared. While e-learning technology has matured considerably since its inception, there are still many problems that practitioners find when come to implementing e-learning. Today's knowledge society of the 21st century requires a flexible learning environment which is capable to adapt according to teaching and learning objectives, students' profiles and preferences for information and communication technologies and services. Advances in technology offer new opportunities in enhancing teaching and learning. Many advances in learning technologies are taking place throughout the world. The new technologies enable individuals to personalize the environment in which they work or learn, utilizing a range of tools to meet their interests and needs. Research community has believed that an e-learning ecosystem is the next generation e-learning but has faced challenges in optimizing resource allocations, dealing with dynamic demands on getting information and knowledge anywhere and anytime, handling rapid storage growth requirements, cost controlling and greater flexibility. Additionally, e-learning ecosystems need to improve its infrastructure, which can devote the required computation and storage resources for e-learning ecosystems. So, we need flourish, growing, up-to-date and strong infrastructure e-learning ecosystems in a productive and cost-effective way to be able to face rapidly-changing environments. In this paper, an e-learning ecosystem (ELES) which supports modern technologies is introduced and implemented. An integration between cloud computing and Web 2.0 technologies and services will be used to support the development of e-learning ecosystems; cloud computing as an adoptable technology for many of the organizations with its dynamic scalability and usage of virtualized resources as a service through the Internet. Web 2.0 brings new instruments help building dynamic e-learning ecosystem on the web.


title: The hindsight bias: A meta-analysis

abstract: Abstract The hindsight bias in probability assessments is one of the most frequently cited judgment biases. A meta-analysis of 122 studies revealed evidence that the bias occurs under some conditions and that its effect can be moderated by a subject's familiarity with the task and by the type of outcome information presented. The data also suggest that the use of “almanac” questions can generate an unusually large hindsight effect. An observed asymmetry in the effect of the hindsight bias suggests that cognitive and not motivational factors may be the main cause of the bias. Finally, the overall magnitude of the effect of the hindsight bias was found to be small (r = .17). While these results suggest that the bias may not be as worrisome as commonly assumed in the literature, we discuss some situations when an effect this small may be of importance to practitioners. We also show that, depending upon the familiarity of the task and type of outcome information presented, anywhere from a minimum of 0% to a maximum of 7–27% of the population may make different decisions because of the hindsight bias.


title: Analogical Reinforcement Learning

abstract: Analogical Reinforcement Learning James M. Foster & Matt Jones james.m.foster@colorado.edu, mcj@colorado.edu University of Colorado, Department of Psychology & Neuroscience Boulder, CO 80309 USA Abstract learning. Conversely, the prediction-error signals from RL can be used to guide induction of new higher-order relational concepts. Thus we propose there exists a computationally powerful synergy between analogy and RL. The simulation experiment reported here supports this claim. Because of the strong empirical evidence for each of these mechanisms taken separately, we conjecture that the brain exploits this synergy as well. Research in analogical reasoning suggests that higher-order cognitive functions such as abstract reasoning, far transfer, and creativity are founded on recognizing structural similari- ties among relational systems. Here we integrate theories of analogy with the computational framework of reinforcement learning (RL). We propose a computational synergy between analogy and RL, in which analogical comparison provides the RL learning algorithm with a measure of relational similar- ity, and RL provides feedback signals that can drive analogical learning. Initial simulation results support the power of this approach. Keywords: Analogy; Reinforcement Learning; Schema In- duction; Similarity; Generalization Analogy Research in human conceptual knowledge representation has shown that concepts are represented not just as distributions of features (cf. Nosofsky, 1986; Rosch & Mervis, 1975) but as relational structures. This relational knowledge includes both internal structure, such as the fact that a robin’s wings allow it to fly (Sloman et al., 1998), as well as external structure, such as the fact that a dog likes to chase cats (Jones & Love, 2007). Theories of analogical reasoning represent relational knowl- edge of this type in a predicate calculus that binds objects to the roles of relations, for example CHASE ( DOG , CAT ). Ac- cording to these theories, an analogy between two complex episodes (each a network of relations and objects) amounts to recognition that they share a common relational structure (Gentner, 1983; Hummel & Holyoak, 2003). At a more mechanistic level, the dominant theory of anal- ogy is structural alignment (Gentner, 1983). This process involves building a mapping between two episodes, mapping objects to objects and relations to relations. The best map- ping is one that maps objects to similar objects, maps rela- tions to similar relations, and most importantly, satisfies par- allel connectivity. Parallel connectivity means that, when- ever two relations are mapped to each other, the objects fill- ing their respective role-fillers are also mapped together. An example is shown in Figure 1. Parallel connectivity is sat- isfied here because, for each mapped pair of ATTACK rela- tions (red arrows), the objects filling the ATTACKER role are mapped together (knight is mapped to queen), and the objects filling the ATTACKED role are also mapped together (rook to rook and king to king). Thus structural alignment con- stitutes a (potentially partial or imperfect) isomorphism be- tween two episodes, which respects the relational structure that they have in common. Importantly, if the search for a mapping gives little emphasis to object-level similarity (as opposed to relation-level similarity and parallel connectivity), then structural alignment can find abstract commonalities be- tween episodes having little or no surface similarity (i.e., in terms of perceptual features). We propose structural alignment is critical to learning of Introduction The goal of the present work is to develop a computational understanding of how people learn abstract concepts. Pre- vious research in analogical reasoning suggests that higher- order cognitive functions such as abstract reasoning, far trans- fer, and creativity are founded on recognizing structural sim- ilarities among relational systems (Doumas et al., 2008; Gen- tner, 1983; Hummel & Holyoak, 2003). However, we argue a critical element is missing from these theories, in that their operation is essentially unsupervised, merely seeking patterns that recur in the environment, rather than focusing on the ones that are predictive of reward or other important outcomes. Here we integrate theories of analogy with the computa- tional framework of reinforcement learning (RL). RL offers a family of learning algorithms that have been highly success- ful in machine learning applications (e.g., Bagnell & Schnei- der, 2001; Tesauro, 1995) and that have neurophysiological support in the brain (e.g., Schultz et al., 1997). A shortcom- ing of RL is that it only learns efficiently in complex tasks if it starts with a representation (i.e., a means for encoding stimuli or states of the environment) that somehow captures the crit- ical structure inherent in the task. We formalize this notion below in terms of similarity-based generalization (Shepard, 1987) and kernel methods from statistical machine learning (Shawe-Taylor & Cristianini, 2004). In other words, RL re- quires a sophisticated sense of similarity to succeed in real- istically complex tasks. Psychologically, the question of how such a similarity function is learned can be cast as a question of learning sophisticated, abstract representations. This paper proposes a computational model of analogical RL, in which analogical comparison provides the RL learning algorithm with a measure of relational similarity, and RL pro- vides feedback signals that can drive analogical learning. Re- lational similarity enables RL to generalize knowledge from past to current situations more efficiently, leading to faster


Learning Musical Structure Directly from Sequences of Music


title: Morph Deterction from Single Face Image: a Multi-Algorithm Fusion Approach

abstract: The vulnerability of face, fingerprint and iris recognition systems to attacks based on morphed biometric samples has been established in the recent past. However, so far a reliable detection of morphed biometric samples has remained an unsolved research challenge. In this work, we propose the first multi-algorithm fusion approach to detect morphed facial images. The FRGCv2 face database is used to create a set of 4,808 morphed and 2,210 bona fide face images which are divided into a training and test set. From a single cropped facial image features are extracted using four types of complementary feature extraction algorithms, including texture descriptors, keypoint extractors, gradient estimators and a deep learning-based method. By performing a score-level fusion of comparison scores obtained by four different types of feature extractors, a detection equal error rate (D-EER) of 2.8% is achieved. Compared to the best single algorithm approach achieving a D-EER of 5.5%, the D-EER of the proposed multi-algorithm fusion system is al- most twice as low, confirming the soundness of the presented approach.


title: Evaluating Critical Security Issues of the IoT World: Present and Future Challenges

abstract: Social Internet of Things (SIoT) is a new paradigm where Internet of Things (IoT) merges with social networks, allowing people and devices to interact, and facilitating information sharing. However, security and privacy issues are a great challenge for IoT but they are also enabling factors to create a “trust ecosystem.” In fact, the intrinsic vulnerabilities of IoT devices, with limited resources and heterogeneous technologies, together with the lack of specifically designed IoT standards, represent a fertile ground for the expansion of specific cyber threats. In this paper, we try to bring order on the IoT security panorama providing a taxonomic analysis from the perspective of the three main key layers of the IoT system model: 1) perception; 2) transportation; and 3) application levels. As a result of the analysis, we will highlight the most critical issues with the aim of guiding future research directions.


title: SurfelWarp: Efficient Non-Volumetric Single View Dynamic Reconstruction

abstract: We contribute a dense SLAM system that takes a live stream of depth images as input and reconstructs non-rigid deforming scenes in real time, without templates or prior models. In contrast to existing approaches, we do not maintain any volumetric data structures, such as truncated signed distance function (TSDF) fields or deformation fields, which are performance and memory intensive. Our system works with a flat point (surfel) based representation of geometry, which can be directly acquired from commodity depth sensors. Standard graphics pipelines and general purpose GPU (GPGPU) computing are leveraged for all central operations: i.e., nearest neighbor maintenance, non-rigid deformation field estimation and fusion of depth measurements. Our pipeline inherently avoids expensive volumetric operations such as marching cubes, volumetric fusion and dense deformation field update, leading to significantly improved performance. Furthermore, the explicit and flexible surfel based geometry representation enables efficient tackling of topology changes and tracking failures, which makes our reconstructions consistent with updated depth observations. Our system allows robots to maintain a scene description with non-rigidly deformed objects that potentially enables interactions with dynamic working environments.


title: Modular software architecture for human-robot interaction applied to the InterBot mobile robot

abstract: This paper describes a new modular software architecture designed to allow collaborative Human-Robot Interaction (HRI). The proposed HRI system is applied to the InterBot (Interactive mobile roBot), which integrates two Human-Machine Interface (HMI) devices: an on-board portable device and a remote station. The software architecture is mainly developed using the Robot Operating System (ROS) framework, and is designed to be modular and versatile. The proposed software modules provide a reliable structure for users to interact with and directly control the InterBot, e.g. allowing a human to be guided during a tour, or for a human to order the InterBot to perform tasks such as delivering messages or items between laboratories. The remote station acts as the supervisor, being in charge of planning the robot tasks sequentially, while displaying the robot information in a perceivable way. A series of tests and experiments were carried out in order to assess the performance of the InterBot platform in different test scenarios and under three Human-Robot control modes: teleoperation, autonomous and shared control. Presented real-world tests show the functional flexibility of the proposed architecture and outline future directions of development and research. The results also show that the InterBot software architecture is robust to the occurrence of network delays.


title: Vision-based pedestrian detection -reliable pedestrian candidate detection by combining IPM and a 1D profile

abstract: This article presents the improvement of an Inverse Perspective Mapping (IPM) based obstacle detection algorithm and its utilization in a pedestrian candidate detection module of our vision based pedestrian detection system. A vertical 1D profile of the IPM detection on the region of interest termed Pedestrian Detection Strip (PDS) is created first and the candidates are chosen by applying a threshold on the profile. The usage of the vertical profile increases the robustness of the detection on low contrast images as well as distant pedestrians significantly. A low level pedestrian oriented segmentation and fast symmetry search on the leg region of pedestrians is also presented.


title: Linked independent component analysis for multimodal data fusion

abstract: In recent years, neuroimaging studies have increasingly been acquiring multiple modalities of data and searching for task- or disease-related changes in each modality separately. A major challenge in analysis is to find systematic approaches for fusing these differing data types together to automatically find patterns of related changes across multiple modalities, when they exist. Independent Component Analysis (ICA) is a popular unsupervised learning method that can be used to find the modes of variation in neuroimaging data across a group of subjects. When multimodal data is acquired for the subjects, ICA is typically performed separately on each modality, leading to incompatible decompositions across modalities. Using a modular Bayesian framework, we develop a novel "Linked ICA" model for simultaneously modelling and discovering common features across multiple modalities, which can potentially have completely different units, signal- and contrast-to-noise ratios, voxel counts, spatial smoothnesses and intensity distributions. Furthermore, this general model can be configured to allow tensor ICA or spatially-concatenated ICA decompositions, or a combination of both at the same time. Linked ICA automatically determines the optimal weighting of each modality, and also can detect single-modality structured components when present. This is a fully probabilistic approach, implemented using Variational Bayes. We evaluate the method on simulated multimodal data sets, as well as on a real data set of Alzheimer's patients and age-matched controls that combines two very different types of structural MRI data: morphological data (grey matter density) and diffusion data (fractional anisotropy, mean diffusivity, and tensor mode).


title: A fully data parallel WFSTbased large vocabulary continuous speech recognition on a graphics processing unit

abstract: Tremendous compute throughput is becoming available in personal desktop and laptop systems through the use of graphics processing units (GPUs). However, exploiting this resource requires re-architecting an application to fit a data parallel programming model. The complex graph traversal routines in the inference process for large vocabulary continuous speech recognition (LVCSR) have been considered by many as unsuitable for extensive parallelization. We explore and demonstrate a fully data parallel implementation of a speech inference engine on NVIDIA’s GTX280 GPU. Our implementation consists of two phases compute-intensive observation probability computation phase and communication-intensive graph traversal phase. We take advantage of dynamic elimination of redundant computation in the compute-intensive phase while maintaining close-to-peak execution efficiency. We also demonstrate the importance of exploring application-level trade-offs in the communication-intensive graph traversal phase to adapt the algorithm to data parallel execution on GPUs. On 3.1 hours of speech data set, we achieve more than 11× speedup compared to a highly optimized sequential implementation on Intel Core i7 without sacrificing accuracy.


title: Analysis and Design of a Multiport Converter Using a Magnetic Coupling Inductor Technique

abstract: A novel multiport dc–dc converter with a coupling magnetic inductor is analyzed and designed. The proposed circuit integrates two multiphase converters and one isolated dc–dc converter. These converters can be controlled independently in one circuit by adjusting the duty ratio and phase angle difference. There are four dc ports in the circuit, and the dc power can be delivered multidirectionally among the four dc ports. In this paper, a 1.5-kW prototype of the multiport converter was constructed and successfully operated under full power. The experimental results confirm the theoretical analysis, and the conversion efficiency is above 90% over a wide range of output power. In addition, an example of a multifunctional power conversion system using the multiport converter is demonstrated.


title: Learning face hallucination in the wild

abstract: Face hallucination method is proposed to generate high-resolution images from low-resolution ones for better visualization. However, conventional hallucination methods are often designed for controlled settings and cannot handle varying conditions of pose, resolution degree, and blur. In this paper, we present a new method of face hallucination, which can consistently improve the resolution of face images even with large appearance variations. Our method is based on a novel network architecture called Bi-channel Convolutional Neural Network (Bi-channel CNN). It extracts robust face representations from raw input by using deep convolu-tional network, then adaptively integrates two channels of information (the raw input image and face representations) to predict the high-resolution image. Experimental results show our system outperforms the prior state-of-the-art methods.


A Framework of WEB-GIS for Urban Drainage Network System


title: Specifying Services for ITIL Service Management

abstract: The Information Technology Infrastructure Library (ITIL) is a collection of best practices for the management of IT services. ITIL helps organizations to become aware of the business value their IT services provide to internal and external stakeholders. Understanding this value is crucial to the definition of service level agreements (SLA) between an IT department and its stakeholders. However, it is not ITIL's objective to define how this value is to be elicited from stakeholders. This creates an opportunity for the use of RE methods in businesses. This paper describes the main principles of ITIL service management and illustrates how the SEAM RE method can contribute to the definition of an SLA by modeling the service provided by an IT department, the stakeholders of this service and the value the stakeholders expect from this service. A real industrial example is presented and analyzed.


title: Single-Fed Broadband Circularly Polarized Stacked Patch Antenna With Horizontally Meandered Strip for Universal UHF RFID Applications

abstract: In this paper, a horizontally meandered strip (HMS) feed technique is proposed to achieve good impedance matching and symmetrical broadside radiation patterns for a single-fed broadband circularly polarized stacked patch antenna, which is suitable for universal ultrahigh frequency (UHF) RF identification (RFID) applications. The antenna is composed of two corner truncated patches and an HMS, all of which are printed on the upper side of the FR4 substrates. One end of the HMS is connected to the main patch by a probe, while the other end is connected to an SMA connector. Simulation results are compared with the measurements, and a good agreement is obtained. The measurements show that the antenna has an impedance bandwidth (VSWR <; 1.5) of about 25.8% (758-983 MHz), a 3-dB axial ratio (AR) bandwidth of about 13.5% (838-959 MHz), and a gain level of about 8.6 dBic or larger within the 3-dB AR bandwidth. Therefore, the proposed antenna can be a good candidate for universal UHF RFID readers operating at the UHF band of 840-955 MHz. In addition, a parametric study and a design guideline of the proposed antenna are presented to provide the engineers with information for designing, modifying, and optimizing such an antenna. At last, the proposed antenna is validated in RFID system applications.


title: Learning Deep Generative Models

abstract: Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many AI related tasks, including object recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires models with deep architectures that involve many layers of nonlinear processing. ::: The aim of the thesis is to demonstrate that deep generative models that contain many layers of latent variables and millions of parameters can be learned efficiently, and that the learned high-level feature representations can be successfully applied in a wide spectrum of application domains, including visual object recognition, information retrieval, and classification and regression tasks. In addition, similar methods can be used for nonlinear dimensionality reduction. ::: The first part of the thesis focuses on analysis and applications of probabilistic generative models called Deep Belief Networks. We show that these deep hierarchical models can learn useful feature representations from a large supply of unlabeled sensory inputs. The learned high-level representations capture a lot of structure in the input data, which is useful for subsequent problem-specific tasks, such as classification, regression or information retrieval, even though these tasks are unknown when the generative model is being trained. ::: In the second part of the thesis, we introduce a new learning algorithm for a different type of hierarchical probabilistic model, which we call a Deep Boltzmann Machine. Like Deep Belief Networks, Deep Boltzmann Machines have the potential of learning internal representations that become increasingly complex at higher layers, which is a promising way of solving object and speech recognition problems. Unlike Deep Belief Networks and many existing models with deep architectures, the approximate inference procedure, in addition to a fast bottom-up pass, can incorporate top-down feedback. This allows Deep Boltzmann Machines to better propagate uncertainty about ambiguous inputs.


title: Group Preference Aggregation: A Nash Equilibrium Approach

abstract: Group-oriented services such as group recommendations aim to provide services for a group of users. For these applications, how to aggregate the preferences of different group members is the toughest yet most important problem. Inspired by game theory, in this paper, we propose to explore the idea of Nash equilibrium to simulate the selections of members in a group by a game process. Along this line, we first compute the preferences (group-dependent optimal selections) of each individual member in a given group scene, i.e., an equilibrium solution of this group, with the help of two pruning approaches. Then, to get the aggregated unitary preference of each group from all group members, we design a matrix factorization-based method which aggregates the preferences in latent space and estimates the final group preference in rating space. After obtaining the group preference, group-oriented services (e.g., group recommendation) can be directly provided. Finally, we construct extensive experiments on two real-world data sets from multiple aspects. The results clearly demonstrate the effectiveness of our method.


Machine Learning Based Ticket Classification in Issue Tracking Systems


title: Microwave dielectric spectroscopy of a single biological cell with improved sensitivity up to 40 GHz

abstract: This paper presents the sensitivity optimization of a microwave biosensor dedicated to the analysis of a single living biological cell from 40 MHz to 40 GHz, directly in its culture medium. To enhance the sensor sensitivity, different capacitive gap located in the center of the biosensor, below the cell position, have been evaluated with different beads sizes. The best capacitive and conductive contrasts have been reached for a gap width of 5 µm with beads exhibiting diameters of 10 and 20 µm, due to electromagnetic field penetration in the beads. Contrasts improvement of 40 and 60 % have been achieved with standard deviations in the order of only 4% and 6% for the capacitive and conductive contrasts respectively. This sensor therefore permits to measure single living biological cells directly in their culture medium with capacitive and conductive contrasts of 0.4 fF at 5 GHz and 85 µS at 40 GHz, and associated standard deviations estimated at 7% and 14% respectively.


title: Survey and Evaluation of Audio Fingerprinting Schemes for Mobile Query-by-Example Applications

abstract: We survey and evaluate popular audio fingerprinting schemes in a common framework with short query probes captured from cell phones. We report and discuss results important for mobile applications: Receiver Operating Characteristic (ROC) performance, size of fingerprints generated compared to size of audio probe, and transmission delay if the fingerprint data were to be transmitted over a wireless link. We hope that the evaluation in this work will guide work towards reducing latency in practical mobile audio retrieval applications.


title: A Novel Single-Diode Microwave Rectifier With a Series Band-Stop Structure

abstract: A single-diode compact microwave rectifier with a series band-stop structure is presented in this paper. The band-stop structure is connected to the diode in series to block the harmonics produced during rectifying. It replaces the cascading bandpass or low-pass filter between the input and the diode in a conventional microwave rectifier. A microwave rectifier at 2.45 GHz with an HSMS 282 Schottky diode is realized based on the proposed design method. A short-ended eighth-wavelength microstrip transmission line is applied to the rectifier in series with the diode as a band-stop structure. The short-ended microstrip transmission line presents an inductive impedance to compensate the diode capacitive impedance at fundamental frequency and turns to an open circuit to block the second harmonic for power recycling. The proposed circuit shows a novel schematic for compact microwave rectifiers. The fabricated rectifier is compact with a dimension of $0.15\lambda _{g} \times 0.2\lambda _{g}$ . A microwave-to-dc conversion efficiency of 80.9% is achieved at 20 dBm input power with a 580- $\Omega $ dc load.


title: Data Structures for Statistical Computing in Python

abstract: In this paper we are concerned with the practical issues of working with data sets common to finance, statistics, and other related fields. pandas is a new library which aims to facilitate working with these data sets and to provide a set of fundamental building blocks for implementing statistical models. We will discuss specific design issues encountered in the course of developing pandas with relevant examples and some comparisons with the R language. We conclude by discussing possible future directions for statistical computing and data analysis using Python.


title: Lossy compression on IoT big data by exploiting spatiotemporal correlation

abstract: As the volume of data generated by various deployed IoT devices increases, storing and processing IoT big data becomes a huge challenge. While compression, especially lossy ones, can drastically reduce data volume, finding an optimal balance between the volume reduction and the information loss is not an easy task given that the data collected by diverse sensors exhibit different characteristics. Motivated by this, we present a feasibility analysis of lossy compression on agricultural sensor data by comparing fidelity of reconstructed data from various signal processing algorithms and temporal difference encoding. Specifically, we evaluated five real-world sensor data from weather stations as one of major IoT applications. Our experimental results indicate that Discrete Cosine Transform (DCT) and Fast Walsh-Hadamard Transform (FWHT) generate higher compression ratios than others. In terms of information loss, Lossy Delta Encoding (LDE) significantly outperforms others nonetheless. We also observe that, as compression factor is increased, error rates for all compression algorithms also increase. However, the impact of introduced error is much severe in DCT and FWHT while LDE was able to maintain a relatively lower error rate than other methods.


Invigorating Event-driven Process Chains – Towards an integrated meta model for EPC standardization


title: Quantitative architectural analysis: a new approach to cortical mapping

abstract: Recent progress in anatomical and functional MRI has revived the demand for a reliable, topographic map of the human cerebral cortex. Till date, interpretations of specific activations found in functional imaging studies and their topographical analysis in a spatial reference system are, often, still based on classical architectonic maps. The most commonly used reference atlas is that of Brodmann and his successors, despite its severe inherent drawbacks. One obvious weakness in traditional, architectural mapping is the subjective nature of localising borders between cortical areas, by means of a purely visual, microscopical examination of histological specimens. To overcome this limitation, more objective, quantitative mapping procedures have been established in the past years. The quantification of the neocortical, laminar pattern by defining intensity line profiles across the cortical layers, has a long tradition. During the last years, this method has been extended to enable a reliable, reproducible mapping of the cortex based on image analysis and multivariate statistics. Methodological approaches to such algorithm-based, cortical mapping were published for various architectural modalities. In our contribution, principles of algorithm-based mapping are described for cyto- and receptorarchitecture. In a cytoarchitectural parcellation of the human auditory cortex, using a sliding window procedure, the classical areal pattern of the human superior temporal gyrus was modified by a replacing of Brodmann’s areas 41, 42, 22 and parts of area 21, with a novel, more detailed map. An extension and optimisation of the sliding window procedure to the specific requirements of receptorarchitectonic mapping, is also described using the macaque central sulcus and adjacent superior parietal lobule as a second, biologically independent example. Algorithm-based mapping procedures, however, are not limited to these two architectural modalities, but can be applied to all images in which a laminar cortical pattern can be detected and quantified, e.g. myeloarchitectonic and in vivo high resolution MR imaging. Defining cortical borders, based on changes in cortical lamination in high resolution, in vivo structural MR images will result in a rapid increase of our knowledge on the structural parcellation of the human cerebral cortex.


title: Collaborative Ranking: A Case Study on Entity Linking

abstract: In this paper, we present a new ranking scheme, collaborative ranking (CR). In contrast to traditional non-collaborative ranking scheme which solely relies on the strengths of isolated queries and one stand-alone ranking algorithm, the new scheme integrates the strengths from multiple collaborators of a query and the strengths from multiple ranking algorithms. We elaborate three specific forms of collaborative ranking, namely, micro collaborative ranking (MiCR), macro collaborative ranking (MaCR) and micro-macro collaborative ranking (MiMaCR). Experiments on entity linking task show that our proposed scheme is indeed effective and promising.


title: RGB-D-Based Face Reconstruction and Recognition

abstract: Most RGB-D based research focuses on gesture analysis, scene reconstruction and SLAM, but only few study its impacts on face recognition. A common yet challenging scenario considered in face recognition across pose takes a single 2D face of frontal pose as the galley and other poses as the probe set. We consider a similar scenario but with a RGB-D image pair taken at frontal pose in the gallery, only 2D images with a large scope of poses in the probe set, and study the advantage of the additional depth map on top of the regular RGB image. We formulate the 3D face reconstruction using the RGB-D image as a constrained optimization, and compare the results with different reconstruction settings. The reconstructed 3D face allows the generation of 2D face with specific poses, which can be matched with the probes. Experiments on the Biwi Kinect Head Pose Database and Eurecom Database show that the additional depth map substantially improves the cross-pose recognition performance, and the depth-based component selection also improves the recognition under occlusion and expression variation.


title: On Bitcoin as a public randomness source.

abstract: 1 Stanford University 2 Concordia University 3 Princeton University Abstract. We formalize the use of Bitcoin as a source of publiclyverifiable randomness. As a side-effect of Bitcoin’s proof-of-work-based consensus system, random values are broadcast every time new blocks are mined. We can derive strong lower bounds on the computational min-entropy in each block: currently, at least 68 bits of min-entropy are produced every 10 minutes, from which one can derive over 32 nearuniform bits using standard extractor techniques. We show that any attack on this beacon would form an attack on Bitcoin itself and hence have a monetary cost that we can bound, unlike any other construction for a public randomness beacon in the literature. In our simplest construction, we show that a lottery producing a single unbiased bit is manipulation-resistant against an attacker with a stake of less than 50 bitcoins in the output, or about US$12,000 today. Finally, we propose making the beacon output available to smart contracts and demonstrate that this simple tool enables a number of interesting applications.


title: Inter-satellite links for cubesats

abstract: Realizing inter-satellite links is a must for ensuring the success of cubesat swarm missions. Nevertheless, it has hardly been considered until now. The communication systems for cubesats have to deal with a few peculiar demands regarding consumed power, geometry and throughput. Depending on the type of application, required data rates can go up to tens of megabits per second, while power consumption and physical size are limited by the platform. The proposed communication scheme will combine power-efficient modulation and channel coding with multiple access and spread spectrum techniques, enabling the deployment of multiple satellites. Apart from this, the antenna system has to be designed such that links can be established and maintained independent of the satellites' orientation. An electrically steerable radiation pattern is achieved by placing antennas on each face of the cube. Conformal beamforming provides the system with 5 dBi gain for any desired direction of transmission, eliminating the need for attitude control. Furthermore, using planar antennas reduces the complexity of the mechanical part as they require no deployment.


title: Intel® Software Guard Extensions (Intel® SGX) Architecture for Oversubscription of Secure Memory in a Virtualized Environment

abstract: As workloads and data move to the cloud, it is essential that software writers are able to protect their applications from untrusted hardware, systems software, and co-tenants. Intel® Software Guard Extensions (SGX) enables a new mode of execution that is protected from attacks in such an environment with strong confidentiality, integrity, and replay protection guarantees. Though SGX supports memory oversubscription via paging, virtualizing the protected memory presents a significant challenge to Virtual Machine Monitor (VMM) writers and comes with a high performance overhead. This paper introduces SGX Oversubscription Extensions that add additional instructions and virtualization support to the SGX architecture so that cloud service providers can oversubscribe secure memory in a less complex and more performant manner.


title: Learning-based approximation of interconnect delay and slew in signoff timing tools

abstract: Incremental static timing analysis (iSTA) is the backbone of iterative sizing and Vt-swapping heuristics for post-layout timing recovery and leakage power reduction. Performing such analysis through available interfaces of a signoff STA tool brings efficiency and functionality limitations. Thus, an internal iSTA tool must be built that matches the signoff STA tool. A key challenge is the matching of “black-box” modeling of interconnect effects in the signoff tool, so as to match wire slew, wire delay, gate slew and gate delay on each arc of the timing graph. Previous moment-based analytical models for gate and wire slew and delay typically have large errors when compared to values from signoff STA tools. To mitigate the accumulation of these errors and preserve timing correlation, sizing tools must invoke the signoff STA tool frequently, thus incurring large runtime costs. In this work, we pursue a learning-based approach to fit analytical models of wire slew and delay to estimates from a signoff STA tool. These models can improve the accuracy of delay and slew estimations, such that the number of invocations of the signoff STA tool during sizing optimizations is significantly reduced.