title: A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching

abstract: Depth from defocus (DfD) and stereo matching are two most studied passive depth sensing schemes. The techniques are essentially complementary: DfD can robustly handle repetitive textures that are problematic for stereo matching whereas stereo matching is insensitive to defocus blurs and can handle large depth range. In this paper, we present a unified learning-based technique to conduct hybrid DfD and stereo matching. Our input is image triplets: a stereo pair and a defocused image of one of the stereo views. We first apply depth-guided light field rendering to construct a comprehensive training dataset for such hybrid sensing setups. Next, we adopt the hourglass network architecture to separately conduct depth inference from DfD and stereo. Finally, we exploit different connection methods between the two separate networks for integrating them into a unified solution to produce high fidelity 3D disparity maps. Comprehensive experiments on real and synthetic data show that our new learning-based hybrid 3D sensing technique can significantly improve accuracy and robustness in 3D reconstruction.


title: SMSCrypto: A lightweight cryptographic framework for secure SMS transmission

abstract: Despite the continuous growth in the number of smartphones around the globe, Short Message Service (SMS) still remains as one of the most popular, cheap and accessible ways of exchanging text messages using mobile phones. Nevertheless, the lack of security in SMS prevents its wide usage in sensitive contexts such as banking and health-related applications. Aiming to tackle this issue, this paper presents SMSCrypto, a framework for securing SMS-based communications in mobile phones. SMSCrypto encloses a tailored selection of lightweight cryptographic algorithms and protocols, providing encryption, authentication and signature services. The proposed framework is implemented both in Java (target at JVM-enabled platforms) and in C (for constrained SIM Card processors) languages, thus being suitable for a wide range of scenarios. In addition, the signature model adopted does not require an on-line infrastructure and the inherent overhead found in the Public Key Infrastructure (PKI) model, facilitating the development of secure SMS-based applications. We evaluate the proposed framework on a real phone and on SIM Card-comparable microcontroller.


title: A General-Purpose Tagger with Convolutional Neural Networks

abstract: We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information. The CNN tagger is robust across different tagging tasks: without task-specific tuning of hyper-parameters, it achieves state-of-the-art results in part-of-speech tagging, morphological tagging and supertagging. The CNN tagger is also robust against the out-of-vocabulary problem, it performs well on artificially unnormalized texts.


title: Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs

abstract: This book addresses the question of how far it is possible to go in knowledge representation and reasoning by representing knowledge with graphs (in the graph theory sense) and reasoning with graph operations. The authors have carefully structured the book with the first part covering basic conceptual graphs, the second developing the computational aspects, and the final section pooling the kernel extensions. An appendix summarizes the basic mathematical notions. This is the first book to provide a comprehensive view on the computational facets of conceptual graphs. The mathematical prerequisites are minimal and the material presented can be used in artificial intelligence courses at graduate level upwards.


Composite Task-Completion Dialogue System via Hierarchical Deep Reinforcement Learning.


title: Converter-free multiple-voltage scaling techniques for low-power CMOS digital design

abstract: Recent research has shown that voltage scaling is a very effective technique for low-power design. This paper describes a voltage scaling technique to minimize the power consumption of a combinational circuit. First, the converter-free multiple-voltage (CFMV) structures are proposed, including the p-type, the n-type, and the two-way CFMV structures. The CFMV structures make use of multiple supply voltages and do not require level converters. In contrast, previous works employing multiple supply voltages need level converters to prevent static currents, which may result in large power consumption. In addition, the CFMV structures group the gates with the same supply voltage in a cluster to reduce the complexity of placement and routing for the subsequent physical layout stage. Next, we formulated the problem and proposed an efficient heuristic algorithm to solve it. The heuristic algorithm has been implemented in C and experiments were performed on the ISCAS85 circuits to demonstrate the effectiveness of our approach.


title: Visual odometry and control for an omnidirectional mobile robot with a downward-facing camera

abstract: An omnidirectional Mecanum base allows for more flexible mobile manipulation. However, slipping of the Mecanum wheels results in poor dead-reckoning estimates from wheel encoders, limiting the accuracy and overall utility of this type of base. We present a system with a downward-facing camera and light ring to provide robust visual odometry estimates. We mounted the system under the robot which allows it to operate in conditions such as large crowds or low ambient lighting. We demonstrate that the visual odometry estimates are sufficient to generate closed-loop PID (Proportional Integral Derivative) and LQR (Linear Quadratic Regulator) controllers for motion control in three different scenarios: waypoint tracking, small disturbance rejection, and sideways motion. We report quantitative measurements that demonstrate superior control performance when using visual odometry compared to wheel encoders. Finally, we show that this system provides high-fidelity odometry estimates and is able to compensate for wheel slip on a four-wheeled omnidirectional mobile robot base.


title: Learning Visual Reasoning Without Strong Priors

abstract: Achieving artificial visual reasoning - the ability to answer image-related questions which require a multi-step, high-level process - is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, structured reasoning process over images from language. Standard deep learning approaches tend to exploit biases in the data rather than learn this underlying structure, while leading methods learn to visually reason successfully but are hand-crafted for reasoning. We show that a general-purpose, Conditional Batch Normalization approach achieves state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4% error rate. We outperform the next best end-to-end method (4.5%) and even methods that use extra supervision (3.1%). We probe our model to shed light on how it reasons, showing it has learned a question-dependent, multi-step process. Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively.


title: Securing wireless sensor networks: a survey

abstract: The significant advances of hardware manufacturing technology and the development of efficient software algorithms make technically and economically feasible a network composed of numerous, small, low-cost sensors using wireless communications, that is, a wireless sensor network. WSNs have attracted intensive interest from both academia and industry due to their wide application in civil and military scenarios. In hostile scenarios, it is very important to protect WSNs from malicious attacks. Due to various resource limitations and the salient features of a wireless sensor network, the security design for such networks is significantly challenging. In this article, we present a comprehensive survey of WSN security issues that were investigated by researchers in recent years and that shed light on future directions for WSN security.


Lattice Based Cryptography


title: An improved Linux firewall using a hybrid frame of netfìlter

abstract: With the steady advancement of the network technology present day, network not only brings us a conducive and productive life, and is followed by a collection of network security threats. Due to awareness about the threats the need for security has never been more important that's why it has become extremely important to protect our web servers as well as our web assets. A firewall is main security component that allows and restrict access to specific network and ports. In this research main focus is on designing strong firewall filtering rules so that detection of malicious code will be achieved to the optimal level. The proposed framework is introduced to improve performance issues, code maintenance (i.e. code duplication), scalability, for improving performance of the network traffic etc. in the dataset. In this work, we examine the Linux Netfilter/iptable, nftable firewall technology. In this paper, a new hybrid approach is proposed where Geometric efficient matching algorithm and stateless firewall optimization algorithm is merged into the code of the Linux iptables and nftables open source firewall for securing Linux web server. "Geometric Efficient Matching algorithm" GEM-iptables & nftables execution manage to filter packets-per-second on a standard system. It is efficient and practical, algorithm for firewall packet matching. While there are a number of paths that can be followed to provide a best malware detection method for firewall security, this work will be beneficial for small enterprises in terms of money and time using Netfilter/nftables. This makes it easy and simple to configure the strong firewall to solve the security related problems & detect malware using strong firewall rules to achieve optimal level.


title: Learning Differentially Private Recurrent Language Models

abstract: We demonstrate that it is possible to train large recurrent language models with user-level differential privacy guarantees with only a negligible cost in predictive accuracy. Our work builds on recent advances in the training of deep networks on user-partitioned data and privacy accounting for stochastic gradient descent. In particular, we add user-level privacy protection to the federated averaging algorithm, which makes "large step" updates from user-level data. Our work demonstrates that given a dataset with a sufficiently large number of users (a requirement easily met by even small internet-scale datasets), achieving differential privacy comes at the cost of increased computation, rather than in decreased utility as in most prior work. We find that our private LSTM language models are quantitatively and qualitatively similar to un-noised models when trained on a large dataset.


title: The effect of flow experience on mobile SNS users' loyalty

abstract: Purpose – The purpose of this research is to examine the effect of flow experience on mobile social networking service (SNS) users' loyalty.Design/methodology/approach – Based on 305 valid responses collected from a survey questionnaire, structural equation modeling (SEM) technology was employed to examine the research model.Findings – The results show that both information quality and system quality significantly affect users' trust and flow experiences, which further determine their loyalty. The results indicate that flow experience is the strongest determinant of users' loyalty.Practical implications – Mobile SNS providers need to consider user experience when seeking users' loyalty. They should enhance information quality and system quality in order to improve user trust and flow experience.Originality/value – Although much research has been conducted to explore the effects of extrinsic motivations, such as perceived usefulness, on mobile commerce user behavior, the effect of intrinsic motivations, su...


title: Recommender systems inspired by the structure of quantum theory

abstract: Physicists use quantum models to describe the behavior of physical systems. Quantum models owe their success to their interpretability, to their relation to probabilistic models (quantization of classical models) and to their high predictive power. Beyond physics, these properties are valuable in general data science. This motivates the use of quantum models to analyze general nonphysical datasets. Here we provide both empirical and theoretical insights into the application of quantum models in data science. In the theoretical part of this paper, we firstly show that quantum models can be exponentially more efficient than probabilistic models because there exist datasets that admit low-dimensional quantum models and only exponentially high-dimensional probabilistic models. Secondly, we explain in what sense quantum models realize a useful relaxation of compressed probabilistic models. Thirdly, we show that sparse datasets admit low-dimensional quantum models and finally, we introduce a method to compute hierarchical orderings of properties of users (e.g., personality traits) and items (e.g., genres of movies). In the empirical part of the paper, we evaluate quantum models in item recommendation and observe that the predictive power of quantum-inspired recommender systems can compete with state-of-the-art recommender systems like SVD++ and PureSVD. Furthermore, we make use of the interpretability of quantum models by computing hierarchical orderings of properties of users and items. This work establishes a connection between data science (item recommendation), information theory (communication complexity), mathematical programming (positive semidefinite factorizations) and physics (quantum models).


title: Cloud Computing education strategies

abstract: Cloud Computing is changing the services consumption and delivery platform as well as the way businesses and users interact with IT resources. It represents a major conceptual shift that introduces new elements in programming models and development environments that are not present in traditional technologies. The evolution of Cloud Computing motivates teaching Cloud Computing to computer science senior students and graduate students so that they can gain broad exposure to the main body of knowledge of Cloud Computing and get prepared for occupations in industry. There is thus a strong need for having a Cloud Computing education course that (i) has a broad coverage of different roles interacting with a cloud; and (ii) leverages Cloud Computing concepts, technology and architecture topics at both introductory and advanced level. In this paper, we describe the demand for understanding the impact of Cloud Computing in computer science higher education. We propose education strategies for teaching Cloud Computing, including key knowledge areas for an enduring Cloud Computing course.


title: Service Fairness and Customer Satisfaction in Internet Bank: From a Trust and Perceived Customer Value Perspective

abstract: Recent research has found that fairness plays a key role in customer satisfaction. Fairness in an online context and how it influences customer satisfaction, however, has yet been investigated. This research examines satisfaction from a fairness lens and explores the mediators of fairness to satisfaction in the internet bank context. 131 surveys were analyzed and results show that in internet bank, fairness that includes distributive fairness, procedural fairness and informational fairness is positively related to customer satisfaction. Trust and value are identified as two key mediators of fairness to customer satisfaction.


title: Representing Specialized Events with FrameBase

abstract: Events of various sorts make up an important subset of the entities relevant not only in knowledge representation but also in natural language processing and numerous other fields and tasks. How to represent these in a homogeneous yet expressive, extensive, and extensible way remains a challenge. In this paper, we propose an approach based on FrameBase, a broad RDFS-based schema consisting of frames and roles. The concept of a frame, which is a very general one, can be considered as subsuming existing definitions of events. This ensures a broad coverage and a uniform representation of various kinds of events, thus bearing the potential to serve as a unified event model. We show how FrameBase can represent events from several different sources and domains. These include events from a specific taxonomy related to organized crime, events captured using schema.org, and events from DBpedia.


title: Wind Estimation and Airspeed Calibration using a UAV with a Single-Antenna GPS Receiver and Pitot Tube

abstract: This paper proposes a method that uses an aircraft with a single-antenna GPS receiver and Pitot tube to estimate wind speed and direction and to calibrate the airspeed. This sensor combination alone does not determine the true attitude of the aircraft, so the wind parameters cannot be obtained directly from the measurements. However, if the aircraft flies at different headings, such as in banking turns or circle maneuvers, the wind magnitude and direction can be estimated from the geometrical relation between the wind and the measurements. An extended Kalman filter (EKF) is applied to estimate wind parameters. The EKF can also estimate the scaling factor used to convert dynamic pressure to airspeed. This is useful for the operation of small unmanned aerial vehicles (UAVs) because of difficulty in determining the airspeed scaling factor of a low-cost UAV. Simulations are performed for a constant 2-D wind. To test the effectiveness of the proposed method, flight tests of a small UAV are conducted. Simulations and flight test results show that the proposed method is effective.


title: Efficient sparse matrix-vector multiplication on x86-based many-core processors

abstract: Sparse matrix-vector multiplication (SpMV) is an important kernel in many scientific applications and is known to be memory bandwidth limited. On modern processors with wide SIMD and large numbers of cores, we identify and address several bottlenecks which may limit performance even before memory bandwidth: (a) low SIMD efficiency due to sparsity, (b) overhead due to irregular memory accesses, and (c) load-imbalance due to non-uniform matrix structures. We describe an efficient implementation of SpMV on the IntelR Xeon PhiTM Coprocessor, codenamed Knights Corner (KNC), that addresses the above challenges. Our implementation exploits the salient architectural features of KNC, such as large caches and hardware support for irregular memory accesses. By using a specialized data structure with careful load balancing, we attain performance on average close to 90% of KNC's achievable memory bandwidth on a diverse set of sparse matrices. Furthermore, we demonstrate that our implementation is 3.52x and 1.32x faster, respectively, than the best available implementations on dual IntelR XeonR Processor E5-2680 and the NVIDIA Tesla K20X architecture.


title: A Smart Gateway Framework for IOT Services

abstract: With various connectivity and communication protocols, it is conceivable that all raw data from sensor devices can be made available in the Internet. However, this imposes a significant intricacy when dealing with the amount of information and the management of the devices. To alleviate the problem, this paper presents a design and implementation of a prototype IoT gateway framework that exposes high-level abstraction for accessing and manipulating the devices of an ambient environment and the data that they produce. Along with integrating and consolidating edge devices, the gateway framework enables the construction of high-level knowledge to represent the property of the environment and to support privacy protection. Thus, intelligent services, created with reasoning rules or event triggers, can be adaptable to application environment. To illustrate the capability of the IoT gateway framework, an application scenario and its performance evaluation are included in the paper.


title: A Meta-Surface Antenna Array Decoupling (MAAD) Method for Mutual Coupling Reduction in a MIMO Antenna System

abstract: In this paper, a method to reduce the inevitable mutual coupling between antennas in an extremely closely spaced two-element MIMO antenna array is proposed. A suspended meta-surface composed periodic square split ring resonators (SRRs) is placed above the antenna array for decoupling. The meta-surface is equivalent to a negative permeability medium, along which wave propagation is rejected. By properly designing the rejection frequency band of the SRR unit, the mutual coupling between the antenna elements in the MIMO antenna system can be significantly reduced. Two prototypes of microstrip antenna arrays at 5.8 GHz band with and without the metasurface have been fabricated and measured. The matching bandwidths of antennas with reflection coefficient smaller than −15 dB for the arrays without and with the metasurface are 360 MHz and 900 MHz respectively. Using the meta-surface, the isolation between elements is increased from around 8 dB to more than 27 dB within the band of interest. Meanwhile, the total efficiency and peak gain of each element, the envelope correlation coefficient (ECC) between the two elements are also improved by considerable amounts. All the results demonstrate that the proposed method is very efficient for enhancing the performance of MIMO antenna arrays.


title: Epigenome-Wide Meta-Analysis of Methylation in Children Related to Prenatal NO2 Air Pollution Exposure

abstract: Background: Prenatal exposure to air pollution is considered to be associated with adverse effects on child health. This may partly be mediated by mechanisms related to DNA methylation. Objectives: We investigated associations between exposure to air pollution, using nitrogen dioxide (NO2) as marker, and epigenome-wide cord blood DNA methylation. Methods: We meta-analyzed the associations between NO2 exposure at residential addresses during pregnancy and cord blood DNA methylation (Illumina 450K) in four European and North American studies (n = 1,508) with subsequent look-up analyses in children ages 4 (n = 733) and 8 (n = 786) years. Additionally, we applied a literature-based candidate approach for antioxidant and anti-inflammatory genes. To assess influence of exposure at the transcriptomics level, we related mRNA expression in blood cells to NO2 exposure in 4- (n = 111) and 16-year-olds (n = 239). Results: We found epigenome-wide significant associations [false discovery rate (FDR) p < 0.05] between maternal NO2 exposure during pregnancy and DNA methylation in newborns for 3 CpG sites in mitochondria-related genes: cg12283362 (LONP1), cg24172570 (3.8 kbp upstream of HIBADH), and cg08973675 (SLC25A28). The associations with cg08973675 methylation were alsosignificant in the older children. Further analysis of antioxidant and anti-inflammatory genes revealed differentially methylated CpGs in CAT and TPO in newborns (FDR p < 0.05). NO2 exposure at the time of biosampling in childhood had a significant impact on CAT and TPO expression. Conclusions: NO2 exposure during pregnancy was associated with differential offspring DNA methylation in mitochondria-related genes. Exposure to NO2 was also linked to differential methylation as well as expression of genes involved in antioxidant defense pathways


Community information management


title: A new intuitionism: Meaning, memory, and development in Fuzzy-Trace Theory

abstract: Combining meaning, memory, and development, the perennially popular topic of intuition can be approached in a new way. Fuzzy-trace theory integrates these topics by distinguishing between meaning-based gist representations, which support fuzzy (yet advanced) intuition, and superficial verbatim representations of information, which support precise analysis. Here, I review the counterintuitive findings that led to the development of the theory and its most recent extensions to the neuroscience of risky decision making. These findings include memory interference (worse verbatim memory is associated with better reasoning); nonnumerical framing (framing effects increase when numbers are deleted from decision problems); developmental decreases in gray matter and increases in brain connectivity; developmental reversals in memory, judgment, and decision making (heuristics and biases based on gist increase from childhood to adulthood, challenging conceptions of rationality); and selective attention effects that provide critical tests comparing fuzzy-trace theory, expected utility theory, and its variants (e.g., prospect theory). Surprising implications for judgment and decision making in real life are also discussed, notably, that adaptive decision making relies mainly on gist-based intuition in law, medicine, and public health.


title: Predicting information seeker satisfaction in community question answering

abstract: Question answering communities such as Naver and Yahoo! Answers have emerged as popular, and often effective, means of information seeking on the web. By posting questions for other participants to answer, information seekers can obtain specific answers to their questions. Users of popular portals such as Yahoo! Answers already have submitted millions of questions and received hundreds of millions of answers from other participants. However, it may also take hours --and sometime days-- until a satisfactory answer is posted. In this paper we introduce the problem of predicting information seeker satisfaction in collaborative question answering communities, where we attempt to predict whether a question author will be satisfied with the answers submitted by the community participants. We present a general prediction model, and develop a variety of content, structure, and community-focused features for this task. Our experimental results, obtained from a largescale evaluation over thousands of real questions and user ratings, demonstrate the feasibility of modeling and predicting asker satisfaction. We complement our results with a thorough investigation of the interactions and information seeking patterns in question answering communities that correlate with information seeker satisfaction. Our models and predictions could be useful for a variety of applications such as user intent inference, answer ranking, interface design, and query suggestion and routing.


title: Broadband effect of linear tapered transitions between probe pads and GCPW signal lines on-chip

abstract: To effect a low-reflection interconnect between GSG probe pads and on-chip GCPW, a linear taper between the signal pad and the GCPW signal line is often included. This work evaluates, both in parametric simulation and experimentation, the effect of this taper shape to the input reflection in the band 1 – 110 GHz. It is found that, although longer tapers offer some advantage below 30 GHz, the taper ultimately impedes the input reflection of the interconnect.


title: A tutorial on the cross-entropy method

abstract: The cross-entropy (CE) method is a new generic approach to combinatorial and multi-extremal optimization and rare event simulation. The purpose of this tutorial is to give a gentle introduction to the CE method. We present the CE methodology, the basic algorithm and its modiﬁcations, and discuss applications in combinatorial optimization and machine learning.


E-Learning Recommender System Based on Collaborative Filtering and Ontology


title: The Next Frontier for Power/Performance Benchmarking: Energy Efficiency of Storage Subsystems

abstract: The increasing concern of energy usage in datacenters has drastically changed how the IT industry evaluates servers. The energy conscious selection of storage subsystems is the next logical step. This paper first quantifies the possible energy savings of utilizing modern storage subsystems by identifying inherent energy characteristics of next generation disk IO subsystems. Additionally, the power consumptions of a variety of workload patterns is demonstrated.


title: Study of impact of BTI's local layout effect including recovery effect on various standard-cells in 10nm FinFET

abstract: This paper presents an analysis methodology of the impact of Local Layout Effect (LLE) of bias temperature instability (BTI) on logic circuits by measuring Ring-Oscillators (RO) consist of many kinds of standard cells and shows its measurement result in a 10nm FinFET process. The measured delay degradations of all ROs are well correlated with estimated one without considering LLE of BTI and its maximum error rates is −16% and +13%. The LLE on BTI recovery effect is also evaluated and there is no obvious standard cell type dependency. This analysis would be useful for more accurate guideline to design BTI guard-band on logic circuits for high reliability required applications.


title: A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations

abstract: Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards ~100% sensitivity at the cost of high FP levels (~40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work.


title: An industrial case study of implementing software risk management

abstract: Explicit risk management is gaining ground in industrial software development projects. However, there are few empirical studies that investigate the transfer of explicit risk management into industry, the adequacy of the risk management approaches to the constraints of industrial contexts, or their cost-benefit. This paper presents results from a case study that introduced a systematic risk management method, namely the Riskit method, into a large German telecommunication company. The objective of the case study was (1) to analyze the usefulness and adequacy of the Riskit method and (2) to analyze the cost-benefit of the Riskit method in this industrial context. The results of (1) also aimed at improvement and customization of the Riskit method. Moreover, we compare our findings with results of previous case studies to obtain more generalized conclusions on the Riskit method. Our results showed that the Riskit method is practical, adds value to the project, and that its key concepts are understood and usable in practice. Additionally, many lessons learned are reported that are useful for the general audience who wants to transfer risk management into new projects.


title: Software effort estimation based on open source projects: Case study of Github

abstract: Abstract Context Managers usually want to pre-estimate the effort of a new project for reasonably dividing their limited resources. In reality, it is common practice to train a prediction model based on effort datasets to predict the effort required by a project. Sufficient data is the basis for training a good estimator, yet most of the data owners are unwilling to share their closed source project (CSP) effort data due to the privacy concerns, which means that we can only obtain a small number of effort data. Effort estimator built on the limited data usually cannot satisfy the practical requirement. Objective We aim to provide a method which can be used to collect sufficient data for solving the problem of lack of training data when building an effort estimation model. Method We propose to mine GitHub to collect sufficient and diverse real-life effort data for effort estimation. Specifically, we first demonstrate the feasibility of our cost metrics (including functional point analysis and personnel factors). In particular, we design a quantitative method for evaluating the personnel metrics based on GitHub data. Then we design a samples incremental approach based on AdaBoost and Classification And Regression Tree (ABCART) to make the collected dataset owns dynamic expansion capability. Results Experimental results on the collected dataset show that: (1) the personnel factor is helpful for improving the performance of the effort estimation. (2) the proposed ABCART algorithm can increase the samples of the collected dataset online. (3) the estimators built on the collected data can achieve comparable performance with those of the estimators which built on existing effort datasets. Conclusions Effort estimation based on Open Source Project (OSP) is an effective way for getting the effort required by a new project, especially for the case of lacking training data.


title: Inverted Alignments for End-to-End Automatic Speech Recognition

abstract: In this paper, we propose an inverted alignment approach for sequence classification systems like automatic speech recognition (ASR) that naturally incorporates discriminative, artificial-neural-network-based label distributions. Instead of aligning each input frame to a state label as in the standard hidden Markov model (HMM) derivation, we propose to inversely align each element of an HMM state label sequence to a segment-wise encoding of several consecutive input frames. This enables an integrated discriminative model that can be trained end-to-end from scratch or starting from an existing alignment path. The approach does not assume the usual decomposition into a separate (generative) acoustic model and a language model, and allows for a variety of model assumptions, including statistical variants of attention. Following our initial paper with proof-of-concept experiments on handwriting recognition, the focus of this paper was the investigation of integrated training and an inverted decoding approach, whereas the acoustic modeling still remains largely similar to standard hybrid modeling. We provide experiments on the CHiME-4 noisy ASR task. Our results show that we can reach competitive results with inverted alignment and decoding strategies.


title: GMAD: Graph-based Malware Activity Detection by DNS traffic analysis

abstract: Malicious activities on the Internet are one of the most dangerous threats to Internet users and organizations. Malicious software controlled remotely is addressed as one of the most critical methods for executing the malicious activities. Since blocking domain names for command and control (C&C) of the malwares by analyzing their Domain Name System (DNS) activities has been the most effective and practical countermeasure, attackers attempt to hide their malwares by adopting several evasion techniques, such as client sub-grouping and domain flux on DNS activities. A common feature of the recently developed evasion techniques is the utilization of multiple domain names for render malware DNS activities temporally and spatially more complex. In contrast to analyzing the DNS activities for a single domain name, detecting the malicious DNS activities for multiple domain names is not a simple task. The DNS activities of malware that uses multiple domain names, termed multi-domain malware, are sparser and less synchronized with respect to space and time. In this paper, we introduce a malware activity detection mechanism, GMAD: Graph-based Malware Activity Detection that utilizes a sequence of DNS queries in order to achieve robustness against evasion techniques. GMAD uses a graph termed Domain Name Travel Graph which expresses DNS query sequences to detect infected clients and malicious domain names. In addition to detecting malware C&C domain names, GMAD detects malicious DNS activities such as blacklist checking and fake DNS querying. To detect malicious domain names utilized to malware activities, GMAD applies domain name clustering using the graph structure and determines malicious clusters by referring to public blacklists. Through experiments with four sets of DNS traffic captured in two ISP networks in the U.S. and South Korea, we show that GMAD detected thousands of malicious domain names that had neither been blacklisted nor detected through group activity of DNS clients. In a detection accuracy evaluation, GMAD showed an accuracy rate higher than 99% on average, with a higher than 90% precision and lower than 0.5% false positive rate. It is shown that the proposed method is effective for detecting multi-domain malware activities irrespective of evasion techniques.


title: VISA: a visual sentiment analysis system

abstract: Sentiment plays a critical role in many information-centric business scenarios. The opinion mining methods proposed in the recent decade have formed a solid foundation to investigate the sentiment analysis tasks, but are often too complicated and scattered to serve the needs of real customers. We introduce the VISA system in this paper, which applies the visualization technology to synthesize the sentiment analysis results and present to the end user in an interactive manner. VISA builds on the generic sentiment tuple based data model and consumes the different facets of sentiment data with coordinated multiple views, hence is scalable to work with most of existing sentiment analysis engines on various application domains. We showcase the usage of VISA in a real world example and demonstrate the system's effectiveness through the user trail in finding an appropriate hotel for his family trip.


title: Towards smart city education

abstract: Sustainability has been an important topic in UK schools for some time, most notably since the Sustainable School Strategy was proposed by the UK Department for Education (DFES) in 2006. However, as smart city technologies emerge and show real promise in contributing to a more sustainable future, it is becoming apparent that new skills for working with the big urban data sets that drive these innovations must be taught to upcoming generations to ensure that they can be active smart city citizens. Current practice within schools is to distribute teaching of different aspects of data skills across the curriculum. We ask the question how can data skills be taught using a more unified and practical approach, which facilitates application of skills in genuine, smart city contexts. We propose to use Urban Data Games to set a context for learning, and demonstrating, practical application of skills for handling large complex data sets. This paper focuses on an Appathon challenge, which will shortly be trialled in a Milton Keynes school, in which participants are tasked to design a novel App from real satellite data after first learning and applying data skills to data about home energy consumption.


title: Reduced Memory Region Based Deep Convolutional Neural Network Detection

abstract: Accurate pedestrian detection has a primary role in automotive safety: for example, by issuing warnings to the driver or acting actively on cars brakes, it helps decreasing the probability of injuries and human fatalities. In order to achieve very high accuracy, recent pedestrian detectors have been based on Convolutional Neural Networks (CNN). Unfortunately, such approaches require vast amounts of computational power and memory, preventing efficient implementations on embedded systems. This work proposes a CNN-based detector, adapting a general-purpose convolutional network to the task at hand. By thoroughly analyzing and optimizing each step of the detection pipeline, we develop an architecture that outperforms methods based on traditional image features and achieves an accuracy close to the state-of-the-art while having low computational complexity. Furthermore, the model is compressed in order to fit the tight constrains of low power devices with a limited amount of embedded memory available. This paper makes two main contributions: (1) it proves that a region based deep neural network can be finely tuned to achieve adequate accuracy for pedestrian detection (2) it achieves a very low memory usage without reducing detection accuracy on the Caltech Pedestrian dataset.


title: Mining User Comment Activity for Detecting Forum Spammers in YouTube

abstract: Research shows that comment spamming (comments which are unsolicited, unrelated, abusive, hateful, commercial advertisements etc) in online discussion forums has become a common phenomenon in Web 2.0 applications and there is a strong need to counter or combat comment spamming. We present a method to automatically detect comment spammer in YouTube (largest and a popular video sharing website) forums. The proposed technique is based on mining comment activity log of a user and extracting patterns (such as time interval between subsequent comments, presence of exactly same comment across multiple unrelated videos) indicating spam behavior. We perform empirical analysis on data crawled from YouTube and demonstrate that the proposed method is effective for the task of comment spammer detection.


title: Entity Disambiguation with Freebase

abstract: Entity disambiguation with a knowledge base becomes increasingly popular in the NLP community. In this paper, we employ Freebase as the knowledge base, which contains significantly more entities than Wikipedia and others. While huge in size, Freebase lacks context for most entities, such as the descriptive text and hyperlinks in Wikipedia, which are useful for disambiguation. Instead, we leverage two features of Freebase, namely the naturally disambiguated mention phrases (aka aliases) and the rich taxonomy, to perform disambiguation in an iterative manner. Specifically, we explore both generative and discriminative models for each iteration. Experiments on 2, 430, 707 English sentences and 33, 743 Freebase entities show the effectiveness of the two features, where 90% accuracy can be reached without any labeled data. We also show that discriminative models with proposed split training strategy is robust against over fitting problem, and constantly outperforms the generative ones.


title: Encrypted Data Management with Deduplication in Cloud Computing

abstract: Cloud computing plays an important role in supporting data storage, processing, and management in the Internet of Things (IoT). To preserve cloud data confidentiality and user privacy, cloud data are often stored in an encrypted form. However, duplicated data that are encrypted under different encryption schemes could be stored in the cloud, which greatly decreases the utilization rate of storage resources, especially for big data. Several data deduplication schemes have recently been proposed. However, most of them suffer from security weakness and lack of flexibility to support secure data access control. Therefore, few can be deployed in practice. This article proposes a scheme based on attribute-based encryption (ABE) to deduplicate encrypted data stored in the cloud while also supporting secure data access control. The authors evaluate the scheme's performance based on analysis and implementation. Results show the efficiency, effectiveness, and scalability of the scheme for potential practical deployment.


title: FIST: Fast Industrial-Strength Triangulation of Polygons

abstract: We discuss a triangulation algorithm that is based on repeatedly clipping ears of a polygon. The main focus of our work was on designing and engineering an algorithm that is (1) completely reliable, (2) easy to implement, and (3) fast in practice. The algorithm was implemented in ANSI C, based on floating-point arithmetic. Due to a series of heuristics that get applied as a back-up for the standard ear-clipping process if the code detects deficiencies in the input polygon, our triangulation code can handle any type of polygonal input data, be it simple or not. Based on our implementation we report on different strategies (geometric hashing, bounding-volume trees) for speeding up the ear-clipping process in practice. The code has been tuned accordingly, and cpu-time statistics document that it tends to be faster than other popular triangulation codes. All engineering details that ensure the reliability and efficiency of the triangulation code are described in full detail. We also report experimental data on how different strategies for avoiding sliver triangles affect the cpu-time consumption of the algorithm. Our code, named FIST as an acronym for fast industrial-strength triangulation, forms the core of a package for triangulating the faces of three-dimensional polyhedra, and it has been successfully incorporated into several industrial graphics packages, including an implementation for Java 3D by Sun Microsystems.


title: Fidelity of soft nano-imprint lithographic replication of polymer masters fabricated by two-photon polymerization

abstract: Using modern laser fabrication techniques such as two-photon polymerisation (2PP), complex 3D and planar structures with the resolutions down to 100 nm can be achieved. Advantages of 2PP can be exploited by combining it with the nano-imprint lithography (NIL). We report on the fabrication of masters by 2PP and its replicas by the soft NIL technique using poly(dimethylsiloxane) (PDMS). With PDMS stamps, very reproducible replicas of the master can be created. The stamp surface quality can be better than that of the master due to hydrostatic pressure during the stamp fabrication. Putting the stamp under stress allows the fabrication of structures with controllable reduction of the structural dimensions in one direction compared to the original master. Experimental investigations of the shape transfer fidelity and surface roughness of the replicas are presented.


title: Big data process analytics for continuous process improvement in manufacturing

abstract: One of the most important challenges in manufacturing is the continuous process improvement that requires new insights about the behavior/quality control of processes in order to understand the optimization/improvement potential. The paper elaborates on usage of big data-driven clustering for an efficient discovering of real-time unusualities in the process and their route-cause analysis. Our approach extends traditional clustering algorithms (like k-Means) with methods for better understanding the nature of clusters and provides a very efficient big data realization. We argue that this approach paves the way for a new generation of quality management tools based on big data analytics that will extend traditional statistical process control and empower Lean Six Sigma through big data processing. The proposed approach has been applied for improving process control in Whirlpool (washing machine tests, factory in Italy) and we present the most important finding from the evaluation study.


title: Confidence Intervals Permit, but Do Not Guarantee, Better Inference than Statistical Significance Testing

abstract: A statistically significant result, and a non-significant result may differ little, although significance status may tempt an interpretation of difference. Two studies are reported that compared interpretation of such results presented using null hypothesis significance testing (NHST), or confidence intervals (CIs). Authors of articles published in psychology, behavioural neuroscience, and medical journals were asked, via email, to interpret two fictitious studies that found similar results, one statistically significant, and the other non-significant. Responses from 330 authors varied greatly, but interpretation was generally poor, whether results were presented as CIs or using NHST. However, when interpreting CIs respondents who mentioned NHST were 60% likely to conclude, unjustifiably, the two results conflicted, whereas those who interpreted CIs without reference to NHST were 95% likely to conclude, justifiably, the two results were consistent. Findings were generally similar for all three disciplines. An email survey of academic psychologists confirmed that CIs elicit better interpretations if NHST is not invoked. Improved statistical inference can result from encouragement of meta-analytic thinking and use of CIs but, for full benefit, such highly desirable statistical reform requires also that researchers interpret CIs without recourse to NHST.


Mechanical Design of a Rover for Mobile Manipulation in Uneven Terrain in the Context of the SpaceBot Cup


title: A smart prepaid energy metering system to control electricity theft

abstract: Power utilities in different countries especially in the developing ones are incurring huge losses due to electricity theft. This paper proposes a prepaid energy metering system to control electricity theft. In this system a smart energy meter is installed in every consumer unit and a server is maintained at the service provider side. Both the meter and the server are equipped with GSM module which facilitates bidirectional communication between the two ends using the existing GSM infrastructure. Consumers can easily recharge their energy meter by sending a PIN number hidden in a scratch card to the server using SMS. This paper presents some measures to control meter bypassing and tampering. The bidirectional GSM communication using SMS ensures the effectiveness of these measures. Pilferage of electricity can be substantially reduced by incorporating the proposed measures along with the prepaid metering scheme. Legal actions against dishonest consumers can also be taken in this system.


title: A Frequency- and Polarization-Reconfigurable Stub-Loaded Microstrip Patch Antenna

abstract: A stub-loaded microstrip patch antenna with reconfigurability in both frequency and polarization is presented. Using 12 varactors with two independent voltages, reconfigurability is achieved in a fractional bandwidth of around 40% while allowing selection between circular polarization (CP) with both rotating senses and linear polarization (LP). The design is optimized based on an analytical model, which significantly speeds up the process while yielding reasonably accurate predictions. For illustration of the concept, an antenna is designed, optimized, and manufactured for reconfigurable operation in the 2.4–3.6 GHz frequency range. A good agreement between simulations and measurements is obtained which validates the proposed method. A full reconfigurability is demonstrated in the operation band with the ability to both tune the antenna to a given frequency and select a polarization state among left-hand or right-hand CP or various states of LP.


title: A sock puppet detection algorithm on virtual spaces

abstract: On virtual spaces, some individuals use multiple usernames or copycat/forge other users (usually called ''sock puppet'') to communicate with others. Those sock puppets are fake identities through which members of Internet community praise or create the illusion of support for the product or one's work, pretending to be a different person. A fundamental problem is how to identify these sock puppets. In this paper, we propose a sock puppet detection algorithm which combines authorship-identification techniques and link analysis. Firstly, we propose an interesting social network model in which links between two IDs are built if they have similar attitudes to most topics that both of them participate in; then, the edges are pruned according a hypothesis test, which consider the impact of their writing styles; finally, the link-based community detection for pruned network is performed. Compared to traditional methods, our approach has three advantages: (1) it conforms to the practical meanings of sock puppet community; (2) it can be applied in online situation; (3) it increases the efficiency of link analysis. In the experimental work, we evaluate our method using real datasets and compared our approach with several previous methods; the results have proved above advantages.


Metaphors in User-Interfaces Design


title: Evolving memory-augmented neural architecture for deep memory problems

abstract: In this paper, we present a new memory-augmented neural network called Gated Recurrent Unit with Memory Block (GRU-MB). Our architecture builds on the gated neural architecture of a Gated Recurrent Unit (GRU) and integrates an external memory block, similar to a Neural Turing Machine (NTM). GRU-MB interacts with the memory block using independent read and write gates that serve to decouple the memory from the central feedforward operation. This allows for regimented memory access and update, administering our network the ability to choose when to read from memory, update it, or simply ignore it. This capacity to act in detachment allows the network to shield the memory from noise and other distractions, while simultaneously using it to effectively retain and propagate information over an extended period of time. We evolve GRU-MB using neuroevolution and perform experiments on two different deep memory tasks. Results demonstrate that GRU-MB performs significantly faster and more accurately than traditional memory-based methods, and is robust to dramatic increases in the depth of these tasks.


title: Designing Fuzzy Logic Controllers for ROS-Based Multirotors

abstract: This chapter presents a tutorial on using an open-source ROS package for implementing control systems based on Fuzzy Logic. Such a package has been created to facilitate the development of fuzzy control systems along with ROS technology and infrastructure. A step-by-step tutorial discusses how to develop a set of distributed and interconnected fuzzy controllers using the proposed ROS package. A fuzzy control system that controls the movement of an unmanned multirotor (specifically a hexacopter) is presented as case study. The behavior of this control system is demonstrated by means of a commercial robotics simulation environment named V-REP. One scenario is used to illustrate the fuzzy control system steering the movement of a virtual hexacopter carrying an attached loose payload, i.e. such a loose payload forms a pendulum. In this case study, one can see the hexacopter flight after receiving commands to fly to distinct positions within the scenario. It is important to highlight that, in order to be able to perform this tutorial, the reader must use ROS Indigo Igloo and V-REP PRO EDU version V3.3.0 both running on Ubuntu 14.04.4 LTS.


Graphs for Malware Detection : The Next Frontier


title: A Framework for Real-time Path Planning in Changing Environments

abstract: We present a new method for generating collision-free paths for robots operating in changing environments. Our approach is closely related to recent probabilistic roadmap approaches. These planners use preprocessing and query stages, and are aimed at planning many times in the same environment. In contrast, our preprocessing stage creates a representation of the configuration space that can be easily modified in real time to account for changes in the environment. As with previous approaches, we begin by constructing a graph that represents a roadmap in the configuration space, but we do not construct this graph for a specific workspace. Instead, we construct the graph for an obstacle-free workspace, and encode the mapping from workspace cells to nodes and arcs in the graph. When the environment changes, this mapping is used to make the appropriate modifications to the graph, and plans can be generated by searching the modified graph. ::: As part of our presentation, we discuss the construction of the roadmap, including how samples of the configuration space may be generated and how to connect the samples to form the roadmap. We then discuss the mapping from the workspace to the roadmap, explaining efficient techniques for its generation and representation. We continue with some robustness measures and how these may be used to enhance the robustness of the roadmap. We then present an extension of our method for mobile robots. Finally, we evaluate an implementation of our approach for serial-link manipulators with up to 20 joints.


title: Multi-source heterogeneous data fusion

abstract: As the exponential growth of data in internet era, there comes the big data era. Big data fusion creates huge values that makes it a research hotspot. However, in big data era, data shows characters of large volume, velocity, veracity and especially variety which is also called heterogeneity. Multiple different sources of data lead to data heterogeneity. Multi-source heterogeneous data brings opportunities and challenges to big data fusion. This paper introduces big data fusion and methods for heterogeneous data fusion, especially focus on the application of deep learning methods in multi-source heterogeneous data fusion. Challenges of dealing with multi-source heterogeneous data fusion is also discussed.


title: Comparison and combination of ear and face images in appearance-based biometrics

abstract: Researchers have suggested that the ear may have advantages over the face for biometric recognition. Our previous experiments with ear and face recognition, using the standard principal component analysis approach, showed lower recognition performance using ear images. We report results of similar experiments on larger data sets that are more rigorously controlled for relative quality of face and ear images. We find that recognition performance is not significantly different between the face and the ear, for example, 70.5 percent versus 71.6 percent, respectively, in one experiment. We also find that multimodal recognition using both the ear and face results in statistically significant improvement over either individual biometric, for example, 90.9 percent in the analogous experiment.


title: Development of a distributed firewall using software defined networking technology

abstract: Software defined networking (SDN) presents a new network architecture that separates the control logic of a network from its physical infrastructure. This allows for easy programmable networks without having to manually configure every network device individually. However, there are not much studies on security applications for SDN based networks. Hence, the goal of this work is to explore security possibilities by focusing on the development of a firewall prototype that maximizes the advantages of SDN. By building around the features of OpenFlow, an open SDN standard, a distributed flow-based firewall prototype was developed and tested on a simulated network through Mininet. The prototype was tested to show full functionality through ping tests in a distributed configuration without causing any delays in terms of latency.


title: Semantics and implementation of continuous sliding window queries over data streams

abstract: In recent years the processing of continuous queries over potentially infinite data streams has attracted a lot of research attention. We observed that the majority of work addresses individual stream operations and system-related issues rather than the development of a general-purpose basis for stream processing systems. Furthermore, example continuous queries are often formulated in some declarative query language without specifying the underlying semantics precisely enough. To overcome these deficiencies, this article presents a consistent and powerful operator algebra for data streams which ensures that continuous queries have well-defined, deterministic results. In analogy to traditional database systems, we distinguish between a logical and a physical operator algebra. While the logical algebra specifies the semantics of the individual operators in a descriptive but concrete way over temporal multisets, the physical algebra provides efficient implementations in the form of stream-to-stream operators. By adapting and enhancing research from temporal databases to meet the challenging requirements in streaming applications, we are able to carry over the conventional transformation rules from relational databases to stream processing. For this reason, our approach not only makes it possible to express continuous queries with a sound semantics, but also provides a solid foundation for query optimization, one of the major research topics in the stream community. Since this article seamlessly explains the steps from query formulation to query execution, it outlines the innovative features and operational functionality implemented in our state-of-the-art stream processing infrastructure.


title: Toward robotic cars

abstract: This article advocates self-driving, robotic technology for cars. Recent challenges organized by DARPA have induced a significant advance in technology for autopilots for cars; similar to those already used in aircraft and marine vessels. This article reviews this technology, and argues that enormous societal benefits can be reaped by deploying this emerging technology in the marketplace. It lays out a vision for deployment, and discusses some of the key remaining technology obstacles.


title: Data Consistency Properties and the Trade-offs in Commercial Cloud Storage: the Consumers' Perspective

abstract: A new class of data storage systems, called NoSQL (Not Only SQL), have emerged to complement traditional database systems, with rejection of general ACID transactions as one common feature. Different platforms, and indeed different primitives within one NoSQL platform, can offer various consistency properties, from Eventual Consistency to single-entity ACID. For the platform provider, weaker consistency should allow better availability, lower latency, and other benefits. This paper investigates what consumers observe of the consistency and performance properties of various offerings. We find that many platforms seem in practice to offer more consistency than they promise; we also find cases where the platform offers consumers a choice between stronger and weaker consistency, but there is no observed benefit from accepting weaker consistency properties.


title: Sentiment classification of Hinglish text

abstract: In order to determine the sentiment polarity of Hinglish text written in Roman script, we experimented with different combinations of feature selection methods and a host of classifiers using term frequency-inverse document frequency feature representation. We carried out in total 840 experiments in order to determine the best classifiers for sentiment expressed in the news and Facebook comments written in Hinglish. We concluded that a triumvirate of term frequency-inverse document frequency-based feature representation, gain ratio based feature selection, and Radial Basis Function Neural Network as the best combination to classify sentiment expressed in the Hinglish text.


title: Slowly Digestible Carbohydrate for Balanced Energy: In Vitro and In Vivo Evidence

abstract: There is growing interest among consumers in foods for sustained energy management, and an increasing number of ingredients are emerging to address this demand. The SUSTRA™ 2434 slowly digestible carbohydrate is a blend of tapioca flour and corn starch, with the potential to provide balanced energy after a meal. The aim of the study was to characterize this starch’s digestion profile in vitro (modified Englyst assay) and in vivo (intact and cecectomized rooster study), and to determine its effects on available energy, by measuring post-prandial glycemia in healthy adults (n = 14), in a randomized, double-blind, placebo-controlled, cross-over study, with two food forms: cold-pressed bar and pudding. The in vitro starch digestion yielded a high slowly digestible fraction (51%) compared to maltodextrin (9%). In the rooster digestibility model, the starch was highly digestible (94%). Consumption of slowly digestible starch (SDS), in an instant pudding or bar, yielded a significantly lower glycemic index compared to a control. At individual time points, the SDS bar and pudding yielded blood glucose levels with significantly lower values at 30–60 min and significantly higher values at 120–240 min, demonstrating a balanced energy release. This is the first study to comprehensively characterize the physiological responses to slowly digestible starch (tapioca and corn blend) in in vitro and in vivo studies.


title: Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization.

abstract: This article concerns the assignment of buffer time between two connected flights and the number of reserve crews in crew pairing to mitigate flight disruption due to flight arrival delay. Insufficient crew members for a flight will lead to flight disruptions such as delays or cancellations. In reality, most of these disruption cases are due to arrival delays of the previous flights. To tackle this problem, many research studies have examined the assignment method based on the historical flight arrival delay data of the concerned flights. However, flight arrival delays can be triggered by numerous factors. Accordingly, this article proposes a new forecasting approach using a cascade neural network, which considers a massive amount of historical flight arrival and departure data. The approach also incorporates learning ability so that unknown relationships behind the data can be revealed. Based on the expected flight arrival delay, the buffer time can be determined and a new dynamic reserve crew strategy can then be used to determine the required number of reserve crews. Numerical experiments are carried out based on one year of flight data obtained from 112 airports around the world. The results demonstrate that by predicting the flight departure delay as the input for the prediction of the flight arrival delay, the prediction accuracy can be increased. Moreover, by using the new dynamic reserve crew strategy, the total crew cost can be reduced. This significantly benefits airlines in flight schedule stability and cost saving in the current big data era.


title: Boosting Recommendation in Unexplored Categories by User Price Preference

abstract: State-of-the-art methods for product recommendation encounter a significant performance drop in categories where a user has no purchase history. This problem needs to be addressed since current online retailers are moving beyond single category and attempting to be diversified. In this article, we investigate the challenging problem of product recommendation in unexplored categories and discover that the price, a factor comparable across categories, can improve the recommendation performance significantly. We introduce the price utility concept to characterize users’ sense of price and propose three different utility functions. We show that user price preference in a category is a distribution and we mine typical user price preference patterns based on three different types of distance between distributions. We fuse user price preference through regularization and joint factorization to boost recommendation performance in both browsing and buying shopping orientations. Experimental results show that fusing user price preference improves performance in a series of recommendation tasks: unexplored category recommendation, product recommendation under a given unexplored category, and product recommendation under generic unexplored categories.


title: Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems

abstract: Recently, there has been growing interest in developing unmanned aircraft systems (UAS) with advanced onboard autonomous capabilities. This paper describes the current state of the art in autonomous rotorcraft UAS (RUAS) and provides a detailed literature review of the last two decades of active research on RUAS. Three functional technology areas are identified as the core components of an autonomous RUAS. Guidance, navigation, and control (GNC) have received much attention from the research community, and have dominated the UAS literature from the nineties until now. This paper first presents the main research groups involved in the development of GNC systems for RUAS. Then it describes the development of a framework that provides standard definitions and metrics characterizing and measuring the autonomy level of a RUAS using GNC aspects. This framework is intended to facilitate the understanding and the organization of this survey paper, but it can also serve as a common reference for the UAS community. The main objective of this paper is to present a comprehensive survey of RUAS research that captures all seminal works and milestones in each GNC area, with a particular focus on practical methods and technologies that have been demonstrated in flight tests. These algorithms and systems have been classified into different categories and classes based on the autonomy level they provide and the algorithmic approach used. Finally, the paper discusses the RUAS literature in general and highlights challenges that need to be addressed in developing autonomous systems for unmanned rotorcraft. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.


title: Tomographic SAR Inversion by $L_{1}$ -Norm Regularization—The Compressive Sensing Approach

abstract: Synthetic aperture radar (SAR) tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. The resolution in the elevation direction depends on the size of the elevation aperture, i.e., on the spread of orbit tracks. Since the orbits of modern meter-resolution spaceborne SAR systems, like TerraSAR-X, are tightly controlled, the tomographic elevation resolution is at least an order of magnitude lower than in range and azimuth. Hence, super-resolution reconstruction algorithms are desired. The high anisotropy of the 3-D tomographic resolution element renders the signals sparse in the elevation direction; only a few pointlike reflections are expected per azimuth-range cell. This property suggests using compressive sensing (CS) methods for tomographic reconstruction. This paper presents the theory of 4-D (differential, i.e., space-time) CS TomoSAR and compares it with parametric (nonlinear least squares) and nonparametric (singular value decomposition) reconstruction methods. Super-resolution properties and point localization accuracies are demonstrated using simulations and real data. A CS reconstruction of a building complex from TerraSAR-X spotlight data is presented.


title: Detect-and-Track: Efficient Pose Estimation in Videos

abstract: This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video. We propose an extremely lightweight yet highly effective approach that builds upon the latest advancements in human detection and video understanding. Our method operates in two-stages: keypoint estimation in frames or short clips, followed by lightweight tracking to generate keypoint predictions linked over the entire video. For frame-level pose estimation we experiment with Mask R-CNN, as well as our own proposed 3D extension of this model, which leverages temporal information over small clips to generate more robust frame predictions. We conduct extensive ablative experiments on the newly released multi-person video pose estimation benchmark, PoseTrack, to validate various design choices of our model. Our approach achieves an accuracy of 55.2% on the validation and 51.8% on the test set using the Multi-Object Tracking Accuracy (MOTA) metric, and achieves state of the art performance on the ICCV 2017 PoseTrack keypoint tracking challenge.


title: Incidental findings on brain Magnetic Resonance Imaging in long-term survivors of breast cancer treated with adjuvant chemotherapy.

abstract: Abstract Purpose Incidental brain findings defined as previously undetected abnormalities of potential clinical relevance that are unexpectedly discovered at brain imaging and are unrelated to the purpose of the examination are common in the general population. Because it is unclear whether the prevalence of incidental findings in breast cancer patients treated with chemotherapy is different to that in the general population, we compared the prevalence in breast cancer survivors treated with chemotherapy to that in a population-based sample of women without a history of any cancer. Patients and methods Structural brain MRI (1.5T) was performed in 191 female CMF (Cyclophosphamide, Methotrexate, 5-Fluorouracil) chemotherapy-exposed breast cancer survivors. A reference group of 1590 women without a history of cancer was sampled from a population-based cohort study. All participants were aged 50 to 80 years. Five trained reviewers recorded the brain abnormalities. Two experienced neuro-radiologists reviewed the incidental findings. Results The cancer survivors had completed chemotherapy on average 21 years before. Of the 191 subjects, 2.6% had an aneurysm and 3.7% had a meningioma. The prevalence of meningiomas and aneurysms was not different between the groups. The prevalence of pituitary macro adenomas in the breast cancer survivors (1.6%) was higher than that in the reference group (0.1%) (OR = 23.7; 95% CI 2.3–245.8). Conclusion Contrary to commonly held opinions, we did not observe an increased prevalence of meningiomas in cancer survivors. Breast cancer survivors previously treated with chemotherapy are more likely to develop pituitary adenomas than persons without a history of cancer and chemotherapy treatment.


title: Ascaridia numidae (Leiper, 1908; Travassos, 1913) in Rock Partridge (Alectoris chukar) in Turkey

abstract: This report present first case of Ascaridia numidae isolated from three dead rock partridges (Alectoris chukar) with stressing morphological characteristics and its responsiveness to the medical treatment. A. numidae was confirmed by presence of one papilla on the posterior region of the preanal sucker and a total of 10 pairs of the caudal papillae. In necropsy, a total of 680 (282 male and 3,988 female) A. numidae and 289 (147 male and 142 female) Heterakis dispar were isolated from all partridges. Females were 11.20 mm longer and 0.22 mm wider than males. There was no difference in length of oesophagus (2.27 mm). Lengths of the spikules were equals and ranged from 2.08 to 2.93 according to the body size. Vulva was located in medial line and the mean egg size was 0.10 × 0.06 mm. Faecal samples from live birds from the same farm contained Ascaridia spp. and Heterakis spp. eggs. No eggs were encountered 1 week after oral administration of a single dose of levamisole (30 mg/kg), but 3 months post-medication. In conclusion, this is the first case reporting A. numidae in exotic birds in Turkey. A. numidae has unique papillae that are distinguishable from the other ascarids and is responsive to medical treatment as long as hygienic conditions are maintained.


title: Empowering individuals with do-it-yourself assistive technology

abstract: Assistive Technologies empower individuals to accomplish tasks they might not be able to do otherwise. Unfortunately, a large percentage of Assistive Technology devices that are purchased (35% or more) end up unused or abandoned [7,10], leaving many people with Assistive Technology that is inappropriate for their needs. Low acceptance rates of Assistive Technology occur for many reasons, but common factors include 1) lack of considering user opinion in selection, 2) ease in obtaining devices, 3) poor device performance, and 4) changes in user needs and priorities [7]. We are working to help more people gain access to the Assistive Technology they need by empowering non-engineers to "Do-It-Yourself" (DIY) and create, modify, or build. This paper illustrates that it is possible to custom-build Assistive Technology, and argues why empowering users to make their own Assistive Technology can improve the adoption process (and subsequently adoption rates). We discuss DIY experiences and impressions from individuals who have either built Assistive Technology before, or rely on it. We found that increased control over design elements, passion, and cost motivated individuals to make their own Assistive Technology instead of buying it. We discuss how a new generation of rapid prototyping tools and online communities can empower more individuals. We synthesize our findings into design recommendations to help promote future DIY-AT success.


title: Active Learning from Relative Queries

abstract: Active learning has been extensively studied and shown to be useful in solving real problems. The typical setting of traditional active learning methods is querying labels from an oracle. This is only possible if an expert exists, which may not be the case in many real world applications. In this paper, we focus on designing easier questions that can be answered by a non-expert. These questions poll relative information as opposed to absolute information and can be even generated from sideinformation. We propose an active learning approach that queries the ordering of the importance of an instance's neighbors rather than its label. We explore our approach on real datasets and make several interesting discoveries including that querying neighborhood information can be an effective question to ask and sometimes can even yield better performance than querying labels.


title: Timestamps in Message-Passing Systems That Preserve the Partial Ordering

abstract: Timestamping is a common method of totally ordering events in concurrent programs. However, for applications requiring access to the global state, a total ordering is inappro priate. This paper presents algorithms for timestamping events in both synchronous and asynchronous n1essage-passing programs that allow for access to the partial ordering in herent in a parallel system. The algorithms do not change the con1munications graph or require a central timestamp issuing authority.


title: Unsupervised robotic sorting: Towards autonomous decision making robots

abstract: Autonomous sorting is a crucial task in industrial robotics which can be very challenging depending on the expected amount of automation. Usually, to decide where to sort an object, the system needs to solve either an instance retrieval (known object) or a supervised classification (predefined set of classes) problem. In this paper, we introduce a new decision making module, where the robotic system chooses how to sort the objects in an unsupervised way. We call this problem Unsupervised Robotic Sorting (URS) and propose an implementation on an industrial robotic system, using deep CNN feature extraction and standard clustering algorithms. We carry out extensive experiments on various standard datasets to demonstrate the efficiency of the proposed image clustering pipeline. To evaluate the robustness of our URS implementation, we also introduce a complex real world dataset containing images of objects under various background and lighting conditions. This dataset is used to fine tune the design choices (CNN and clustering algorithm) for URS. Finally, we propose a method combining our pipeline with ensemble clustering to use multiple images of each object. This redundancy of information about the objects is shown to increase the clustering results.


title: XONTO: An Ontology-Based System for Semantic Information Extraction from PDF Documents

abstract: Information extraction is of paramount importance in several real world applications in the areas of business intelligence, competitive and military intelligence. Although several sophisticated and indeed complex approaches were proposed, they are still limited in many aspects. In this paper the novel ontology-based system named XONTO, that allows the semantic extraction of information from PDF unstructured documents, is presented. The XONTO system is founded on the idea of self-describing ontologies in which objects and classes can be equipped by a set of rules named descriptors. These rules represent patterns that allow to automatically recognize and extract ontology objects contained in PDF documents also when information is arranged in tabular form. This way a self-describing ontology expresses the semantic of the information to extract and the rules that, in turn, populate itself. In the paper XONTO system behaviors and structure are sketched by means of a running example.


title: Continuous and fine-grained breathing volume monitoring from afar using wireless signals

abstract: In this work, we propose for the first time an autonomous system, called WiSpiro, that continuously monitors a person's breathing volume with high resolution during sleep from afar. WiSpiro relies on a phase-motion demodulation algorithm that reconstructs minute chest and abdominal movements by analyzing the subtle phase changes that the movements cause to the continuous wave signal sent by a 2.4 GHz directional radio. These movements are mapped to breathing volume, where the mapping relationship is obtained via a short training process. To cope with body movement, the system tracks the large-scale movements and posture changes of the person, and moves its transmitting antenna accordingly to a proper location in order to maintain its beam to specific areas on the frontal part of the person's body. It also incorporates interpolation mechanisms to account for possible inaccuracy of our posture detection technique and the minor movement of the person's body. We have built WiSpiro prototype, and demonstrated through a user study that it can accurately and continuously monitor user's breathing volume with a median accuracy from 90% to 95.4% (or 0.0581 to 0.111 of error) to even in the presence of body movement. The monitoring granularity and accuracy are sufficiently high to be useful for diagnosis by clinical doctor.


title: Contact Angle and Wetting Properties

abstract: This chapter highlights a variety of techniques that are commonly used to measure contact angles, including the conventional telescope-goniometer method, the Wilhelmy balance method, and the more recently developed drop-shape analysis methods. The various applications and limitations of these techniques are described. Notably, studies of ultrasmall droplets on solid surfaces allow wetting theories to be tested down to the nanometer scale, bringing new insight to contact angle phenomena and wetting behavior.


title: Advanced drawing beautification with ShipShape

abstract: Sketching is one of the simplest ways to visualize ideas. Its key advantage is its easy availability and accessibility, as it require the user to have neither deep knowledge of a particular drawing program nor any advanced drawing skills. In practice, however, all these skills become necessary to improve the visual fidelity of the resulting drawing. In this paper, we present ShipShape-a general beautification assistant that allows users to maintain the simplicity and speed of freehand sketching while still taking into account implicit geometric relations to automatically rectify the output image. In contrast to previous approaches ShipShape works with general Bezier curves, enables undo/redo operations, is scale independent, and is fully integrated into Adobe Illustrator. We show various results to demonstrate the capabilities of the proposed method. Graphical abstractDisplay Omitted HighlightsWe present a system for interactive drawing beautification.Drawings are incrementally beautified using extensible set of geometric rules.The system enables easy creation of highly regularized drawing.


title: Review of integrated applications with AIML based chatbot

abstract: Artificial Intelligence Markup Language (AIML) is derived from Extensible Markup Language (XML) which is used to build up conversational agent (chatbot) artificially. There are developed a lot of works to make conversational agent. But low cost, configuration and availability make possible to use it in various applications. In this paper, we give a brief review of some applications which are used AIML chatbot for their conversational service. These applications are related to cultural heritage, e-learning, e-government, web base model, dialog model, semantic analysis framework, interaction framework, humorist expert, network management, adaptive modular architecture as well. In this case, they are not only providing useful services but also interact with customers and give solution of their problems through AIML chatbot instead of human beings. So, this is popular day by day with entrepreneur and users to provide efficient service.


title: GPU-based parallel algorithm for VPL-approximated EM wave propagation

abstract: The simulation of EM (electromagnetic) wave propagation requires considerable computation time, as it analyzes a large number of propagation paths. To overcome this problem, we propose a GPU (graphics processing unit)-based parallel algorithm for VPL (vertical plane launch)-approximated EM wave propagation. The conventional algorithm computes the gain along propagation paths with irregular memory access, which results in low GPU performance. In our proposed algorithm, a CPU reorders irregular propagation paths to a GPU-suitable linear order on the CPU memory at each receiving point. We hid the reordering time behind CPU-GPU communication and GPU-based computation of gain on the reordered memory. We found that our proposed algorithm with a quad GPU is up to 30 times faster than the conventional algorithm with a 16-threaded dual CPU.


title: Enhancement of model predictive control implementation on a DCS PCS7

abstract: It is well-known that the implementation of Advanced Process Control (APC) techniques such as Model Predictive Control (MPC) can enhance the performance of the plant operations in comparison to the common PID controller. However, the limited capabilities of most Distributed Control Systems (DCS) and their simple available controller libraries do not allow flexible and effective implementations of many advanced control approaches. The main purpose of this paper is to focus on simple and effective implementation of MPC on a Siemens DCS called “PCS7”. While PCS7 allows a limited version of MPC called Dynamic Matrix Control (DMC) (i.e. available in its library), our aim is to provide some new tools on PCS7 for simpler and faster implementations of more general versions of MPC with better performances. First, the available DMC block is invoked for a case study. Then, some new blocks for more advanced MPC implementations using SCL (Structured Control Language) programming language are developed and provided. The simulation results show the effectiveness of the newly developed blocks. Besides, it is shown that the new blocks has lower hardware and software limitations.


title: Robot pebbles: One centimeter modules for programmable matter through self-disassembly

abstract: This paper describes the design, fabrication, and experimental results of a programmable matter system capable of 2D shape formation through subtraction. The system is composed of autonomous 1cm modules which use custom-designed electropermanent magnets to bond, communicate, and share power with their neighbors. Given an initial block composed of many of these modules latched together in a regular crystalline structure, our system is able to form shapes by detaching the unnecessary modules. Many experiments show that the modules in our system are able to distribute data at 9600bps to their neighbors with a 98.5% success rate after four retries, and the connectors are able to support over 85 times the weight of a single module.


title: Learning Fine-grained Features via a CNN Tree for Large-scale Classification

abstract: We propose a novel approach to enhance the discriminability of Convolutional Neural Networks (CNN). The key idea is to build a tree structure that could progressively learn fine-grained features to distinguish a subset of classes, by learning features only among these classes. Such features are expected to be more discriminative, compared to features learned for all the classes. We develop a new algorithm to effectively learn the tree structure from a large number of classes. Experiments on large-scale image classification tasks demonstrate that our method could boost the performance of a given basic CNN model. Our method is quite general, hence it can potentially be used in combination with many other deep learning models.


title: A Context Modeling Survey

abstract: Context-awareness is one of the drivers of the ubiquitous computing paradigm, whereas a well designed model is a key accessor to the context in any context-aware system. This paper provides a survey of the the most relevant current approaches to modeling context for ubiquitous computing. Numerous approaches are reviewed, classified relative to their core elements and evaluated with respect to their appropriateness for ubiquitous computing.


title: Biomaterials based strategies for skeletal muscle tissue engineering: Existing technologies and future trends

abstract: Abstract Skeletal muscles have a robust capacity to regenerate, but under compromised conditions, such as severe trauma, the loss of muscle functionality is inevitable. Research carried out in the field of skeletal muscle tissue engineering has elucidated multiple intrinsic mechanisms of skeletal muscle repair, and has thus sought to identify various types of cells and bioactive factors which play an important role during regeneration. In order to maximize the potential therapeutic effects of cells and growth factors, several biomaterial based strategies have been developed and successfully implemented in animal muscle injury models. A suitable biomaterial can be utilized as a template to guide tissue reorganization, as a matrix that provides optimum micro-environmental conditions to cells, as a delivery vehicle to carry bioactive factors which can be released in a controlled manner, and as local niches to orchestrate in situ tissue regeneration. A myriad of biomaterials, varying in geometrical structure, physical form, chemical properties, and biofunctionality have been investigated for skeletal muscle tissue engineering applications. In the current review, we present a detailed summary of studies where the use of biomaterials favorably influenced muscle repair. Biomaterials in the form of porous three-dimensional scaffolds, hydrogels, fibrous meshes, and patterned substrates with defined topographies, have each displayed unique benefits, and are discussed herein. Additionally, several biomaterial based approaches aimed specifically at stimulating vascularization, innervation, and inducing contractility in regenerating muscle tissues are also discussed. Finally, we outline promising future trends in the field of muscle regeneration involving a deeper understanding of the endogenous healing cascades and utilization of this knowledge for the development of multifunctional, hybrid, biomaterials which support and enable muscle regeneration under compromised conditions.


title: Getting inspired!: understanding how and why examples are used in creative design practice

abstract: The use of examples serves a critical role in creative design practice, but details of this process remain an enigma. This is problematic for both the understanding of design activity as well as for developing more effective design tools. In this paper, we report results of a study that understands and compares how designers (N=11) utilize, manage, and share examples to support the creative design process. The domains studied were Web, graphic, and product design. Our study shows that examples are a cornerstone of creative practice and are utilized for many reasons throughout the design process. Since examples are pivotal to the success of a project, more effective tools that support retrieval, storage, and dissemination of examples are needed. This paper contributes understanding of the benefits and roles of examples in the design process and implications for the design of more effective tools that support example usage.


title: FRIL: A tool for comparative record linkage

abstract: A fine-grained record integration and linkage tool (FRIL) is presented. The tool extends traditional record linkage tools with a richer set of parameters. Users may systematically and iteratively explore the optimal combination of parameter values to enhance linking performance and accuracy. Results of linking a birth defects monitoring program and birth certificate data using FRIL show 99% precision and 95% recall rates when compared to results obtained through handcrafted algorithms, and the process took significantly less time to complete. Experience and experimental result suggest that FRIL has the potential to increase the accuracy of data linkage across all studies involving record linkage. In particular, FRIL will enable researchers to assess objectively the quality of linked data.


Early to rise? The effect of daily start times on academic performance


title: Context-Aware Deep Spatiotemporal Network for Hand Pose Estimation From Depth Images

abstract: As a fundamental and challenging problem in computer vision, hand pose estimation aims to estimate the hand joint locations from depth images. Typically, the problems are modeled as learning a mapping function from images to hand joint coordinates in a data-driven manner. In this paper, we propose a context-aware deep spatiotemporal network, a novel method to jointly model the spatiotemporal properties for hand pose estimation. Our proposed network is able to learn the representations of the spatial information and the temporal structure from the image sequences. Moreover, by adopting the adaptive fusion method, the model is capable of dynamically weighting different predictions to lay emphasis on sufficient context. Our method is examined on two common benchmarks, the experimental results demonstrate that our proposed approach achieves the best or the second-best performance with the state-of-the-art methods and runs in 60 fps.


title: Effective Nearest-Neighbor Music Recommendations

abstract: Automated recommendations for next tracks to listen to or to include in a playlist are a common feature on modern music platforms. Correspondingly, a variety of algorithmic approaches for determining tracks to recommend have been proposed in academic research. The most sophisticated among them are often based on conceptually complex learning techniques which can also require substantial computational resources or special-purpose hardware like GPUs. Recent research, however, showed that conceptually more simple techniques, e.g., based on nearest-neighbor schemes, can represent a viable alternative to such techniques in practice. In this paper, we describe a hybrid technique for next-track recommendation, which was evaluated in the context of the ACM RecSys 2018 Challenge. A combination of nearest-neighbor techniques, a standard matrix factorization algorithm, and a small set of heuristics led our team KAENEN to the 3rd place in the "creative" track and the 7th one in the "main" track, with accuracy results only a few percent below the winning teams. Given that offline prediction accuracy is only one of several possible quality factors in music recommendation, practitioners have to validate if slight accuracy improvements truly justify the use of highly complex algorithms in real-world applications.


title: Sketching, scaffolding, and inking: a visual history for interactive 3D modeling

abstract: Inspired by the comic-book production pipeline, a method is proposed for integrating visual aspects of the sketching process into 3D sketch-based modeling systems. In particular, artist-drawn construction aids called visual scaffolding are explored. Two scaffolding components which simulate elements of pencil sketching, geometric massing and eraser marks, are integrated into a rendering pipeline which also includes a suite of new object-space techniques for high-fidelity pen-and-ink depiction of implicit surfaces. Based on a hybrid, hierarchical approach which leverages both the implicit surface definition and an underlying coarse tessellation, new methods are described for computing silhouettes, suggestive contours, surface stippling, and surfel-based visibility culling. These techniques are real-time but require no pre-computation, allowing them to be applied to dynamic hierarchical implicit surfaces, and are demonstrated in ShapeShop, an interactive sketch-based modeling tool. The result is a real-time display pipeline which composites these novel scaffolding and pen-and-ink techniques to depict a visual history of the modeling process.


title: An Overview of the Blue Gene/L System Software Organization

abstract: The Blue Gene/L supercomputer will use system-on-a-chip integration and a highly scalable cellular architecture. With 65,536 compute nodes, Blue Gene/L represents a new level of complexity for parallel system software, with specific challenges in the areas of scalability, maintenance and usability. In this paper we present our vision of a software architecture that faces up to these challenges, and the simulation framework that we have used for our experiments.


title: Word Sense And Subjectivity

abstract: Subjectivity and meaning are both important properties of language. This paper explores their interaction, and brings empirical evidence in support of the hypotheses that (1) subjectivity is a property that can be associated with word senses, and (2) word sense disambiguation can directly benefit from subjectivity annotations.