title: Recent progress and trends in predictive visual analytics

abstract: A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem spurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the prediction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual analytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summarization of the predictive analytics workflow.


title: Arabic LFG-inspired Dependency Treebank

abstract: For the data-driven approach in Natural Language Processing (NLP) applications, good quality linguistic resources considered as a main factor to obtain good results. Although Arabic language is one of the main languages in the world, it is considered as low-resourced language in term of good quality and free linguistic resources. This work presents the first stage of building a new open source dependency treebank for Arabic language. It describes the prototype of the new dependency treebank that are inspired by Lexical Functional Grammar (LFG). This paper shows a main approach of developing a newly treebank and put lines the future work needed to complete this novel linguistic resource.


title: Solution aversion: On the relation between ideology and motivated disbelief.

abstract: There is often a curious distinction between what the scientific community and the general population believe to be true of dire scientific issues, and this skepticism tends to vary markedly across groups. For instance, in the case of climate change, Republicans (conservatives) are especially skeptical of the relevant science, particularly when they are compared with Democrats (liberals). What causes such radical group differences? We suggest, as have previous accounts, that this phenomenon is often motivated. However, the source of this motivation is not necessarily an aversion to the problem, per se, but an aversion to the solutions associated with the problem. This difference in underlying process holds important implications for understanding, predicting, and influencing motivated skepticism. In 4 studies, we tested this solution aversion explanation for why people are often so divided over evidence and why this divide often occurs so saliently across political party lines. Studies 1, 2, and 3-using correlational and experimental methodologies-demonstrated that Republicans' increased skepticism toward environmental sciences may be partly attributable to a conflict between specific ideological values and the most popularly discussed environmental solutions. Study 4 found that, in a different domain (crime), those holding a more liberal ideology (support for gun control) also show skepticism motivated by solution aversion. (PsycINFO Database Record (c) 2014 APA, all rights reserved). Language: en


title: Massive MIMO performance evaluation based on measured propagation data

abstract: Massive MIMO, also known as very-large MIMO or large-scale antenna systems, is a new technique that potentially can offer large network capacities in multi-user scenarios. With a massive MIMO system, we consider the case where a base station equipped with a large number of antenna elements simultaneously serves multiple single-antenna users in the same time-frequency resource. So far, investigations are mostly based on theoretical channels with independent and identically distributed (i.i.d.) complex Gaussian coefficients, i.e., i.i.d. Rayleigh channels. Here, we investigate how massive MIMO performs in channels measured in real propagation environments. Channel measurements were performed at 2.6 GHz using a virtual uniform linear array (ULA) which has a physically large aperture, and a practical uniform cylindrical array (UCA) which is more compact in size, both having 128 antenna ports. Based on measurement data, we illustrate channel behavior of massive MIMO in three representative propagation conditions, and evaluate the corresponding performance. The investigation shows that the measured channels, for both array types, allow us to achieve performance close to that in i.i.d. Rayleigh channels. It is concluded that in real propagation environments we have characteristics that can allow for efficient use of massive MIMO, i.e., the theoretical advantages of this new technology can also be harvested in real channels.


title: Multi-Relational Learning at Scale with ADMM

abstract: Learning from multiple-relational data which contains noise, ambiguities, or duplicate entities is essential to a wide range of applications such as statistical inference based on Web Linked Data, recommender systems, computational biology, and natural language processing. These tasks usually require working with very large and complex datasets - e.g., the Web graph - however, current approaches to multi-relational learning are not practical for such scenarios due to their high computational complexity and poor scalability on large data. ::: In this paper, we propose a novel and scalable approach for multi-relational factorization based on consensus optimization. Our model, called ConsMRF, is based on the Alternating Direction Method of Multipliers (ADMM) framework, which enables us to optimize each target relation using a smaller set of parameters than the state-of-the-art competitors in this task. ::: Due to ADMM's nature, ConsMRF can be easily parallelized which makes it suitable for large multi-relational data. Experiments on large Web datasets - derived from DBpedia, Wikipedia and YAGO - show the efficiency and performance improvement of ConsMRF over strong competitors. In addition, ConsMRF near-linear scalability indicates great potential to tackle Web-scale problem sizes.


Efficient Hybrid Proxy Re-Encryption for Practical Revocation and Key Rotation


title: An overview of routing optimization for internet traffic engineering

abstract: Traffic engineering is an important mechanism for Internet network providers seeking to optimize network performance and traffic delivery. Routing optimization plays a key role in traffic engineering, finding efficient routes so as to achieve the desired network performance. In this survey we review Internet traffic engineering from the perspective of routing optimization. A taxonomy of routing algorithms in the literature is provided, dating from the advent of the TE concept in the late 1990s. We classify the algorithms into multiple dimensions: unicast/multicast, intra-/inter- domain, IP-/MPLS-based and offline/online TE schemes. In addition, we investigate some important traffic engineering issues, including robustness, TE interactions, and interoperability with overlay selfish routing. In addition to a review of existing solutions, we also point out some challenges in TE operation and important issues that are worthy of investigation in future research activities.


Understanding the Container Ecosystem : A Taxonomy of Building Blocks for Container Lifecycle and Cluster Management


title: ADVERSE SELECTION AND REPUTATION SYSTEMS IN ONLINE AUCTIONS: EVIDENCE FROM EBAY MOTORS

abstract: While the possibility of adverse selection is present in many transactional settings, online auctions appear to be especially susceptible to the problem. Unlike buyers in most traditional settings, online auction shoppers are physically unable to inspect the products for sale and must rely on pictures and descriptions provided by the seller. If buyers cannot distinguish quality until after the purchase has been made, there is no incentive for sellers to provide high quality products. As a result, buyers will be unwilling to pay a quality premium, the average quality in the market will decline, and the level of trade will fall to a level below what is socially optimal. Accordingly, if adverse selection exists in online auctions, the quality of items traded would be expected to be subaverage. In addition, we would expect the decreases in online prices to be larger than in offline prices as the variance in the condition of items increases. Using data from completed eBay Motors vehicle auctions, we test both assumptions and examine the ability of online reputation systems to offset the effects of adverse selection. Our results suggest that adverse selection is more pronounced in online auctions compared to traditional marketplaces for used goods, and that reputation systems reduce, but do not fully eliminate, the problem. Consistent with theoretical predictions, we find that as vehicle age and mileage increase (i.e., as the variance of a car’s condition increases), the price that eBay buyers are willing to pay for the vehicle decreases by amounts greater than we would expect offline. In addition, we find that newer cars and those with low mileage are less likely to be sold on eBay. Further, sellers of higher quality vehicles and those with poorer reputations are more likely to protect themselves from the effects of adverse selection by setting reserve prices, and sellers with better reputations are more likely to sell their vehicles and receive higher price premiums.


title: Soft sensors for crude distillation unit product properties estimation and control

abstract: Neural network-based soft sensors are developed for quality estimation of kerosene, a refinery crude distillation unit side product. Based on temperature and flow measurements two soft sensors serve as the estimators for the kerosene distillation end point (95%) and freezing point. The neural networks are trained by the adaptive gradient method using cascade learning. Research results show possibilities of applying soft sensors for refinery product quality estimation and inferential control as an alternative for process analyzers and laboratory assays.


title: A Unitary Approach to Lexical Pragmatics: Relevance, Inference and Ad Hoc Concepts

abstract: According to recent work in the new field of lexical pragmatics, the meanings of words are frequently pragmatically adjusted and fine-tuned in context, so that their contribution to the proposition expressed is different from their lexically encoded sense. Well-known examples include lexical narrowing (e.g. ‘drink’ used to mean ALCOHOLIC DRINK), approximation (or loosening) (e.g. ‘flat’ used to mean RELATIVELY FLAT) and metaphorical extension (e.g. ‘bulldozer’ used to mean FORCEFUL PERSON). These three phenomena are often studied in isolation from each other and given quite distinct kinds of explanation. In this chapter, we will propose a more unified account. We will try to show that narrowing, loosening and metaphorical extension are simply different outcomes of a single interpretive process which creates an ad hoc concept, or occasion-specific sense, based on interaction among encoded concepts, contextual information and pragmatic expectations or principles. We will outline an inferential account of the lexical adjustment process using the framework of relevance theory, and compare it with some alternative accounts.


title: Learning Discriminant Face Descriptor

abstract: Local feature descriptor is an important module for face recognition and those like Gabor and local binary patterns (LBP) have proven effective face descriptors. Traditionally, the form of such local descriptors is predefined in a handcrafted way. In this paper, we propose a method to learn a discriminant face descriptor (DFD) in a data-driven way. The idea is to learn the most discriminant local features that minimize the difference of the features between images of the same person and maximize that between images from different people. In particular, we propose to enhance the discriminative ability of face representation in three aspects. First, the discriminant image filters are learned. Second, the optimal neighborhood sampling strategy is soft determined. Third, the dominant patterns are statistically constructed. Discriminative learning is incorporated to extract effective and robust features. We further apply the proposed method to the heterogeneous (cross-modality) face recognition problem and learn DFD in a coupled way (coupled DFD or C-DFD) to reduce the gap between features of heterogeneous face images to improve the performance of this challenging problem. Extensive experiments on FERET, CAS-PEAL-R1, LFW, and HFB face databases validate the effectiveness of the proposed DFD learning on both homogeneous and heterogeneous face recognition problems. The DFD improves POEM and LQP by about 4.5 percent on LFW database and the C-DFD enhances the heterogeneous face recognition performance of LBP by over 25 percent.


title: The Innovative Applications of Artificial Intelligence Conference: Past and Future

abstract: This article is a reflection on the goals and focus of the Innovative Applications of Artificial Intelligence (IAAI) Conference. The author begins with an historical review of the conference. He then goes on to discuss the role of the IAAI conference, including an examination of the relationship between AI scientific research and the application of AI technology. He concludes with a presentation of the new vision for the IAAI conference.


title: LTE-V for Sidelink 5G V2X Vehicular Communications: A New 5G Technology for Short-Range Vehicle-to-Everything Communications

abstract: This article provides an overview of the long-term evolution-vehicle (LTE-V) standard supporting sidelink or vehicle-to-vehicle (V2V) communications using LTE's direct interface named PC5 in LTE. We review the physical layer changes introduced under Release 14 for LTE-V, its communication modes 3 and 4, and the LTE-V evolutions under discussion in Release 15 to support fifth-generation (5G) vehicle-to-everything (V2X) communications and autonomous vehicles' applications. Modes 3 and 4 support direct V2V communications but differ on how they allocate the radio resources. Resources are allocated by the cellular network under mode 3. Mode 4 does not require cellular coverage, and vehicles autonomously select their radio resources using a distributed scheduling scheme supported by congestion control mechanisms. Mode 4 is considered the baseline mode and represents an alternative to 802.11p or dedicated shortrange communications (DSRC). In this context, this article also presents a detailed analysis of the performance of LTE-V sidelink mode 4, and proposes a modification to its distributed scheduling.


title: Novel frictional-locking-mechanism for a flat belt: Theory, mechanism, and validation

abstract: Abstract In this study, a novel mechanism that locks a flat belt using frictional force was proposed. The mechanism is composed of two important elements; the flat belt folds back and overlaps itself while coming into contact with a curved surface, and the contact region can be changed through the use of constrained rollers. The former of these two elements allows for a belt to be locked when a large tensile force is applied due to the resultant frictional force; some theories for analyzing this mechanism are proposed in this paper. The latter of the two elements, meanwhile, ensures that the belt remains locked or unlocked, and it switches the state of the belt from locked to unlocked (and vice versa) using only a small amount of power, despite a large force being applied to the belt. The proposed mechanism therefore results in an efficient locking device for flat belts. This mechanism, and the associated theoretical equations, were validated experimentally, and an example of a practical design using the proposed mechanism is described.


title: Cognizant Enterprise Maturity Model (CEMM)

abstract: Presents the cognizant enterprise maturity model (CEMM). The model provides tripartite usage of calibration, capability assessment and maturity advancement. The entry point is an organizational and departmental profiler that provides relevance measures based on fuzzy multicriteria group decision-making capabilities to key maturity areas (KMAs) identified for the five-level maturity model. These relevance measures allow organizations to weigh and score the KMAs and to nurture the model based on their own experience and on industry experience. Each KMA comes with a set of goals and abilities. CEMM relies on four criteria: (1) cognizant environments, (2) knowledge utilization and leverage, (3) the SEI's software CMM (capability maturity model) and people CMM, and (4) an adaptive enterprise, and it distinguishes between information and knowledge management (KM).


title: Deep Defense: Training DNNs with Improved Adversarial Robustness

abstract: Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in security-critical systems. Recent works have shown the possibility of generating imperceptibly perturbed image inputs (a.k.a., adversarial examples) to fool well-trained DNN classifiers into making arbitrary predictions. To address this problem, we propose a training recipe named"deep defense". Our core idea is to integrate an adversarial perturbation-based regularizer into the classification objective, such that the obtained models learn to resist potential attacks, directly and precisely. The whole optimization problem is solved just like training a recursive network. Experimental results demonstrate that our method outperforms training with adversarial/Parseval regularizations by large margins on various datasets (including MNIST, CIFAR-10 and ImageNet) and different DNN architectures. Code and models for reproducing our results are available at https://github.com/ZiangYan/deepdefense.pytorch


title: RecQL4 regulates autophagy and apoptosis in U2OS cells

abstract: RecQL4, one of the 5 human RecQ helicases, is a key mediator of genomic stability and its deficiency can cause premature aging phenotypes. Here, by using CRISPR/Cas and RNAi technology, we demonstrated that autophagy level was elevated in both RecQL4 knockdown and knockout cells compared with those of the control cells. Surprisingly, mitochondrial content was increased and LC3 co-localization with mitochondria was partially lost in RecQL4 knockout cells compared with the control cells, suggesting that RecQL4 deficiency impaired mitophagic processes in U2OS cells. Furthermore, we found that knockout of RecQL4 destabilized PINK1. In addition, RecQL4 knockout cells were more susceptible to apoptosis under mitochondrial stress than the control cells. In conclusion, our findings indicated a novel role of RecQL4 in the regulation of autophagy/mitophagy in U2OS cells.


title: Client-Side Cross-Site Scripting Protection

abstract: Web applications are becoming the dominant way to provide access to online services. At the same time, web application vulnerabilities are being discovered and disclosed at an alarming rate. Web applications often make use of JavaScript code that is embedded into web pages to support dynamic client-side behavior. This script code is executed in the context of the user's web browser. To protect the user's environment from malicious JavaScript code, browsers use a sand-boxing mechanism that limits a script to access only resources associated with its origin site. Unfortunately, these security mechanisms fail if a user can be lured into downloading malicious JavaScript code from an intermediate, trusted site. In this case, the malicious script is granted full access to all resources (e.g., authentication tokens and cookies) that belong to the trusted site. Such attacks are called cross-site scripting (XSS) attacks. In general, XSS attacks are easy to execute, but difficult to detect and prevent. One reason is the high flexibility of HTML encoding schemes, offering the attacker many possibilities for circumventing server-side input filters that should prevent malicious scripts from being injected into trusted sites. Also, devising a client-side solution is not easy because of the difficulty of identifying JavaScript code as being malicious. This paper presents Noxes, which is, to the best of our knowledge, the first client-side solution to mitigate cross-site scripting attacks. Noxes acts as a web proxy and uses both manual and automatically generated rules to mitigate possible cross-site scripting attempts. Noxes effectively protects against information leakage from the user's environment while requiring minimal user interaction and customization effort.


title: Model-Assisted Adaptive Recovery of Compressed Sensing with Imaging Applications

abstract: In compressive sensing (CS), a challenge is to find a space in which the signal is sparse and, hence, faithfully recoverable. Since many natural signals such as images have locally varying statistics, the sparse space varies in time/spatial domain. As such, CS recovery should be conducted in locally adaptive signal-dependent spaces to counter the fact that the CS measurements are global and irrespective of signal structures. On the contrary, existing CS reconstruction methods use a fixed set of bases (e.g., wavelets, DCT, and gradient spaces) for the entirety of a signal. To rectify this problem, we propose a new framework for model-guided adaptive recovery of compressive sensing (MARX) and show how a 2-D piecewise autoregressive model can be integrated into the MARX framework to make CS recovery adaptive to spatially varying second order statistics of an image. In addition, MARX offers a mechanism of characterizing and exploiting structured sparsities of natural images, greatly restricting the CS solution space. Simulation results over a wide range of natural images show that the proposed MARX technique can improve the reconstruction quality of existing CS methods by 2-7 dB.


title: Preventing data errors with continuous testing

abstract: Today, software systems that rely on data are ubiquitous, and ensuring the data's quality is an increasingly important challenge as data errors result in annual multi-billion dollar losses. While software debugging and testing have received heavy research attention, less effort has been devoted to data debugging: identifying system errors caused by well-formed but incorrect data. We present continuous data testing (CDT), a low-overhead, delay-free technique that quickly identifies likely data errors. CDT continuously executes domain-specific test queries; when a test fails, CDT unobtrusively warns the user or administrator. We implement CDT in the ConTest prototype for the PostgreSQL database management system. A feasibility user study with 96 humans shows that ConTest was extremely effective in a setting with a data entry application at guarding against data errors: With ConTest, users corrected 98.4% of their errors, as opposed to 40.2% without, even when we injected 40% false positives into ConTest's output. Further, when using ConTest, users corrected data entry errors 3.2 times faster than when using state-of-the-art methods.


title: Pipeline Item-Based Collaborative Filtering Based on MapReduce

abstract: As we all know, it is an era of information explosion, in which we always get huge amounts of information. Therefore, it is in urgent need of picking out the useful and interesting information quickly. In order to solve this serious problem, recommendation system arises at the historic moment. Among the existing recommendation algorithms, the item-based collaborative filtering recommendation algorithm is the most widely used one. Its principle is based on the user's evaluation of items. The purpose is to find the similarity between users, and recommend items to the target user according to the records of the similar users. However, the number of customers and products keeps increasing at a high rate, which increases the cost to find out the recommendation list for each user. The efficiency of a single common computer will not satisfy the requirement and the super computer will cost too much. In order to solve the problem, we propose to use MapReduce to implement the recommendation system. Besides, we distribute the job to some computer clusters and the input file of the current computer cluster only relies on the previous one or the origin input. So the pipeline technology will be adopted to improve the efficiency further. The experiment shows that the method can merge the ability of some common PC to process large-scale data in a short time.


title: First Step toward Model-Free, Anonymous Object Tracking with Recurrent Neural Networks

abstract: In this paper, we propose and study a novel visual object tracking approach based on convolutional networks and recurrent networks. The proposed approach is distinct from the existing approaches to visual object tracking, such as filtering-based ones and tracking-by-detection ones, in the sense that the tracking system is explicitly trained off-line to track anonymous objects in a noisy environment. The proposed visual tracking model is end-to-end trainable, minimizing any adversarial effect from mismatches in object representation and between the true underlying dynamics and learning dynamics. We empirically show that the proposed tracking approach works well in various scenarios by generating artificial video sequences with varying conditions; the number of objects, amount of noise and the match between the training shapes and test shapes.


title: Adversarial Reinforcement Learning Framework for Benchmarking Collision Avoidance Mechanisms in Autonomous Vehicles

abstract: With the rapidly growing interest in autonomous navigation, the body of research on motion planning and collision avoidance techniques has enjoyed an accelerating rate of novel proposals and developments. However, the complexity of new techniques and their safety requirements render the bulk of current benchmarking frameworks inadequate, thus leaving the need for efficient comparison techniques unanswered. This work proposes a novel framework based on deep reinforcement learning for benchmarking the behavior of collision avoidance mechanisms under the worst-case scenario of dealing with an optimal adversarial agent, trained to drive the system into unsafe states. We describe the architecture and flow of this framework as a benchmarking solution, and demonstrate its efficacy via a practical case study of comparing the reliability of two collision avoidance mechanisms in response to intentional collision attempts.


navigation, and learning for off-road traversal


Differentiation of 3 D scanners and their positioning method when applied to pipeline integrity


Towards a Logical Analysis of Misleading and Trust Erosion


Title Co-MaxRS : Continuous Maximizing Range-Sum Query


title: Probabilistic Planning for Continuous Dynamic Systems under Bounded Risk

abstract: This paper presents a model-based planner called the Probabilistic Sulu Planner or the p-Sulu Planner, which controls stochastic systems in a goal directed manner within user-specified risk bounds. We first develop a new plan representation called a chance-constrained qualitative state plan (CCQSP), through which users can specify the desired evolution of the plant state as well as the acceptable level of risk. We then develop the p-Sulu Planner, which can tractably solve a CCQSP planning problem.


title: Human actions recognition based on 3D deep neural network

abstract: In this paper, a new proposed model has been used to recognize human actions from video frames using a 3D deep neural network (3D DNN). To classify human actions, our recognition process is implemented under different recording conditions from a surveillance camera. By applying Caffe_GoogLeNet framework, we trained our 3D DNN with different training epoch values (TEs). The experiments were then evaluated using three different datasets: KTH, Weizmann, and UCF101 with gray and color resolutions. The results of the experiments demonstrate significantly high performance in the recognition rates by changing the training epoch values (TEs) to accomplish the best classification accuracy with a remarkable short running time. We then compare the classification accuracy results of 3D DNN with other state-of-the-art for three datasets. Classification accuracy resulting is 98.90% for KTH dataset, while Weizmann dataset had an accuracy of 97.02 %. The accuracy of UCF 101 dataset reached to 100% which was the optimum state in our model.


title: A Framework for Integrating Sarbanes-Oxley Compliance into the Systems Development Process

abstract: The Sarbanes-Oxley act introduces a new set of requirements into software development. Corporations need to assess their internal control effectiveness for business processes to show compliance with the act. This paper proposes a conceptual framework for integrating SarbanesOxley compliance needs into the software development process by mapping the various stages of the software development process with an established framework for internal controls.


title: Goal Evaluation of Segmentation Algorithms for Traffic Sign Recognition

abstract: This paper presents a quantitative comparison of several segmentation methods (including new ones) that have successfully been used in traffic sign recognition. The methods presented can be classified into color-space thresholding, edge detection, and chromatic/achromatic decomposition. Our support vector machine (SVM) segmentation method and speed enhancement using a lookup table (LUT) have also been tested. The best algorithm will be the one that yields the best global results throughout the whole recognition process, which comprises three stages: 1) segmentation; 2) detection; and 3) recognition. Thus, an evaluation method, which consists of applying the entire recognition system to a set of images with at least one traffic sign, is attempted while changing the segmentation method used. This way, it is possible to observe modifications in performance due to the kind of segmentation used. The results lead us to conclude that the best methods are those that are normalized with respect to illumination, such as RGB or Ohta Normalized, and there is no improvement in the use of Hue Saturation Intensity (HSI)-like spaces. In addition, an LUT with a reduction in the less-significant bits, such as that proposed here, improves speed while maintaining quality. SVMs used in color segmentation give good results, but some improvements are needed when applied to achromatic colors.


title: Vehicle Detection and Tracking in Car Video Based on Motion Model

abstract: This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. We have investigated videos of day and night on different types of roads, showing that our approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras.


title: An Open-Source Framework for Smart Meters: Data Communication and Security Traffic Analysis

abstract: In this paper, a structured methodology is proposed to define the architecture for a communication framework with multiframe capability, which can be embedded in a residential smart meter hardware for smart grid applications. This framework is based on the Transmission Control Protocol/Internet protocol, and it is considered that the data are exchanged via wireless technologies. The architectural model was based upon the state-of-the-art in software engineering, making use of the design patterns and the principles of low coupling and high cohesion, which result in a solution that is both more reliable and maintainable. A security module is also provided and the framework performance is evaluated, in terms of packet throughput and computational effort of the security layer on the processing device, via practical experiments of some laboratory scenarios. Furthermore, errors on some frames are introduced in order to evaluate the feasibility of this framework in real field applications. Results show that this framework architecture supports acceptable transfer rates even with extra computational cost due to the use of data encryption and separated processing modules, making it a feasible proposal for the considered application context.


Augmenting reality for augmented reality


Every Object Tells a Story


title: Serious Games Get Smart: Intelligent Game-Based Learning Environments

abstract: Intelligent game-based learning environments integrate commercial game technologies with AI methods from intelligent tutoring systems and intelligent narrative technologies. This article introduces the CRYSTAL ISLAND intelligent game-based learning environment, which has been under development in the authors’ laboratory for the past seven years. After presenting CRYSTAL ISLAND, the principal technical problems of intelligent game-based learning environments are discussed: narrative-centered tutorial planning, student affect recognition, student knowledge modeling, and student goal recognition. Solutions to these problems are illustrated with research conducted with the CRYSTAL ISLAND learning environment.


title: A Dutch Approach to Cybersecurity through Participation

abstract: At the end of 2013, the Dutch government published its second National Cyber Security Strategy, a major step in the development of the governmental approach to cybersecurity. This document stresses the importance of increasing cyber resilience through participation. The challenge of securing this entire domain is too large for any one organization. Therefore, the Dutch have chosen to engage the private sector, and foster participation to face the challenge together. In typical Dutch style, the government encourages voluntary, bottom-up participation and, thus, invites public and private parties to the table to discuss common threats and identify areas for collaboration. The recently formed Dutch National Cyber Security Centre plays a crucial role in coordinating, facilitating, encouraging and supporting such participation.


title: Practical Considerations for Building GROMOS-Compatible Small-Molecule Topologies

abstract: Molecular dynamics simulations are being applied to increasingly complex systems, including those involving small endogenous compounds and drug molecules. In order to obtain meaningful and accurate data from these simulations, high-quality topologies for small molecules must be generated in a manner that is consistent with the derivation of the force field applied to the system. Often, force fields are designed for use with macromolecules such as proteins, making their transferability to other species challenging. Investigators are increasingly attracted to automated topology generation programs, although the quality of the resulting topologies remains unknown. Here we assess the applicability of the popular PRODRG server that generates small-molecule topologies for use with the GROMOS family of force fields. We find that PRODRG does not reproduce topologies for even the most well-characterized species in the force field due to inconsistent charges and charge groups. We assessed the effects of PRODRG-derived charges on several systems: pure liquids, amino acids at a hydrophobic-hydrophilic interface, and an enzyme-cofactor complex. We found that partial atomic charges generated by PRODRG are largely incompatible with GROMOS force fields, and the behavior of these systems deviates substantially from that of simulations using GROMOS parameters. We conclude by proposing several points as "best practices" for parametrization of small molecules under the GROMOS force fields.


Parallel Quicksort Algorithm using OpenMP


title: A Multivariate Correlation Assessment of Chess Proficiency Using Brain Signals

abstract: Chess game has attracted the interest of many academic works with several experiments carried out to address the differences in brain activation on proficients and non-proficients chess players. However, none of these works takes into account explicitly the cognitive patterns of the chess players to rank and classify them. In our work, we aim to present a cognitive model, using EEG and multivariate statistical methods, to assess chess volunteers and compare their performance to the traditional metric based on accuracy and response time. In total, 32 volunteers have participated in this study based on visual stimuli computationally generated. Our main results show that it is important not only to top rank the volunteers with high accuracy and low response time, but also understand how the main brain processes occur to a chess expert to achieve such top performance.


Design and Development of a Tapered Slot Vivaldi Antenna for Ultra-Wide Band Application


title: Project Jacquard: Interactive Digital Textiles at Scale

abstract: Project Jacquard presents manufacturing technologies that enable deploying invisible ubiquitous interactivity at scale. We propose novel interactive textile materials that can be manufactured inexpensively using existing textile weaving technology and equipment. The development of touch-sensitive textiles begins with the design and engineering of a new highly conductive yarn. The yarns and textiles can be produced by standard textile manufacturing processes and can be dyed to any color, made with a number of materials, and designed to a variety of thicknesses and textures to be consistent with garment designers' needs. We describe the development of yarn, textiles, garments, and user interactivity; we present the opportunities and challenges of creating a manufacturable interactive textile for wearable computing.


title: Early and Multi Level Fusion for Reliable Automotive Safety Systems

abstract: The fusion of data from different sensorial sources is today the most promising method to increase robustness and reliability of environmental perception. The project ProFusion2 pushes the sensor data fusion for automotive applications in the field of driver assistance systems. ProFusion2 was created to enhance fusion techniques and algorithms beyond the current state-of-the-art. It is a horizontal subproject in the Integrated Project PReVENT (funded by the EC). The paper presents two approaches concerning the detection of vehicles in road environments. An early fusion and a multi level fusion processing strategy are described. The common framework for the representation of the environment model and the representation of perception results is introduced. The key feature of this framework is the storing and representation of all data involved in one perception memory in a common data structure and the holistic accessibility.


title: Roadmap for smart metering deployment for Indian smart grid

abstract: India has the 3rd largest Electricity transmission and distribution network in the world. However, India faces a number of challenges like inadequate access to electricity, supply shortfalls, huge network losses, poor quality, reliability and theft. Smart Grid would address these issues and transform the existing grid into a more efficient and reliable one that would help in providing electricity access to all. The smart metering infrastructure is a major component of smart grid and this will enhance efficiency and reliability through use of smart/advance sensors, modern communication infrastructure, metering technologies, and energy management solutions for current electric power system. In India, government has taken up initiative for reforming distribution system. This paper discusses the ongoing initiative taken up by government and roadmap for smart metering deployment for Indian smart grid.


title: Deep Occlusion Reasoning for Multi-camera Multi-target Detection

abstract: People detection in single 2D images has improved greatly in recent years. However, comparatively little of this progress has percolated into multi-camera multi-people tracking algorithms, whose performance still degrades severely when scenes become very crowded. In this work, we introduce a new architecture that combines Convolutional Neural Nets and Conditional Random Fields to explicitly model those ambiguities. One of its key ingredients are high-order CRF terms that model potential occlusions and give our approach its robustness even when many people are present. Our model is trained end-to-end and we show that it outperforms several state-of-art algorithms on challenging scenes.


title: Study on Pattern Distortion and DOA Estimation Performance of Crossed-Loop/Monopole Antenna in HF Radar

abstract: Crossed-loop (CL)/monopole antenna has been widely used in high-frequency ground-wave radar for ocean surface remote sensing owing to its compact size. The multiple signal classification (MUSIC) algorithm is commonly adapted to achieve a favorable angular resolution for this broad beam antenna. However, the direction of arrival (DOA) estimation is always troubled by the frequently occurring distortion of antenna pattern. Previous studies are mainly focused on the calibration by using the actual pattern or other assistant algorithms. In this paper, theoretical analysis is made to illustrate the reason for pattern distortion, which draws a conclusion that the distortion is essentially due to the different influence of environmental obstacles upon the electric field part and magnetic field part. This also implies that there is nearly no distortion between the two loops, which is validated by three different experiments. Based on this conclusion, the DOA estimation performance of the MUSIC algorithm is studied by creating a numerical relationship associating the estimation error with distortion level. Furthermore, a new method using ideal pattern is proposed to improve the estimation performance by removing the amplitude information of monopole and relying on the CL processing. Both simulation and experimental results are given to prove its validity.


title: Femoral cortical index: an indicator of poor bone quality in patient with hip fracture

abstract: BACKGROUND ::: Osteoporosis is a common disease in elderly, characterized by poor bone quality as a result of alterations affecting trabecular bone. However, recent studies have described also an important role of alterations of cortical bone in the physiopathology of osteoporosis. Although dual-energy X-ray absorptiometry (DXA) is a valid method to assess bone mineral density, in the presence of comorbidities real bone fragility is unable to be evaluated. The number of hip fractures is rising, especially in people over 85 years old. ::: ::: ::: AIMS ::: The aim is to evaluate an alternative method so that it can indicate fracture risk, independent of bone mineral density (BMD). Femoral cortical index (FCI) assesses cortical bone stock using femur X-ray. ::: ::: ::: METHODS ::: A retrospective study has been conducted on 152 patients with hip fragility fractures. FCI has been calculated on fractured femur and on the opposite side. The presence of comorbidities, osteoporosis risk factors, vitamin D levels, and BMD have been analyzed for each patient. ::: ::: ::: RESULTS ::: Average values of FCI have been 0.42 for fractured femurs and 0.48 at the opposite side with a statistically significant difference (p = 0.002). Patients with severe hypovitaminosis D had a minor FCI compared to those with moderate deficiency (0.41 vs. 0.46, p < 0.011). 42 patients (27.6%) with osteopenic or normal BMD have presented low values of FCI. ::: ::: ::: DISCUSSION AND CONCLUSION ::: A significant correlation among low values of FCI, comorbidities, severe hypovitaminosis D. and BMD in patients with hip fractures has been found. FCI could be a useful tool to evaluate bone fragility and to predict fracture risk even in the normal and osteopenic BMD patients.


title: A 32-Gb/s backplane transceiver with on-chip AC-coupling and low latency CDR in 32-nm SOI CMOS technology

abstract: This paper describes key design features of a 32 Gb/s 4-tap FFE/15-tap DFE transceiver in 32 nm SOI CMOS which mitigate major sources of degradation in transceiver performance. The transceiver employs a passive feed-forward restore (FFR) scheme in an on-chip AC-coupling network to prevent pattern-dependent baseline wander, a low-latency clock and data recovery (CDR) to improve high-frequency jitter tolerance, and a token-based power management scheme to reduce supply ripple. At 32 Gb/s, the transceiver can equalize a channel with 30 dB of loss at a bit-error rate below 10 ::: -12 ::: while consuming 21 mW/Gbps at 1 V supply and an area of 0.7 mm ::: 2 ::: .


title: Cross Processor Cache Attacks

abstract: Multi-processor systems are becoming the de-facto standard across different computing domains, ranging from high-end multi-tenant cloud servers to low-power mobile platforms. The denser integration of CPUs creates an opportunity for great economic savings achieved by packing processes of multiple tenants or by bundling all kinds of tasks at various privilege levels to share the same platform. This level of sharing carries with it a serious risk of leaking sensitive information through the shared microarchitectural components. Microarchitectural attacks initially only exploited core-private resources, but were quickly generalized to resources shared within the CPU. We present the first fine grain side channel attack that works across processors. The attack does not require CPU co-location of the attacker and the victim. The novelty of the proposed work is that, for the first time the directory protocol of high efficiency CPU interconnects is targeted. The directory protocol is common to all modern multi-CPU systems. Examples include AMD's HyperTransport, Intel's Quickpath, and ARM's AMBA Coherent Interconnect. The proposed attack does not rely on any specific characteristic of the cache hierarchy, e.g. inclusiveness. Note that inclusiveness was assumed in all earlier works. Furthermore, the viability of the proposed covert channel is demonstrated with two new attacks: by recovering a full AES key in OpenSSL, and a full ElGamal key in libgcrypt within the range of seconds on a shared AMD Opteron server.


Automatically Processing Tweets from Gang-Involved Youth: Towards Detecting Loss and Aggression


title: BugRedux: reproducing field failures for in-house debugging

abstract: A recent survey conducted among developers of the Apache, Eclipse, and Mozilla projects showed that the ability to recreate field failures is considered of fundamental importance when investigating bug reports. Unfortunately, the information typically contained in a bug report, such as memory dumps or call stacks, is usually insufficient for recreating the problem. Even more advanced approaches for gathering field data and help in-house debugging tend to collect either too little information, and be ineffective, or too much information, and be inefficient. To address these issues, we present BugRedux, a novel general approach for in-house debugging of field failures. BugRedux aims to synthesize, using execution data collected in the field, executions that mimic the observed field failures. We define several instances of BugRedux that collect different types of execution data and perform, through an empirical study, a cost-benefit analysis of the approach and its variations. In the study, we apply BugRedux to 16 failures of 14 real-world programs. Our results are promising in that they show that it is possible to synthesize in-house executions that reproduce failures observed in the field using a suitable set of execution data.


title: A Study on Using AD5933 to Realize Soft-Sensing Measurement of Conductance and Capacitance

abstract: A conductance and capacitance test system was designed in this paper. AD5933 a new chip for impedance testing was used in it. By designing the test system, and testing the impedance of a model, which is a resistor in parallel with a capacitor, under different frequency (range from1000Hz to 100kHz). The information of impedance, real part, and imaginary part of the model under the frequency points were get. Based on this, the internal relations between conductance, capacitance, impedance, real part and imaginary part were carefully analyzed, using the soft-sensing method established the capacitance support vector machine (SVM) regression model on MATLAB. The regression model is the relation between resistance, impedance and capacitance. The test results showed that the test relative error is small in the capacitance range (0.1 pf~33 pf) and the conductance range (9.05*10-3s~3.8*10-5s), the biggest, smallest and average test error of capacitance are -2.9%, 0.2%, 1.27% respectively. The study showed that using AD5933 and the soft-sensing method to realize the multi-parameter test is feasible.


title: Transmitter Beam Selection in Millimeter-Wave MIMO With In-Band Position-Aiding

abstract: Emerging wireless communication systems will be characterized by a tight coupling between communication and positioning. This is particularly apparent in millimeter-wave (mm-wave) communications, where devices use a large number of antennas and the propagation is well described by geometric channel models. For mm-wave communications, initial access, consisting in the beam selection and alignment of two devices, is challenging and time-consuming in the absence of location information. Conversely, accurate positioning relies on high-quality communication links with proper beam alignment. This paper studies this interaction and proposes a new position-aided beam selection protocol, which considers the problem of joint communication and positioning in scenarios with direct line-of-sight and scattering. Simulation results show significant reductions in latency with respect to a standard protocol.


title: Botnet protocol inference in the presence of encrypted traffic

abstract: Network protocol reverse engineering of botnet command and control (C&C) is a challenging task, which requires various manual steps and a significant amount of domain knowledge. Furthermore, most of today's C&C protocols are encrypted, which prevents any analysis on the traffic without first discovering the encryption algorithm and key. To address these challenges, we present an end-to-end system for automatically discovering the encryption algorithm and keys, generating a protocol specification for the C&C traffic, and crafting effective network signatures. In order to infer the encryption algorithm and key, we enhance state-of-the-art techniques to extract this information using lightweight binary analysis. In order to generate protocol specifications we infer field types purely by analyzing network traffic. We evaluate our approach on three prominent malware families: Sality, ZeroAccess and Ramnit. Our results are encouraging: the approach decrypts all three protocols, detects 97% of fields whose semantics are supported, and infers specifications that correctly align with real protocol specifications.


title: ELF Electric Field Coupling to Dielectric Spheroidal Models of Biological Objects

abstract: The extremely low frequency (ELF) electric field coupling to dielectric spheroidal models of biological objects is studied. Simple expressions are obtained to determine the internal electric field strength induced inside two nonspherical spheroids; a prolate dielectric spheroid and an oblate dielectric spheroid. Certain numerical results are graphically presented for various geometric shapes of the spheroid. The results show that, unlike the symmetric dielectric sphere, the relative position of the spheroid with respect to the orientation of the impressed electric field is an extremely important parameter which considerably affects the electric field coupling.


title: Ultra-wide band CPW to substrate integrated waveguide (SIW) transition based on a U-shaped slot antenna

abstract: A mm-wave transition from coplanar waveguide (CPW) to substrate integrated waveguide (SIW) is presented based on a U-shaped coplanar slot antenna. Using 3D FEM simulations the input impedance exhibited by the structure is analyzed, and simple equations are extracted to design an ultra-wide band transition once the substrate material and the waveguide cut-off frequency are chosen. As an example, a W-band transition on a high resistivity silicon-filled waveguide is implemented: an input matching better than -14.5 dB from 79 to 110 GHz and insertion loss of 0.6 dB at 110 GHz are measured.


title: Landsat-based Remote Sensing of Lake Water Quality Characteristics, Including Chlorophyll and Colored Dissolved Organic Matter (CDOM)

abstract: Abstract Ground-based measurements on 15 Minnesota lakes with wide ranges of optical properties and Landsat TM data from the same lakes were used to evaluate the effect of humic color on satellite-inferred water quality conditions. Color (C440), as measured by absorbance at 440 nm, causes only small biases in estimates of Secchi disk transparency (SDT) from Landsat TM data, except at very high values (> ~ 300 chloroplatinate units, CPU). Similarly, when chlorophyll a (chl a) levels are moderate or high (> 10 μg/L), low-to-moderate levels of humic color have only a small influence on the relationship between SDT and chl a concentration, but it has a pronounced influence at high levels of C440 (e.g., > ~200 CPU). However, deviations from the general chl a-SDT relationship occur at much lower C440 values (~ 60 CPU) when chl a levels are low. Good statistical relationships were found between optical properties of lake water generally associated with algal abundance (SDT, chl a, turbidity) and measured brightn...


title: Relating kindergarten attention to subsequent developmental pathways of classroom engagement in elementary school.

abstract: We examine the relationship between children’s kindergarten attention skills and developmental patterns of classroom engagement throughout elementary school in disadvantaged urban neighbourhoods. Kindergarten measures include teacher ratings of classroom behavior, direct assessments of number knowledge and receptive vocabulary, and parent-reported family characteristics. From grades 1 through 6, teachers also rated children’s classroom engagement. Semi-parametric mixture modeling generated three distinct trajectories of classroom engagement (n = 1369, 50% boys). Higher levels of kindergarten attention were proportionately associated with greater chances of belonging to better classroom engagement trajectories compared to the lowest classroom engagement trajectory. In fact, improvements in kindergarten attention reliably increased the likelihood of belonging to more productive classroom engagement trajectories throughout elementary school, above and beyond confounding child and family factors. Measuring the development of classroom productivity is pertinent because such dispositions represent precursors to mental health, task-orientation, and persistence in high school and workplace behavior in adulthood.


title: Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions

abstract: The large number of potential applications from bridging web data with knowledge bases have led to an increase in the entity linking research. Entity linking is the task to link entity mentions in text with their corresponding entities in a knowledge base. Potential applications include information extraction, information retrieval, and knowledge base population. However, this task is challenging due to name variations and entity ambiguity. In this survey, we present a thorough overview and analysis of the main approaches to entity linking, and discuss various applications, the evaluation of entity linking systems, and future directions.


title: How Transferable Are CNN-Based Features for Age and Gender Classification?

abstract: Age and gender are complementary soft biometric traits for face recognition. Successful estimation of age and gender from facial images taken under real-world conditions can contribute improving the identification results in the wild. In this study, in order to achieve robust age and gender classification in the wild, we have benefited from Deep Convolutional Neural Networks based representation. We have explored transferability of existing deep convolutional neural network (CNN) models for age and gender classification. The generic AlexNet-like architecture and domain specific VGG-Face CNN model are employed and fine-tuned with the Adience dataset prepared for age and gender classification in uncontrolled environments. In addition, task specific GilNet CNN model has also been utilized and used as a baseline method in order to compare with transferred models. Experimental results show that both transferred deep CNN models outperform the GilNet CNN model, which is the state-of-the-art age and gender classification approach on the Adience dataset, by an absolute increase of 7% and 4.5% in accuracy, respectively. This outcome indicates that transferring a deep CNN model can provide better classification performance than a task specific CNN model, which has a limited number of layers and trained from scratch using a limited amount of data as in the case of GilNet. Domain specific VGG-Face CNN model has been found to be more useful and provided better performance for both age and gender classification tasks, when compared with generic AlexNet-like model, which shows that transfering from a closer domain is more useful.


title: Short-Step Chebyshev Impedance Transformers

abstract: Impedance transforming networks are described which consist of short lengths of relatively high impedance transmission line alternating with short lengths of relatively low impedance line. The sections of transmission line are all exactly the same length (except for corrections for fringing capacitances), and the lengths of the line sections are typically short compared to a quarter wavelength throughout the operating band of the transformer. Tables of designs are presented which give exactly Chebyshev transmission characteristics between resistive terminations having ratios ranging from 1.5 to 10, and for fractional bandwidths ranging from 0.10 to 1.20. These impedance-transforming networks should have application where very compact transmission-line or dielectric-layer impedance transformers are desired.


title: Human Pose Estimation by Exploiting Spatial and Temporal Constraints in Body-Part Configurations

abstract: We present an algorithm for estimating a sequence of articulated upper-body human pose in unconstrained videos. Most previous work often fails to locate forearms in those video scenes suffering from illumination varieties, background clutter, camera shake, or occlusion. In order to deal with such intractable cases, we propose a novel algorithm for addressing the problem of certain body parts localization. The proposed approach can be roughly divided into two steps: first, a spatial model is designed to capture the high-order relationship between adjacent parts and meanwhile to generate a set of configurations in each frame under the temporal context constraint; second, a competitive method is presented to select the best body parts among diverse pose configurations. In this paper, the proposed algorithm focuses on the unconstrained video scenes and improves the detection precision of certain body parts with high degree of freedom. Moreover, the proposed algorithm can be well applied to a very challenging dataset named Movies. Experimental results show that the proposed algorithm can dramatically improve performance compared with those related algorithms on two benchmark datasets (MPII and SHPED datasets) and on our Movies dataset.


title: Bubbles of Trust: a decentralized Blockchain-based authentication system for IoT

abstract: Abstract There is no doubt that Internet of Things (IoT) occupy a very important role in our daily lives. Indeed, numerous objects that we use every time, are being equipped with electronic devices and protocol suites in order to make them interconnected and connected to the Internet. In IoT, things process and exchange data without human intervention. Therefore, because of this full autonomy, these entities need to recognize and authenticate each other as well as to ensure the integrity of their exchanged data. Otherwise, they will be the target of malicious users and malicious use. Due to the size and other features of IoT, it is almost impossible to create an efficient centralized authentication system. To remedy this limit, in this paper, we propose an original decentralized system called bubbles of trust , which ensures a robust identification and authentication of devices. Furthermore, it protects the data integrity and availability. To achieve such a goal, our approach relies on the security advantages provided by blockchains, and serves to create secure virtual zones ( bubbles ) where things can identify and trust each other. We also provided a real implementation of our mechanism using the C++ language and Ethereum blockchain. The obtained results prove its ability to satisfy IoT security requirements, its efficiency, and its low cost.


title: Fuzzy logic control of buck converter for photo voltaic emulator

abstract: This paper analyses the digital simulation of a buck converter to emulate the photovoltaic (PV) system with focus on fuzzy logic control of buck converter. A PV emulator is a DC-DC converter (buck converter in the present case) having same electrical characteristics as that of a PV panel. The emulator helps in the real analysis of PV system in an environment where using actual PV systems can produce inconsistent results due to variation in weather conditions. The paper describes the application of fuzzy algorithms to the control of dynamic processes. The complete system is modelled in MATLAB® Simulink SimPowerSystem software package. The results obtained from the simulation studies are presented and the steady state and dynamic stability of the PV emulator system is discussed.


title: Fingerprint-Based Fuzzy Vault: Implementation and Performance

abstract: Reliable information security mechanisms are required to combat the rising magnitude of identity theft in our society. While cryptography is a powerful tool to achieve information security, one of the main challenges in cryptosystems is to maintain the secrecy of the cryptographic keys. Though biometric authentication can be used to ensure that only the legitimate user has access to the secret keys, a biometric system itself is vulnerable to a number of threats. A critical issue in biometric systems is to protect the template of a user which is typically stored in a database or a smart card. The fuzzy vault construct is a biometric cryptosystem that secures both the secret key and the biometric template by binding them within a cryptographic framework. We present a fully automatic implementation of the fuzzy vault scheme based on fingerprint minutiae. Since the fuzzy vault stores only a transformed version of the template, aligning the query fingerprint with the template is a challenging task. We extract high curvature points derived from the fingerprint orientation field and use them as helper data to align the template and query minutiae. The helper data itself do not leak any information about the minutiae template, yet contain sufficient information to align the template and query fingerprints accurately. Further, we apply a minutiae matcher during decoding to account for nonlinear distortion and this leads to significant improvement in the genuine accept rate. We demonstrate the performance of the vault implementation on two different fingerprint databases. We also show that performance improvement can be achieved by using multiple fingerprint impressions during enrollment and verification.


title: TouchMover: actuated 3D touchscreen with haptic feedback

abstract: This paper presents the design and development of a novel visual+haptic device that co-locates 3D stereo visualization, direct touch and touch force sensing with a robotically actuated display. Our actuated immersive 3D display, called TouchMover, is capable of providing 1D movement (up to 36cm) and force feedback (up to 230N) in a single dimension, perpendicular to the screen plane. In addition to describing the details of our design, we showcase how TouchMover allows the user to: 1) interact with 3D objects by pushing them on the screen with realistic force feedback, 2) touch and feel the contour of a 3D object, 3) explore and annotate volumetric medical images (e.g., MRI brain scans) and 4) experience different activation forces and stiffness when interacting with common 2D on-screen elements (e.g., buttons). We also contribute the results of an experiment which demonstrates the effectiveness of the haptic output of our device. Our results show that people are capable of disambiguating between 10 different 3D shapes with the same 2D footprint by touching alone and without any visual feedback (85% recognition rate, 12 participants).


title: Experiments with crowdsourced re-annotation of a POS tagging data set

abstract: Crowdsourcing lets us collect multiple annotations for an item from several annotators. Typically, these are annotations for non-sequential classification tasks. While there has been some work on crowdsourcing named entity annotations, researchers have largely assumed that syntactic tasks such as part-of-speech (POS) tagging cannot be crowdsourced. This paper shows that workers can actually annotate sequential data almost as well as experts. Further, we show that the models learned from crowdsourced annotations fare as well as the models learned from expert annotations in downstream tasks.


DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents


title: Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms

abstract: A completely automated algorithm to detect poor-quality electrocardiograms (ECGs) is described. The algorithm is based on both novel and previously published signal quality metrics, originally designed for intensive care monitoring. The algorithms have been adapted for use on short (5–10 s) single- and multi-lead ECGs. The metrics quantify spectral energy distribution, higher order moments and inter-channel and inter-algorithm agreement. Seven metrics were calculated for each channel (84 features in all) and presented to either a multi-layer perceptron artificial neural network or a support vector machine (SVM) for training on a multiple-annotator labelled and adjudicated training dataset. A single-lead version of the algorithm was also developed in a similar manner. Data were drawn from the PhysioNet Challenge 2011 dataset where binary labels were available, on 1500 12-lead ECGs indicating whether the entire recording was acceptable or unacceptable for clinical interpretation. We re-annotated all the leads in both the training set (1000 labelled ECGs) and test dataset (500 12-lead ECGs where labels were not publicly available) using two independent annotators, and a third for adjudication of differences. We found that low-quality data accounted for only 16% of the ECG leads. To balance the classes (between high and low quality), we created extra noisy data samples by adding noise from PhysioNet’s noise stress test database to some of the clean 12-lead ECGs. No data were shared between training and test sets. A classification accuracy of 98% on the training data and 97% on the test data were achieved. Upon inspection, incorrectly classified data were found to be borderline cases which could be classified either way. If these cases were more consistently labelled, we expect our approach to achieve an accuracy closer to 100%.


title: Seizure classification in EEG signals utilizing Hilbert-Huang transform

abstract: BackgroundClassification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals.MethodDiscrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering.ResultsThe t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use.ConclusionAn original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.


title: Reinventing discovery learning: a field-wide research program

abstract: Whereas some educational designers believe that students should learn new concepts through explorative problem solving within dedicated environments that constrain key parameters of their search and then support their progressive appropriation of empowering disciplinary forms, others are critical of the ultimate efficacy of this discovery-based pedagogical philosophy, citing an inherent structural challenge of students constructing historically achieved conceptual structures from their ingenuous notions. This special issue presents six educational research projects that, while adhering to principles of discovery-based learning, are motivated by complementary philosophical stances and theoretical constructs. The editorial introduction frames the set of projects as collectively exemplifying the viability and breadth of discovery-based learning, even as these projects: (a) put to work a span of design heuristics, such as productive failure, surfacing implicit know-how, playing epistemic games, problem posing, or participatory simulation activities; (b) vary in their target content and skills, including building electric circuits, solving algebra problems, driving safely in traffic jams, and performing martial-arts maneuvers; and (c) employ different media, such as interactive computer-based modules for constructing models of scientific phenomena or mathematical problem situations, networked classroom collective “video games,” and intercorporeal master–student training practices. The authors of these papers consider the potential generativity of their design heuristics across domains and contexts.


title: Qualitative Content Analysis: A Guide to Paths not Taken

abstract: Counting codes makes qualitative content analysis a controversial approach to analyzing textual data. Several decades ago, mainstream content analysis rejected qualitative content analysis on the grounds that it was not sufficiently quantitative; today, it is often charged with not being sufficiently qualitative. This article argues that qualitative content analysis is distinctively qualitative in both its approach to coding and its interpretations of counts from codes. Rather than argue over whether to do qualitative content analysis, researchers must make informed decisions about when to use it in analyzing qualitative data.


title: Simple Test and Modeling of RFID Tag Backscatter

abstract: We consider here worst-case analysis of backscatter from passive radio frequency identification (RFID) tags. The basis is a figure of merit “B” to relate link power at reader ports to tag circuit parameters. A minimum bound for received monostatic backscatter can be determined by inspection from measured B. The bound is general for narrowband signals in any causal linear propagation. For an assembled tag, this minimum varies only with reader transmit power, tag antenna tuning, and chip power sensitivity of different commands. To validate this model, we propose a backscatter calibration device to enable measurements with estimated 0.5 dB uncertainty. We then demonstrate how the minimum bound can inform reader sensitivity specification to help ensure reliable inventory performance.


Gated Convolutional Neural Network for Sentence Matching


title: Improving Question Answering by Commonsense-Based Pre-Training

abstract: Although neural network approaches achieve remarkable success on a variety of NLP tasks, many of them struggle to answer questions that require commonsense knowledge. We believe the main reason is the lack of commonsense connections between concepts. To remedy this, we provide a simple and effective method that leverages external commonsense knowledge base such as ConceptNet. We pre-train direct and indirect relational functions between concepts, and show that these pre-trained functions could be easily added to existing neural network models. Results show that incorporating commonsense-based function improves the state-of-the-art on three question answering tasks that require commonsense reasoning. Further analysis shows that our system discovers and leverages useful evidence from an external commonsense knowledge base, which is missing in existing neural network models and help derive the correct answer.


title: HIGHER EDUCATION REFORM IN LITHUANIA WITH EMPHASIS ON LITHUANIAN EDUCATIONAL STRATEGY

abstract: Reforms of higher education are influenced both directly and indirectly by global social, economical, cultural and demographical changes in the world and European countries. High schools open to innovations must react to the changes and challenges in their surrounding, as well as get involved into competition in the field of studies and scientific research. Contemporary trends in the process of education management in various countries and regions show that the higher education becomes crucial factor making huge influence on different regions of the world. More and more people come to understanding of modern competence and qualification necessity for innovative knowledge based economy. Development of higher education in Lithuania has been a complicated and multi-valued process very closely related to other dimensions of the Lithuanian state development – political regime and historical situation, economical and social structure of the country, ideological and cultural changes paradigms.


title: Chiral projected entangled-pair state with topological order

abstract: We show that projected entangled-pair states (PEPS) can describe chiral topologically ordered phases. For that, we construct a simple PEPS for spin-1/2 particles in a two-dimensional lattice. We reveal a symmetry in the local projector of the PEPS that gives rise to the global topological character. We also extract characteristic quantities of the edge conformal field theory using the bulk-boundary correspondence.


title: A FRAMEWORK OF SECURE BIOMETRIC BASED ONLINE EXAM AUTHENTICATION: AN ALTERNATIVE TO TRADITIONAL EXAM

abstract: In the past fifteen years the use of Internet technologies has been substantially growing for delivery of educational content and online learn- ing has become increasingly popular and evolved. Online examination is an integral and vital component of online learning. Student assessment in online learning is largely submitted remotely without any face-to-face interaction and therefore, student authentication is widely seen as one of the major challenges in online examination. This study aims to investigate potential threats to student authentication in the online examinations and analyzing the benefits and limitations of the existing authentication approaches. Online examinations pose a unique problem, in that it can be very difficult to provide true user authentication. Due to the inherent anonymity of being online, compared to taking an examination in a classroom environment, students may attempt to artificially boost their scores in online examinations by having another individual take the exam for them, which a typical user/password au- thentication scheme cannot detect. For this purpose, we propose the Framework of Secured Biometric student authentication in Online Examinations. This framework uses a multi-modal authentication approach to secure online examination. The solution comprises of two layers of authentication i.e. student biometric authentication and Knowledge based authentication. In this paper, we propose a framework that provides security to improve on-line examination by utilizing technologies such as biometric authentication based Keystroke Dynamics. This paper attempts to address this important prob- lem by proposing a theoretical framework that incorporates available biometric authentication technologies in conjunction with Knowledge based authen- tication. Discussions on future research and possible implications of the proposed theoretical framework for practice are provided.


title: KP-Miner: A Simple System for Effective Keyphrase Extraction

abstract: Automatic key phrase extraction has many important applications including but not limited to summarization, cataloging/indexing, feature extraction for clustering and classification, and data mining. This paper presents a simple, yet effective algorithm (KP-Miner) for achieving this task. The result of an experiment carried out to investigate the effectiveness of this algorithm is also presented. In this experiment the devised algorithm is applied to six different datasets consisting of 481 documents. The results are then compared to two existing sophisticated machine learning based automatic keyphrase extraction systems. The results of this experiment show that the devised algorithm is comparable to both systems.


title: Feel Effects: Enriching Storytelling with Haptic Feedback

abstract: Despite a long history of use in communication, haptic feedback is a relatively new addition to the toolbox of special effects. Unlike artists who use sound or vision, haptic designers cannot simply access libraries of effects that map cleanly to media content, and they lack even guiding principles for creating such effects. In this article, we make progress toward both capabilities: we generate a foundational library of usable haptic vocabulary and do so with a methodology that allows ongoing additions to the library in a principled and effective way. We define a feel effect as an explicit pairing between a meaningful linguistic phrase and a rendered haptic pattern. Our initial experiment demonstrates that users who have only their intrinsic language capacities, and no haptic expertise, can generate a core set of feel effects that lend themselves via semantic inference to the design of additional effects. The resulting collection of more than 40 effects covers a wide range of situations (including precipitation, animal locomotion, striking, and pulsating events) and is empirically shown to produce the named sensation for the majority of our test users in a second experiment. Our experiments demonstrate a unique and systematic approach to designing a vocabulary of haptic sensations that are related in both the semantic and parametric spaces.


title: Risk Aversion and Expected-Utility Theory: A Calibration Theorem

abstract: Within the expected-utility framework, the only explanation for risk aversion is that the utility function for wealth is concave: A person has lower marginal utility for additional wealth when she is wealthy than when she is poor. This paper provides a theorem showing that expected-utility theory is an utterly implausible explanation for appreciable risk aversion over modest stakes: Within expected-utility theory, for any concave utility function, even very little risk aversion over modest stakes implies an absurd degree of risk aversion over large stakes. Illustrative calibrations are provided.


title: Suppression of the Primary Antibody Plaque Response of Mice Following Infection with Friend Disease Virus.∗

abstract: SummaryInfection of mice with Friend Disease Virus prior to or simultaneous with immunization with sheep red blood cells resulted in a marked suppression of appearance of antibody forming cells, as determined by a localized hemolytic plaque technique in agar gel. Infection with virus had a marked suppressive effect on the number of 19S hemolysin forming cells in mouse spleen tissue at the peak of the expected immune response and during a period of two weeks or longer thereafter. Appearance of low efficiency 7S hemolysin forming cells late in the immune response, following a single injection of sheep red cells. Was also depressed in infected mice. Animals infected 3 days to a week or longer prior to immunization had the lowest number of detectable 19S or 7S antibody-forming cells. The number of plaque forming cells per million leukocytes tested was most suppressed in those mice which had obvious splenomegaly due to virus infection.


title: A technique for orthogonal frequency division multiplexing frequency offset correction

abstract: This paper discusses the effects of frequency offset on the performance of orthogonal frequency division multiplexing (OFDM) digital communications. The main problem with frequency offset is that it introduces interference among the multiplicity of carriers in the OFDM signal. It is shown, and confirmed by simulation, that to maintain signal-to-interference ratios of 20 dB or greater for the OFDM carriers, offset is limited to 4% or less of the intercarrier spacing. Next, the paper describes a technique to estimate frequency offset using a repeated data symbol. A maximum likelihood estimation (MLE) algorithm is derived and its performance computed and compared with simulation results. Since the intercarrier interference energy and signal energy both contribute coherently to the estimate, the algorithm generates extremely accurate estimates even when the offset is far too great to demodulate the data values. Also, the estimation error depends only on total symbol energy so it is insensitive to channel spreading and frequency selective fading. A strategy is described for initial acquisition in the event of uncertainty in the initial offset that exceeds 1/2 the carrier spacing, the limit of the MLE algorithm. >


Adaptive Robust Bank-to-Turn Missile Autopilot Design Using Neural Networks


title: SMART Electronic Legal Discovery Via Topic Modeling

abstract: Electronic discovery is an interesting subproblem of information retrieval in which one identifies documents that are potentially relevant to issues and facts of a legal case from an electronically stored document collection (a corpus). In this paper, we consider representing documents in a topic space using the well-known topic models such as latent Dirichlet allocation and latent semantic indexing, and solving the information retrieval problem via finding document similarities in the topic space rather doing it in the corpus vocabulary space. We also develop an iterative SMART ranking and categorization framework including human-in-the-loop to label a set of seed (training) documents and using them to build a semi-supervised binary document classification model based on Support Vector Machines. To improve this model, we propose a method for choosing seed documents from the whole population via an active learning strategy. We report the results of our experiments on a real dataset in the electronic discovery domain.


title: GRVI Phalanx: A Massively Parallel RISC-V FPGA Accelerator Accelerator

abstract: GRVI is an FPGA-efficient RISC-V RV32I soft processor. Phalanx is a parallel processor and accelerator array framework. Groups of processors and accelerators form shared memory clusters. Clusters are interconnected with each other and with extreme bandwidth I/O and memory devices by a Hoplite NOC with 300-bit links. An example Kintex UltraScale 040 system has 400 RISC-V cores, peak throughput of 100,000 MIPS, peak shared memory bandwidth of 600 GB/s, NOC bisection bandwidth of 700 Gb/s, and uses 12-17 W.


title: Discovering and exploiting 3D symmetries in structure from motion

abstract: Many architectural scenes contain symmetric or repeated structures, which can generate erroneous image correspondences during structure from motion (Sfm) computation. Prior work has shown that the detection and removal of these incorrect matches is crucial for accurate and robust recovery of scene structure. In this paper, we point out that these incorrect matches, in fact, provide strong cues to the existence of symmetries and structural regularities in the unknown 3D structure. We make two key contributions. First, we propose a method to recover various symmetry relations in the structure using geometric and appearance cues. A set of structural constraints derived from the symmetries are imposed within a new constrained bundle adjustment formulation, where symmetry priors are also incorporated. Second, we show that the recovered symmetries enable us to choose a natural coordinate system for the 3D structure where gauge freedom in rotation is held fixed. Furthermore, based on the symmetries, 3D structure completion is also performed. Our approach significantly reduces drift through ”structural” loop closures and improves the accuracy of reconstructions in urban scenes.


SOLILOQUY : A Cautionary Tale


title: Complete coverage path planning algorithm for known 2d environment

abstract: Path-planning is an important primitive for autonomous mobile robots that lets robots find the optimal path between two points. Typical path-planning algorithms deal with finding an optimized path from start to end using a map of the environment and the robot to be aware of its location with respect to the map. Complete coverage path planning algorithm, on the other hand, is used in application where an autonomous mobile robot is required to visit all the points at least once in the given environment size with known obstacles. In this paper, a complete coverage path planning algorithm is developed and tested on a actual hardware. The algorithm is development for a known 2D environment with static obstacles with an optimization on minimal coverage time. The scaling of computational time with increasing map sizes and simulation results for different types of obstacle geometries are also presented. This kind of algorithm can be used for area inspection, cleaning robots, painter robot, land mine detectors, lawn mowers, agricultural field machines etc.


title: A data driven approach to understanding the organization of high-level visual cortex

abstract: The neural representation in scene-selective regions of human visual cortex, such as the PPA, has been linked to the semantic and categorical properties of the images. However, the extent to which patterns of neural response in these regions reflect more fundamental organizing principles is not yet clear. Existing studies generally employ stimulus conditions chosen by the experimenter, potentially obscuring the contribution of more basic stimulus dimensions. To address this issue, we used a data-driven approach to describe a large database of scenes (>100,000 images) in terms of their visual properties (orientation, spatial frequency, spatial location). K-means clustering was then used to select images from distinct regions of this feature space. Images in each cluster did not correspond to typical scene categories. Nevertheless, they elicited distinct patterns of neural response in the PPA. Moreover, the similarity of the neural response to different clusters in the PPA could be predicted by the similarity in their image properties. Interestingly, the neural response in the PPA was also predicted by perceptual responses to the scenes, but not by their semantic properties. These findings provide an image-based explanation for the emergence of higher-level representations in scene-selective regions of the human brain.


title: Big Data: Survey, Technologies, Opportunities, and Challenges

abstract: Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.


title: Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model)

abstract: We examine Memory Networks for the task of question answering (QA), under common real world scenario where training examples are scarce and under weakly supervised scenario, that is only extrinsic labels are available for training. We propose extensions for the Dynamic Memory Network (DMN), specifically within the attention mechanism, we call the resulting Neural Architecture as Dynamic Memory Tensor Network (DMTN). Ultimately, we see that our proposed extensions results in over 80% improvement in the number of task passed against the baselined standard DMN and 20% more task passed compared to state-of-the-art End-to-End Memory Network for Facebook's single task weakly trained 1K bAbi dataset.


title: A review of single-phase on-board integrated battery charging topologies for electric vehicles

abstract: The paper provides an extensive overview of single-phase on-board integrated battery chargers for electric vehicles (EVs). Although commercial EVs are still to be equipped with integrated chargers, a multitude of topologies have been proposed for integration. Therefore, the need for classification arises. This paper aims to give an overview of topologies based on functionality of their integrated components. Moreover, it attempts to provide an extensive analysis of their operating principles. All the topologies are classified into three major groups. At first, topologies in which only the converter is integrated are considered. This is followed by configurations based on switched reluctance machines (SRM) in which both the converter and the machine are integrated. At last, the integration of both converter and induction machine (IM) or permanent magnet machine (PM) is elaborated within the third group. Here an example of obtained experimental results is also given. Finally, a short quantitative comparison of the topologies is provided in a table format.


A motivational approach to self: Integration in personality.


title: CCOA: Cloud Computing Open Architecture

abstract: Cloud Computing is evolving as a key computing platform for sharing resources that include infrastructures, software, applications, and business processes. Virtualization is a core technology for enabling cloud resource sharing. However, most existing Cloud Computing platforms have not formally adopted the service-oriented architecture (SOA) that would make them more flexible, extensible, and reusable. By bridging the power of SOA and virtualization in the context of Cloud Computing ecosystem, this paper presents seven architectural principles and derives ten interconnected architectural modules to form a reusable and customizable Cloud Computing Open Architecture (CCOA). Two case studies on Infrastructure and Business Cloud are used to deliver business and practical value of infrastructure and business process provisioning services over the Internet. We also present some potential value-added services of the proposed CCOA to guide strategic planning and other consulting practices of Cloud Computing.


The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction


title: Face, hairstyle and clothing colour de-identification in video sequences

abstract: The authors introduce a system for person de-identification in video data that de-identifies biometric and non-biometric features, namely faces, hairstyles and clothing colours. The authors’ system detects human faces and silhouettes in the input video and replaces the detected faces with random synthesised faces obtained using a deep convolutional generative adversarial network. Alternative hairstyles are rendered over the synthesised faces, and the human silhouette is recoloured so that skin hues are preserved and clothing hues are altered. Through the use of artificially synthesised faces that look realistic, they ensure that the de-identified image looks natural and at the same time avoid ethical and legal considerations present when using real face images as replacement faces. As they address non-biometric feature de-identification, their system offers a considerably higher level of privacy protection than commonly employed solutions that use simple image processing techniques such as blurring. Qualitative and quantitative evaluation suggests that their system produces de-identified images that look natural, at the same time being resistant to re-identification attacks.


title: Hyperbolic Feature-based Sarcasm Detection in Tweets: A Machine Learning Approach

abstract: In recent times, sarcasm analysis has been one of the toughest challenges in Natural Language Processing (NLP). The property of sarcasm that makes it difficult to analyze and detect is the gap between its literal and intended meaning. Detecting sarcastic sentiment in the domain of social media such as Facebook, Twitter, online blogs, reviews, etc. has become an essential task as they influence every business organization. In this article, a hyperbolic feature-based sarcasm detector for Twitter data is proposed. The hyperbolic features consist of intensifiers and interjections of the text. The performance of the proposed system is analyzed using several standard machine learning approaches namely, Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and AdaBoost. The system attains an accuracy (%) of 75.12, 80.27, 80.67, 80.79, and 80.07 using NB, DT, SVM, RF, and AdaBoost respectively.


title: PointCNN: Convolution On $\mathcal{X}$-Transformed Points

abstract: We present a simple and general framework for feature learning from point clouds. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. images). However, point clouds are irregular and unordered, thus directly convolving kernels against features associated with the points, will result in desertion of shape information and variance to point ordering. To address these problems, we propose to learn an $\mathcal{X}$-transformation from the input points, to simultaneously promote two causes. The first is the weighting of the input features associated with the points, and the second is the permutation of the points into a latent and potentially canonical order. Element-wise product and sum operations of the typical convolution operator are subsequently applied on the $\mathcal{X}$-transformed features. The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.


title: On semantic image segmentation using deep convolutional neural network with shortcuts and easy class extension

abstract: In this paper we examine the use of deep convolutional neural networks for semantic image segmentation, which separates an input image into multiple regions corresponding to predefined object classes. We use an encoder-decoder structure and aim to improve it in convergence speed and segmentation accuracy by adding shortcuts between network layers. Besides, we investigate how to extend an already trained model to other new object classes. We propose a new strategy for class extension with only little training data and class labels. In the experiments we use two street scene datasets to demonstrate the strength of shortcuts, to study the contextual information encoded in the learned model and to show the effectiveness of our class extension method.