title: Continuous Authentication on PCs using Artificial Immune System

abstract: Most of the existing systems require the users to provide a password or biometric data to authenticate themselves. However, as long as the user is still active in the system, there are no mechanisms to check whether the user is the genuine one or not. For the most computer system, the user identity is verified using Static Authentication (SA) only during the initial login process. In this paper, we focus on Continuous Authentication (CA) to continuously check the identity of the current user, based on every user action performed. We proposed to apply Artificial Immune System (AIS) Negative Selection (NS) for CA. Our proposed system was validated with a unique biometric dataset. The dataset was collected in a completely uncontrolled environment from 24 users. In this research, a combination of mouse and keystroke user behavior has been used. Our proposed system, shows that AIS was correctly able to continuously authenticate the users with high accuracy. The best achieved accuracies for 10 NS runs, where the number of generated detectors is 100, 200, 300, and 600, are 97.74%, 99.01%, 99.275%, and 99.7% respectively.


title: Corn Disease Identification from Leaf Images Using Convolutional Neural Networks

abstract: Plant diseases are one of the major factors that can negatively impact crop yields. Therefore, we want to create an automated system that can identify plant diseases as easily as possible. Our research goal is to help crop growers who might not be experts in plant pathology be able to identify diseases as soon as they occur in order to minimize the spread of disease and the damage to the crops. We propose a very light-weight convolutional neural network architecture that is trained to classify images of corn leaves into one of the four categories: common rust disease, northern leaf blight disease, gray leaf spot disease, or healthy. The dataset used is the PlantVillage dataset taken from plantvillage.org. The dataset consists of 8506 images of corn leaves from 4 categories. We evaluated our model using 5-fold cross-validation, and we achieves an accuracy of 97.09 % on average. The result of our model is slightly worse than the performance of other well-known CNNs architectures, but the complexity of our architecture is much lower, making it more suitable for real-time inferences on resource-constrained devices such as smartphones.


title: Low-cost traffic analysis of Tor

abstract: Tor is the second generation onion router supporting the anonymous transport of TCP streams over the Internet. Its low latency makes it very suitable for common tasks, such as Web browsing, but insecure against traffic-analysis attacks by a global passive adversary. We present new traffic-analysis techniques that allow adversaries with only a partial view of the network to infer which nodes are being used to relay the anonymous streams and therefore greatly reduce the anonymity provided by Tor. Furthermore, we show that otherwise unrelated streams can be linked back to the same initiator Our attack is feasible for the adversary anticipated by the Tor designers. Our theoretical attacks are backed up by experiments performed on the deployed, albeit experimental, Tor network. Our techniques should also be applicable to any low latency anonymous network. These attacks highlight the relationship between the field of traffic-analysis and more traditional computer security issues, such as covert channel analysis. Our research also highlights that the inability to directly observe network links does not prevent an attacker from performing traffic-analysis: the adversary can use the anonymising network as an oracle to infer the traffic load on remote nodes in order to perform traffic-analysis.


title: How Does Batch Normalization Help Optimization?

abstract: Batch Normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks (DNNs). Despite its pervasiveness, the exact reasons for BatchNorm's effectiveness are still poorly understood. The popular belief is that this effectiveness stems from controlling the change of the layers' input distributions during training to reduce the so-called "internal covariate shift". In this work, we demonstrate that such distributional stability of layer inputs has little to do with the success of BatchNorm. Instead, we uncover a more fundamental impact of BatchNorm on the training process: it makes the optimization landscape significantly smoother. This smoothness induces a more predictive and stable behavior of the gradients, allowing for faster training.


title: Learning to Represent Edits

abstract: We introduce the problem of learning distributed representations of edits. By combining a"neural editor"with an"edit encoder", our models learn to represent the salient information of an edit and can be used to apply edits to new inputs. We experiment on natural language and source code edit data. Our evaluation yields promising results that suggest that our neural network models learn to capture the structure and semantics of edits. We hope that this interesting task and data source will inspire other researchers to work further on this problem.


title: SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis

abstract: Synthesizing realistic images from human drawn sketches is a challenging problem in computer graphics and vision. Existing approaches either need exact edge maps, or require a database to retrieve images from. In this work, we propose a novel Generative Adversarial Network (GAN) approach that synthesizes realistic looking images from 50 categories including motorcycles, horses and couches. We demonstrate a data augmentation technique for sketches which is fully automatic, and we show that the augmented data is helpful to our task. We introduce a new building block suit for both the generator and discriminator which improves the information flow and utilizes input images at multiple scales. Compared to state-of-the-art image translation methods, our approach generates more realistic images and achieves significantly higher Inception Scores.


title: Contextual Multi-Armed Bandits

abstract: D´ avid P´ al Abstract We study contextual multi-armed bandit prob- lems where the context comes from a metric space and the payoff satisfies a Lipschitz condi- tion with respect to the metric. Abstractly, a con- textual multi-armed bandit problem models a sit- uation where, in a sequence of independent trials, an online algorithm chooses, based on a given context (side information), an action from a set of possible actions so as to maximize the total pay- off of the chosen actions. The payoff depends on both the action chosen and the context. In con- trast, context-free multi-armed bandit problems, a focus of much previous research, model situa- tions where no side information is available and the payoff depends only on the action chosen. Our problem is motivated by sponsored web search, where the task is to display ads to a user of an Internet search engine based on her search query so as to maximize the click-through rate (CTR) of the ads displayed. We cast this prob- lem as a contextual multi-armed bandit problem where queries and ads form metric spaces and the payoff function is Lipschitz with respect to both the metrics. For any > 0 we present an algorithm with regret O(T a+b+1 a+b+2+ ) where a;b are the covering dimensions of the query space and the ad space respectively. We prove a lower bound ( T ~ a+~+1 ~ a+~+2 ) for the regret of any algo- rithm where ~ ~ are packing dimensions of the query spaces and the ad space respectively. For finite spaces or convex bounded subsets of Eu- clidean spaces, this gives an almost matching up- per and lower bound.


title: Using Bayesian Networks to Detect Students ’ Learning Styles in a Web-based education system

abstract: Students are characterized by different learning styles, focusing on different types of information and processing this information in different ways. One of the goals of a Web-based education system is that all the students can learn despite their different learning styles. To achieve this goal we have to detect how students learn. We propose the use of Bayesian Networks to detect the learning style of a student in a Web-based education system. The information obtained can be then used by an intelligent agent to provide personalized assistance to students, delivering teaching material that best fits students’ learning styles.


title: Resource scheduling for a network of communications antennas

abstract: This paper describes the Demand Access Network Scheduler (DANS) system for automatically scheduling and rescheduling resources for a network of communications antennas. DANS accepts a baseline schedule and supports rescheduling of antenna and subsystem resources to satisfy tracking goals in the event of: changing track requests, equipment outages, and inclement weather.


title: An Introduction to the Conjugate Gradient Method Without the Agonizing

abstract: The Conjugate Gradient Method is the most prominent iterative method for solving sparse systems of linear equations. Unfortunately, many textbook treatments of the topic are written so that even their own authors would be mystified, if they bothered to read their own writing. For this reason, an understanding of the method has been reserved for the elite brilliant few who have painstakingly decoded the mumblings of their forebears. Nevertheless, the Conjugate Gradient Method is a composite of simple, elegant ideas that almost anyone can understand. Of course, a reader as intelligent as yourself will learn them almost effortlessly. The idea of quadratic forms is introduced and used to derive the methods of Steepest Descent, Conjugate Directions, and Conjugate Gradients. Eigenvectors are explained and used to examine the convergence of the Jacobi Method, Steepest Descent, and Conjugate Gradients. Other topics include preconditioning and the nonlinear Conjugate Gradient Method. I have taken pains to make this article easy to read. Sixty-two illustrations are provided. Dense prose is avoided. Concepts are explained in several different ways. Most equations are coupled with an intuitive interpretation.


title: A survey of wireless sensor network security in the context of Internet of Things

abstract: Today, the common vision of smart components is usually based on the concept of the Internet of Things (IoT). Intelligent infrastructures combine sensor networks, network connectivity and software to oversee and analyze complex systems to identify inefficiencies and inform operational decision-making. Wireless Sensor nodes collect operational information over time and provide real-time data on current conditions such as (volcano activities, disaster parameters in general). The security of wireless sensor networks in the world of the Internet of Things is a big challenge, since there are several types of attacks against different layers of OSI model, in their goal is falsified the values of the detected parameters.


Chainer : a Next-Generation Open Source Framework for Deep Learning


title: An Enhanced Classification Technique for Fetal ECG Signal Separation to Diagnose Fetal Heart Diseases

abstract: The separation tool of Fetal ECG is complicated and very important in medical diagnosis during pregnancy. This work proposes the separation and enhanced classification of fetal ECG signals from abdominal ECG signals to find heart diseases of the fetal. In this paper MULTI-COMBI based blind source separation technique is used to separate the fetal ECG signals. The separated ECG signals have different features, which are extracted using the morphological feature extraction method. These extracted features are used for classification by Enhanced Navie Bayes Classification technique. This classifier classified into five different classes of disease. The entire work is implemented in MATLAB. The Simulated results of BSS method gives low PSNR and SIR values and the enhanced classifier gives the accuracy as 96.56%, Sensitivity as 89.56%, and Specificity as 97.54%.


title: Dermoscopic characterization of cutaneous lymphomas: a pilot survey

abstract: BACKGROUND ::: While substantial dermoscopic analysis of melanocytic lesions has been performed, dermoscopic characterization of cutaneous lymphoid proliferations has been limited. Cutaneous lymphoma, particularly early mycosis fungoides (MF) and its variants, is often challenging to clinically and pathologically distinguish from inflammatory processes of the skin. This study aimed to survey the dermoscopic findings of cutaneous lymphomas and to discern whether any patterns might potentially serve as specific signatures. ::: ::: ::: METHODS ::: Fifteen patients with an established diagnosis of cutaneous lymphoma were prospectively recruited and seen in the university multidisciplinary cutaneous lymphoma program with MF, an MF- variant, CD30-positive lymphoproliferative disorder, or cutaneous B-cell lymphomas and were included in our study. Dermoscopic findings, histologic features, clinical characteristics, and demographic data were analyzed. ::: ::: ::: RESULTS ::: Patch stage MF was characterized by interconnected white structureless patches encircling small fine linear vessels, yielding an overall trabeculated to fenestrated pattern under dermoscopy. Corresponding histopathologic findings for these patterns included epidermotropism, atypical pleomorphic cells, and lichenoid infiltrates. Folliculotropic mycosis fungoides (FMF) was characterized by folliculocentric erosions surrounded by dotted and fine linear vessels, loss of terminal follicles, comedo-like openings, and interconnected regular-appearing structureless patches. Corresponding histopathologic findings in these FMF cases were typical of FMF. Notably, these changes were not appreciated in lymphomatoid papulosis. Primary cutaneous follicle center B cell lymphoma showed crystalline structures and vascular pseudopods. ::: ::: ::: CONCLUSIONS ::: Cutaneous lymphomas appear to demonstrate characteristic dermoscopic patterns, reflective of the specific lymphoma type and its corresponding histopathology, which have not been seen in inflammatory skin conditions, such as psoriasis and eczematous dermatitis.


title: Slot antenna for all-metal smartwatch applications

abstract: A circular slot-antenna designed for uses in smartwatches with all-metal for 2.4G BT/WIFI applications is presented in this paper. The model of smartwatch used for studies has a housing and wristband, both made of metal only. The smartwatch housing has a cylindrical shape with a circular-surface area of π×232 mm2 and a height of 10 mm, occupying a total volume of π×232×10 mm3. The circular slot antenna is formed using an arc-shaped slot with a length of 97 mm and a width of 1 mm along the edge of the circular surface of the smartwatch housing. Computer simulation is used to study the performance of the slot antenna in free space and on a phantom hand. Results of studies show that the slot antenna has the impedance frequency bands of 2.4–2.55 GHz and 2.35–2.5 GHz in free space and on the phantom hand, respectively, to cover the 2.4G BT/WIFI system. The efficiency of the antenna can reach more than 65% even on the phantom hand, making the slot antenna a suitable candidate for all-metal smartwatch applications.


title: Improved histogram based ghost removal in exposure fusion for high dynamic range images

abstract: For producing a single high dynamic range image (HDRI), multiple low dynamic range images (LDRIs) with different exposures are used. In high dynamic range imaging, a moving object in a set of LDRIs causes the ghost effect in a final HDRI. In this paper, we propose a ghost removal method based on histogram information. Object motion is detected by comparing ranks of intensity between LDRIs, and then the generated motion map is combined to construct the weight map in exposure fusion. Experimental results using various sets of LDRIs show that the proposed method effectively removes ghost effect and enhances contrast in a final HDRI. In terms of the computation time, the proposed method gives a better performance than existing methods.


title: SELECTING ENTERPRISE RESOURCE PLANNING SYSTEM USING FUZZY ANALYTIC HIERARCHY PROCESS METHOD

abstract: To select an enterprise resource planning (ERP) system is time consuming due to the resource constraints, the software complexity, and the different of alternatives. A comprehensively systematic selection policy for ERP system is very important to the success of ERP project. In this paper, we propose a fuzzy analytic hierarchy process (FAHP) method to evaluate the alternatives of ERP system. The selection criteria of ERP system are numerous and fuzzy, so how to select an adequate ERP system is crucial in the early phase of an ERP project. The framework decomposes ERP system selection into three main factors. The goal of this paper is to select the best alternative that meets the requirements with respect to product factors, system factors and management factors. The sub-attributes (sub-factors) related to ERP selection have been classified into twelve main categories of Functionality, Reliability, Usability, Efficiency, Maintainability, Cost, Implementation time, User friendliness, Flexibility, Vendor Reputation, Consultancy Services, and R&D Capability and arranged in a hierarchy structure. These criteria and factors are weighted and prioritized and finally a framework is provided for ERP selection with the fuzzy AHP method. Also, a real case study from Iran is also presented to demonstrate efficiency of this method in practice.


title: Addressing insider threat using “where you are” as fourth factor authentication

abstract: Current physical and cybersecurity systems have been relying on traditional three factor authentication to mitigate the threats posed by insider attacks. Typically, systems use one or two of the following factors to authenticate end-users: what you know (e.g., password), what you have (e.g., RSA ID), or what you are (e.g., fingerprint). Systems based on these factors have the following limitations: 1) access is typically bound to a single authentication occurrence leading to remote vulnerabilities, 2) the factors have little impact against persistent insider threats, and 3) many of the authentication systems violate system design principles such as user psychological acceptability by inconveniencing the end-users. In order to mitigate the identified limitations, we propose the usage of “where you are” as a complementary factor that can significantly improve both cybersecurity and physical security. Having accurate location tracking as a new factor for authentication: 1) provides continuous identification tracking and continuous mediation of access to resources, 2) requires remote threats to acquire a physical presence, 3) allows for the enforcement of cybersecurity and physical security policies in real-time through automation, and 4) provides enhanced security without inconveniencing the end-users. Using the strength of location as an authentication factor, this paper specifies design requirements that must be present in an insider-threat Prevention System (iTPS) that is capable of actively monitoring malicious insider behaviors. iTPS has the potential to radically change the physical protection systems and cybersecurity landscape by providing practitioners with the first-of-its-kind tool for real-time insider-threat prevention capabilities. iTPS is particularly suited to address the safety and security needs of critical infrastructure, nuclear facilities, and emergency response situations.


title: Dynamic hair manipulation in images and videos

abstract: This paper presents a single-view hair modeling technique for generating visually and physically plausible 3D hair models with modest user interaction. By solving an unambiguous 3D vector field explicitly from the image and adopting an iterative hair generation algorithm, we can create hair models that not only visually match the original input very well but also possess physical plausibility (e.g., having strand roots fixed on the scalp and preserving the length and continuity of real strands in the image as much as possible). The latter property enables us to manipulate hair in many new ways that were previously very difficult with a single image, such as dynamic simulation or interactive hair shape editing. We further extend the modeling approach to handle simple video input, and generate dynamic 3D hair models. This allows users to manipulate hair in a video or transfer styles from images to videos.


title: Avoiding moving obstacles during visual navigation

abstract: Moving obstacle avoidance is a fundamental requirement for any robot operating in real environments, where pedestrians, bicycles and cars are present. In this work, we design and validate a new approach that takes explicitly into account obstacle velocities, to achieve safe visual navigation in outdoor scenarios. A wheeled vehicle, equipped with an actuated pinhole camera and with a lidar, must follow a path represented by key images, without colliding with the obstacles. To estimate the obstacle velocities, we design a Kalman-based observer. Then, we adapt the tentacles designed in [1], to take into account the predicted obstacle positions. Finally, we validate our approach in a series of simulated and real experiments, showing that when the obstacle velocities are considered, the robot behaviour is safer, smoother, and faster than when it is not.


title: NCHS Data Brief ¡ No. 10 ¡ October 2008 Food Allergy Among U.S. Children: Trends in Prevalence and

abstract: U.S. D Food allergy is a potentially serious immune response to eating specific foods or food additives. Eight types of food account for over 90% of allergic reactions in affected individuals: milk, eggs, peanuts, tree nuts, fish, shellfish, soy, and wheat (1,2). Reactions to these foods by an allergic person can range from a tingling sensation around the mouth and lips and hives to death, depending on the severity of the allergy. The mechanisms by which a person develops an allergy to specific foods are largely unknown. Food allergy is more prevalent in children than adults, and a majority of affected children will “outgrow” food allergies with age. However, food allergy can sometimes become a lifelong concern (1). Food allergies can greatly affect children and their families’ well-being. There are some indications that the prevalence of food allergy may be increasing in the United States and in other countries (2–4).


title: HINTS: A novel approach for realistic simulations of vehicular communications

abstract: One of the main challenges in the Vehicular Ad-Hoc Networks (VANETs) research domain is the simulation of vehicular communications using realistic mobility models. Several efforts have been put lately in this purpose; yet, the proposed models are either inappropriate, or carry significant disadvantages. In this paper, we propose a novel approach for realistic simulations in vehicular networks, inspired from the hierarchical video and image compression technique. We developed HINTS (Hybrid Integration of Network and Traffic Simulators), the correspondent platform, engendered from the integration of SUMO, a traffic simulator and NS-3, a network simulator after adding some supplemental modules to both simulators. Its efficiency and performance are demonstrated in a proof-of-concept study, based on a realistic traffic management scenario.


title: Energy aware routing for low energy ad hoc sensor networks

abstract: The recent interest in sensor networks has led to a number of routing schemes that use the limited resources available at sensor nodes more efficiently. These schemes typically try to find the minimum energy path to optimize energy usage at a node. In this paper we take the view that always using lowest energy paths may not be optimal from the point of view of network lifetime and long-term connectivity. To optimize these measures, we propose a new scheme called energy aware routing that uses sub-optimal paths occasionally to provide substantial gains. Simulation results are also presented that show increase in network lifetimes of up to 40% over comparable schemes like directed diffusion routing. Nodes also burn energy in a more equitable way across the network ensuring a more graceful degradation of service with time.


title: Adaptive stress testing of airborne collision avoidance systems

abstract: This paper presents a scalable method to efficiently search for the most likely state trajectory leading to an event given only a simulator of a system. Our approach uses a reinforcement learning formulation and solves it using Monte Carlo Tree Search (MCTS). The approach places very few requirements on the underlying system, requiring only that the simulator provide some basic controls, the ability to evaluate certain conditions, and a mechanism to control the stochasticity in the system. Access to the system state is not required, allowing the method to support systems with hidden state. The method is applied to stress test a prototype aircraft collision avoidance system to identify trajectories that are likely to lead to near mid-air collisions. We present results for both single and multi-threat encounters and discuss their relevance. Compared with direct Monte Carlo search, this MCTS method performs significantly better both in finding events and in maximizing their likelihood.


title: Developing Non-Goal Dialog System based on Examples of Drama Television

abstract: This paper presents a design and experiments of developing a non-goal dialog system by utilizing human-to-human conversation examples from drama television. The aim is to build a conversational agent that can interact with users in as natural a fashion as possible, while reducing the time requirement for database design and collection. A number of the challenging design issues we faced are described, including (1) filtering and constructing a dialog example database from the drama conversations and (2) retrieving a proper system response by finding the best dialog example based on the current user query. Subjective evaluation from a small user study is also discussed.


title: Stereotype threat spillover: How coping with threats to social identity affects aggression, eating, decision making, and attention.

abstract: Stereotype threat spillover is a situational predicament in which coping with the stress of stereotype confirmation leaves one in a depleted volitional state and thus less likely to engage in effortful self-control in a variety of domains. We examined this phenomenon in 4 studies in which we had participants cope with stereotype and social identity threat and then measured their performance in domains in which stereotypes were not “in the air.” In Study 1 we examined whether taking a threatening math test could lead women to respond aggressively. In Study 2 we investigated whether coping with a threatening math test could lead women to indulge themselves with unhealthy food later on and examined the moderation of this effect by personal characteristics that contribute to identity-threat appraisals. In Study 3 we investigated whether vividly remembering an experience of social identity threat results in risky decision making. Finally, in Study 4 we asked whether coping with threat could directly influence attentional control and whether the effect was implemented by inefficient performance monitoring, as assessed by electroencephalography. Our results indicate that stereotype threat can spill over and impact self-control in a diverse array of nonstereotyped domains. These results reveal the potency of stereotype threat and that its negative consequences might extend further than was previously thought.


title: Truss Decomposition in Massive Networks

abstract: The k-truss is a type of cohesive subgraphs proposed recently for the study of networks. While the problem of computing most cohesive subgraphs is NP-hard, there exists a polynomial time algorithm for computing k-truss. Compared with k-core which is also efficient to compute, k-truss represents the "core" of a k-core that keeps the key information of, while filtering out less important information from, the k-core. However, existing algorithms for computing k-truss are inefficient for handling today's massive networks. We first improve the existing in-memory algorithm for computing k-truss in networks of moderate size. Then, we propose two I/O-efficient algorithms to handle massive networks that cannot fit in main memory. Our experiments on real datasets verify the efficiency of our algorithms and the value of k-truss.


title: VirtualWorlds as Proxy for Multi-object Tracking Analysis

abstract: Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual world cloning method, and validate our approach by building and publicly releasing a new video dataset, called "Virtual KITTI" 1, automatically labeled with accurate ground truth for object detection, tracking, scene and instance segmentation, depth, and optical flow. We provide quantitative experimental evidence suggesting that (i) modern deep learning algorithms pre-trained on real data behave similarly in real and virtual worlds, and (ii) pre-training on virtual data improves performance. As the gap between real and virtual worlds is small, virtual worlds enable measuring the impact of various weather and imaging conditions on recognition performance, all other things being equal. We show these factors may affect drastically otherwise high-performing deep models for tracking.


title: Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer

abstract: Dependency graph, as a heterogeneous graph representing the intrinsic relationships between different pairs of system entities, is essential to many data analysis applications, such as root cause diagnosis, intrusion detection, etc. Given a well-trained dependency graph from a source domain and an immature dependency graph from a target domain, how can we extract the entity and dependency knowledge from the source to enhance the target? One way is to directly apply a mature dependency graph learned from a source domain to the target domain. But due to the domain variety problem, directly using the source dependency graph often can not achieve good performance. Traditional transfer learning methods mainly focus on numerical data and are not applicable. ::: In this paper, we propose ACRET, a knowledge transfer based model for accelerating dependency graph learning from heterogeneous categorical event streams. In particular, we first propose an entity estimation model to filter out irrelevant entities from the source domain based on entity embedding and manifold learning. Only the entities with statistically high correlations are transferred to the target domain. On the surviving entities, we propose a dependency construction model for constructing the unbiased dependency relationships by solving a two-constraint optimization problem. The experimental results on synthetic and real-world datasets demonstrate the effectiveness and efficiency of ACRET. We also apply ACRET to a real enterprise security system for intrusion detection. Our method is able to achieve superior detection performance at least 20 days lead lag time in advance with more than 70% accuracy.


Multi-Channel Scheduling Protocol for Wireless Personal Area Networks


title: Hector Open Source Modules for Autonomous Mapping and Navigation with Rescue Robots

abstract: Key abilities for robots deployed in urban search and rescue tasks include autonomous exploration of disaster sites and recognition of victims and other objects of interest. In this paper, we present related open source software modules for the development of such complex capabilities which include hector_slam for self-localization and mapping in a degraded urban environment. All modules have been successfully applied and tested originally in the RoboCup Rescue competition. Up to now they have already been re-used and adopted by numerous international research groups for a wide variety of tasks. Recently, they have also become part of the basis of a broader initiative for key open source software modules for urban search and rescue robots.


title: Prognostic implications of expression of the cell cycle inhibitor protein p27Kip1

abstract: Mitogenic and growth inhibitory signals influence the activity of a family of cyclin dependent kinases (cdks). p27 is an important cdk inhibitor, acting in G1 to inhibit cyclin-cdks. As negative growth regulators, the cdk inhibitors may function as tumor suppressors. While the p16 gene plays a tumor suppressor role in cancers, p27 gene mutations have been identified only rarely. While high levels of p27 protein are expressed in normal human mammary epithelium, loss of p27 is frequent and is of independent prognostic significance in breast cancers. Low p27 is also a poor prognostic factor in colon, gastric, esophageal, lung, and prostate carcinomas, and enhanced proteasomal degradation may underlie loss of p27 in tumor cells. Loss of p27 has not been significantly correlated with tumor proliferation in a number of studies and may reflect alterations in differentiation and adhesion-dependent growth regulation germane to oncogenesis and tumor progression. Efforts to confirm the prognostic value of p27 are under way in a number of large breast cancer studies. These studies may also indicate whether loss of p27 in association with other traditional or novel markers has greater prognostic potential than each factor alone. p27 immunostaining is inexpensive and reliable and may become part of the routine histopathologic processing of tumors in the near future. Widespread application of p27 in prognostic testing will require greater uniformity in scoring techniques and determination of the cut off levels which distinguish individuals at high and low risk of cancer recurrence and death. Finally, the greatest utility of p27 may lie in the information it sheds on the biology of aberrant growth regulation in breast cancer and the potential to use this in the generation of novel therapeutic strategies.


title: Framework for Designing and Evaluating Game Achievements

abstract: This paper presents a framework for evaluating and designing game design patterns commonly called as “achievements”. The results are based on empirical studies of a variety of popular achievement systems. The results, along with the framework for analyzing and designing achievements, present two definitions of game achievements. From the perspective of the achievement system, an achievement appears as a challenge consisting of a signifying element, rewards and completion logics whose fulfilment conditions are defined through events in other systems (usually games). From the perspective of a single game, an achievement appears as an optional challenge provided by a meta-game that is independent of a single game session and yields possible


Ontologies of Time: Review and Trends


SARSHIPS : A COMPONENT-BASED SYSTEM FOR SHIP DETECTION ON SAR IMAGES


title: Supporting Implicit Human-to-Vehicle Interaction: Driver Identification from Sitting Postures

abstract: Mobile internet services have started to pervade into vehicles, approaching a new generation of networked, ”smart” cars. With the evolution of in-car services, particularly with the emergence of services that are personalized to an individual driver (like road pricing, maintenance, insurance and entertainment services) the need for reliable, yet easy to handle identification and authentication has arisen. Services that demand unambiguous and unmistakable continuous identification of the driver have recently attracted many research efforts, mostly proposing video-based face/pose recognition, or acoustic analysis. A driver identification system for vehicular services is proposed, that, as opposed to video or audio based techniques, does not suffer from the continuously changing environment while driving, like lighting or noise conditions. A posture recognition technique based on a high resolution pressure sensor integrated invisibly and unobtrusively into the fabric of the driver seat has been developed, taking the pelvic bone distance as a biometric trait. Data coming from two 32x32 pressure sensor arrays (seatand backrest) is classified according to features defined based on the pelvic bone signature, mid and high pressure distribution and body weight. Empirical studies, besides analyzing (quantitative) driver recognition performance, assess the identification technique according to the qualitative attributes universality, collectability, uniqueness, and permanency. The proposed driver identification technique is implicit and thus not reliant to attention, it is continuously in operation while seated, and requires no active person cooperation. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ISVCS 2008, July 22 – 24, 2008, Dublin, Ireland. Copyright 2008 ACM ISBN 978-963-9799-27-1 ...$5.00. These characteristics encourage the universal use of the approach – a whole new modality for person-to-environment interaction seems possible.


title: Improved neural machine translation with source syntax

abstract: Neural Machine Translation (NMT) based on the encoder-decoder architecture has recently achieved the state-of-the-art performance. Researchers have proven that extending word level attention to phrase level attention by incorporating source-side phrase structure can enhance the attention model and achieve promising improvement. However, word dependencies that can be crucial to correctly understand a source sentence are not always in a consecutive fashion (i.e. phrase structure), sometimes they can be in long distance. Phrase structures are not the best way to explicitly model long distance dependencies. In this paper we propose a simple but effective method to incorporate source-side long distance dependencies into NMT. Our method based on dependency trees enriches each source state with global dependency structures, which can better capture the inherent syntactic structure of source sentences. Experiments on Chinese-English and English-Japanese translation tasks show that our proposed method outperforms state-of-the-art SMT and NMT baselines.


title: Robust Appointment Scheduling

abstract: Health care providers are under tremendous pressure to reduce costs and increase quality of their services. It has long been recognized that well-designed appointment systems have the potential to improve utilization of expensive personnel and medical equipment and to reduce waiting times for patients. In a widely influential survey on outpatient scheduling, Cayirli and Veral (2003) concluded that the "biggest challenge for future research will be to develop easy-to-use heuristics." We analyze the appointment scheduling problem from a robust-optimization perspective, and we establish the existence of a closed-form optimal solution--arguably the simplest and best `heuristic' possible. In case the order of patients is changeable, the robust optimization approach yields a novel formulation of the appointment scheduling problem as that of minimizing a concave function over a supermodular polyhedron. We devise the first constant-factor approximation algorithm for this case.


title: ExPregel: a new computational model for large-scale graph processing

abstract: These days, large-scale graph processing becomes more and more important. Pregel, inspired by Bulk Synchronous Parallel, is one of the highly used systems to process large-scale graph problems. In Pregel, each vertex executes a function and waits for a superstep to communicate its data to other vertices. Superstep is a very time-consuming operation, used by Pregel, to synchronize distributed computations in a cluster of computers. However, it may become a bottleneck when the number of communications increases in a graph with million vertices. Superstep works like a barrier in Pregel that increases the side effect of skew problem in distributed computing environment. ExPregel is a Pregel-like model that is designed to reduce the number of communication messages between two vertices resided on two different computational nodes. We have proven that ExPregel reduces the number of exchanged messages as well as the number of supersteps for all graph topologies. Enhancing parallelism in our new computational model is another important feature that manifolds the speed of graph analysis programs. More interestingly, ExPregel uses the same model of programming as Pregel. Our experiments on large-scale real-world graphs show that ExPregel can reduce network traffic as well as number of supersteps from 45% to 96%. Runtime speed up in the proposed model varies from 1.2× to 30×. Copyright © 2015 John Wiley & Sons, Ltd.


title: A time-delayed epidemic model for Ebola disease transmission

abstract: In this paper, we propose a delayed mathematical model for the transmission of Ebola in humans. We consider the transmission of infection between the living humans and from infectious corpses to the living individuals in which the latent period of Ebola is incorporated. We identify the basic reproduction number R0 for the model, prove that the disease-free equilibrium is always globally asymptotically stable when R0 1. We show that the endemic steady state is locally asymptotically stable under certain condition and globally asymptotically stable in a special case of the model. Numerical simulations are provided to demonstrate and complement the theoretical results.


Enhancing L 2 learners ’ noticing skills through self-confrontation with their own oral production performance 1


title: Triply Supervised Decoder Networks for Joint Detection and Segmentation

abstract: Joint object detection and semantic segmentation is essential in many fields such as self-driving cars. An initial attempt towards this goal is to simply share a single network for multi-task learning. We argue that it does not make full use of the fact that detection and segmentation are mutually beneficial. In this paper, we propose a framework called TripleNet to deeply boost these two tasks. On the one hand, to deeply join the two tasks at different scales, triple supervisions including detection-oriented supervision and class-aware/agnostic segmentation supervisions are imposed on each layer of the decoder. Class-agnostic segmentation provides an objectness prior to detection and segmentation. On the other hand, to further intercross the two tasks and refine the features in each scale, two light-weight modules (i.e., the inner-connected module and the attention skip-layer fusion) are incorporated. Because segmentation supervision on each decoder layer are not performed at the test stage and two added modules are light-weight, the proposed TripleNet can run at a real-time speed (16 fps). Experiments on the VOC 2007/2012 and COCO datasets show that TripleNet outperforms all the other one-stage methods on both two tasks (e.g., 81.9% mAP and 83.3% mIoU on VOC 2012, and 37.1% mAP and 59.6% mIoU on COCO) by a single network.


title: Multi-Bias Non-linear Activation in Deep Neural Networks

abstract: As a widely used non-linear activation, Rectified Linear Unit (ReLU) separates noise and signal in a feature map by learning a threshold or bias. However, we argue that the classification of noise and signal not only depends on the magnitude of responses, but also the context of how the feature responses would be used to detect more abstract patterns in higher layers. In order to output multiple response maps with magnitude in different ranges for a particular visual pattern, existing networks employing ReLU and its variants have to learn a large number of redundant filters. In this paper, we propose a multi-bias non-linear activation (MBA) layer to explore the information hidden in the magnitudes of responses. It is placed after the convolution layer to decouple the responses to a convolution kernel into multiple maps by multi-thresholding magnitudes, thus generating more patterns in the feature space at a low computational cost. It provides great flexibility of selecting responses to different visual patterns in different magnitude ranges to form rich representations in higher layers. Such a simple and yet effective scheme achieves the state-of-the-art performance on several benchmarks.


title: Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data

abstract: User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search. The data in user response prediction is mostly in a multi-field categorical format and transformed into sparse representations via one-hot encoding. Due to the sparsity problems in representation and optimization, most research focuses on feature engineering and shallow modeling. Recently, deep neural networks have attracted research attention on such a problem for their high capacity and end-to-end training scheme. In this article, we study user response prediction in the scenario of click prediction. We first analyze a coupled gradient issue in latent vector-based models and propose kernel product to learn field-aware feature interactions. Then, we discuss an insensitive gradient issue in DNN-based models and propose Product-based Neural Network, which adopts a feature extractor to explore feature interactions. Generalizing the kernel product to a net-in-net architecture, we further propose Product-network in Network (PIN), which can generalize previous models. Extensive experiments on four industrial datasets and one contest dataset demonstrate that our models consistently outperform eight baselines on both area under curve and log loss. Besides, PIN makes great click-through rate improvement (relatively 34.67%) in online A/B test.


title: Progressive noninfectious anterior vertebral fusion: MRI findings and association with a lipoma of the filum terminale in a 5-year-old girl

abstract: Progressive noninfectious anterior vertebral fusion (PNAF) is a rare but well-defined entity of unknown cause. We report the MRI findings in a 5-year-old in whom PNAF was associated with a lipoma of the filum terminale.


title: Learning a Mahalanobis Metric from Equivalence Constraints

abstract: Many learning algorithms use a metric defined over the input space as a principal tool, and their performance critically depends on the quality of this metric. We address the problem of learning metrics using side-information in the form of equivalence constraints. Unlike labels, we demonstrate that this type of side-information can sometimes be automatically obtained without the need of human intervention. We show how such side-information can be used to modify the representation of the data, leading to improved clustering and classification.Specifically, we present the Relevant Component Analysis (RCA) algorithm, which is a simple and efficient algorithm for learning a Mahalanobis metric. We show that RCA is the solution of an interesting optimization problem, founded on an information theoretic basis. If dimensionality reduction is allowed within RCA, we show that it is optimally accomplished by a version of Fisher's linear discriminant that uses constraints. Moreover, under certain Gaussian assumptions, RCA can be viewed as a Maximum Likelihood estimation of the within class covariance matrix. We conclude with extensive empirical evaluations of RCA, showing its advantage over alternative methods.


title: Online Kernel-Based Learning for Task-Space Tracking Robot Control

abstract: Task-space control of redundant robot systems based on analytical models is known to be susceptive to modeling errors. Data-driven model learning methods may present an interesting alternative approach. However, learning models for task-space tracking control from sampled data is an ill-posed problem. In particular, the same input data point can yield many different output values, which can form a nonconvex solution space. Because the problem is ill-posed, models cannot be learned from such data using common regression methods. While learning of task-space control mappings is globally ill-posed, it has been shown in recent work that it is locally a well-defined problem. In this paper, we use this insight to formulate a local kernel-based learning approach for online model learning for task-space tracking control. We propose a parametrization for the local model, which makes an application in task-space tracking control of redundant robots possible. The model parametrization further allows us to apply the kernel-trick and, therefore, enables a formulation within the kernel learning framework. In our evaluations, we show the ability of the method for online model learning for task-space tracking control of redundant robots.


title: Enhancing security and privacy in biometrics-based authentication systems

abstract: Because biometrics-based authentication offers several advantages over other authentication methods, there has been a significant surge in the use of biometrics for user authentication in recent years. It is important that such biometrics-based authentication systems be designed to withstand attacks when employed in security-critical applications, especially in unattended remote applications such as e-commerce. In this paper we outline the inherent strengths of biometrics-based authentication, identify the weak links in systems employing biometrics-based authentication, and present new solutions for eliminating some of these weak links. Although, for illustration purposes, fingerprint authentication is used throughout, our analysis extends to other biometrics-based methods.


title: Deep Learning for Sentiment Analysis : A Survey

abstract: Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.


title: Factors Affecting e-Tailing Website Effectiveness: An Indian Perspective

abstract: The Indian Retail Market is witnessing a revolution. The growth of internet has enabled the new retail format of the virtual retailer to emerge and forced the existing retailers to consider e-tailing model of retailing as well. A large number of consumers frequently use the Internet for shopping purposes but its' not clear what drives them to shop online. This study captures the important factors affecting the success and effectiveness of e-tailing sites to propose a unifying framework that could eventually guide research in this area and prove beneficial for e-tailers and e-marketers as well.


title: Visual analytics for complex concepts using a human cognition model

abstract: As the information being visualized and the process of understanding that information both become increasingly complex, it is necessary to develop new visualization approaches that facilitate the flow of human reasoning. In this paper, we endeavor to push visualization design a step beyond current user models by discussing a modeling framework of human ldquohigher cognition.rdquo Based on this cognition model, we present design guidelines for the development of visual interfaces designed to maximize the complementary cognitive strengths of both human and computer. Some of these principles are already being reflected in the better visual analytics designs, while others have not yet been applied or fully applied. But none of the guidelines have explained the deeper rationale that the model provides. Lastly, we discuss and assess these visual analytics guidelines through the evaluation of several visualization examples.


Chapter 6 A Survey of Randomization Methods for Privacy-Preserving Data Mining


title: Persuasive food marketing to children: use of cartoons and competitions in Australian commercial television advertisements

abstract: While there is a recognized link between high levels of exposure to advertising of unhealthy foods and overweight and obesity among children, there is little research on the extent to which these exposures include persuasive marketing techniques. This study aimed to measure children's exposure to the use of persuasive marketing within television food advertisements. Advertisements broadcast on all three commercial Australian television channels were recorded for an equivalent 1 week period in May 2006 and 2007 (714 h). Food advertisements were analysed for their use of persuasive marketing, including premium offers, such as competitions, and the use of promotional characters, including celebrities and cartoon characters. Advertised foods were categorized as core, non-core or miscellaneous foods. Commercial data were purchased to determine children's peak viewing times and popular programs. A total of 20 201 advertisements were recorded, 25.5% of which were for food. Significantly more food advertisements broadcast during children's peak viewing times, compared to non-peak times, contained promotional characters (P < 0.05) and premium offers (P < 0.001). During programs most popular with children, there were 3.3 non-core food advertisements per hour containing premium offers, compared to 0.2 per hour during programs most popular with adults. The majority of advertisements containing persuasive marketing during all viewing periods were for non-core foods. Persuasive marketing techniques are frequently used to advertise non-core foods to children, to promote children's brand recognition and preference for advertised products. Future debate relating to television advertising regulations must consider the need to restrict the use of persuasive marketing techniques to children.


title: Keeping the Resident in the Loop: Adapting the Smart Home to the User

abstract: Advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data.


title: Software rejuvenation scheduling using accelerated life testing

abstract: A number of studies have reported the phenomenon of “Software aging”, caused by resource exhaustion and characterized by progressive software performance degradation. In this article, we carry out an experimental study of software aging and rejuvenation for an on-line bookstore application, following the standard configuration of TPC-W benchmark. While real website is used for the bookstore, the clients are emulated. In order to reduce the time to application failures caused by memory leaks, we use the accelerated life testing (ALT) approach. We then select the Weibull time to failure distribution at normal level, to be used in a semi-Markov process, to compute the optimal software rejuvenation trigger interval. Since the validation of optimal rejuvenation trigger interval with emulated browsers will take an inordinate long time, we develop a simulation model to validate the ALT experimental results, and also estimate the steady-state availability to cross-validate the results of the semi-Markov availability model.


title: How does the transformation of an avatar face giving a favorable impression affect human recognition of the face?

abstract: We investigated how different appearances in the favorable impressions of 3D avatar faces affect face-recognition performances by humans. We conducted an encoding and testing experiment using synthesized facial images and artificially manipulated the strength of the perceived impressions in three different dimensions. We also subjectively assessed the favorability of the synthesized faces that were used as visual stimuli in face-recognition tests and found that facial transformation, which decreased the favorability impressions, generally deteriorates human face-recognition performance.


title: Application of electrostatic separation to the recycling of plastic wastes: separation of PVC, PET, and ABS.

abstract: Plastics are widely used in everyday life as a useful material, and thus their consumption is growing at a rate of about 5% per year in Korea. However, the constant generation of plastic wastes and their disposal generates environmental problems along with economic loss. In particular, mixed waste plastics are difficult to recycle because of their inferior characteristics. A laboratory-scale triboelectrostatic separator unit has been designed and assembled for this study. On the basis of the control of electrostatic charge, the separation of three kinds of mixed plastics, polyvinyl chloride (PVC), poly(ethylene terephthalate) (PET), and acrylonitrile-butadiene-styrene (ABS), in a range of similar gravities has been performed through a two-stage separation process. Polypropylene (PP) and high-impact polystyrene (HIPS) were found to be the most effective materials for a tribo-charger in the separation of PVC, PET, and ABS. The charge-to-mass ratio (nC/g) of plastics increased with increasing air velocity in the tribo charger. In the first stage, using the PP cyclone charger, the separation efficiency of particles considerably depended on the air velocity (10 m/s), the relative humidity (< 30%), the electrode potential (> 20 kV), and the splitter position (+2 cm from the center) in the triboelelctrostatic separator unit. At this time, a PVC grade of 99.40% and a recovery of 98.10% have successfully been achieved. In the second stage, using the HIPS cyclone charger, a PET grade of 97.80% and a recovery of 95.12% could be obtained under conditions of 10 m/s, over 25 kV, a central splitter position, and less than 40% relative humidity. In order to obtain 99.9% PVC grade and 99.3% PET grade, their recoveries should be sacrificed by 20.9% and 27%, respectively, with moving the splitter from the center to a (+)6 cm position.


Spring Gravity Compensation Using the Noncircular Pulley and Cable For the Less-Spring Design


title: Emotion regulation in the workplace : a new way to conceptualize emotional labor

abstract: A washing machine includes a wash tub, a water-supply mechanism for supplying water into the wash tub, and a disposition section in which a solid antimicrobial agent containing an organic compound having a nitrogen-halogen atomic combination is disposed. The disposition section is provided in the water-supply mechanism. The antimicrobial agent is brought into contact with water thereby to release hypohalogenous acid into the water.


title: Executive Functions Predict the Success of Top-Soccer Players

abstract: While the importance of physical abilities and motor coordination is non-contested in sport, more focus has recently been turned toward cognitive processes important for different sports. However, this line of studies has often investigated sport-specific cognitive traits, while few studies have focused on general cognitive traits. We explored if measures of general executive functions can predict the success of a soccer player. The present study used standardized neuropsychological assessment tools assessing players' general executive functions including on-line multi-processing such as creativity, response inhibition, and cognitive flexibility. In a first cross-sectional part of the study we compared the results between High Division players (HD), Lower Division players (LD) and a standardized norm group. The result shows that both HD and LD players had significantly better measures of executive functions in comparison to the norm group for both men and women. Moreover, the HD players outperformed the LD players in these tests. In the second prospective part of the study, a partial correlation test showed a significant correlation between the result from the executive test and the numbers of goals and assists the players had scored two seasons later. The results from this study strongly suggest that results in cognitive function tests predict the success of ball sport players.


title: Flexible NO2 gas sensors based on sheet-like hierarchical ZnO1−x coatings deposited on polypropylene papers by suspension flame spraying

abstract: 2D sheet-like ZnO coatings composed of nanoparticles were deposited on flexible polypropylene papers equipped with gold electrodes in three steps. Firstly, sheet-like ZnO powder was synthesized by direct precipitation method. Then, the as-synthesized powders were dispersed in anhydrous ethanol to form feedstock suspension. At last, the feedstock suspension was injected into the acetylene-oxygen flame to deposit ZnO coatings. X-ray diffraction and field-emission microscopy results revealed that the coatings were hexagonal phase and porous hierarchical structure. With an increase in spray distance, the coatings exhibited a morphology with smaller particle size and sheet thickness. Photoluminescence and X-ray photoelectron spectra indicated that large amounts of oxygen vacancies were introduced into the coatings, and oxygen vacancy concentration increased with the spray distance. UV–vis spectra showed that the light absorption of the coatings was extended to visible light region, and wider absorption range was obtained when the spray distance was increased. The ZnO coatings exhibited good responses to sub-ppm level NO2 at room temperature under white LED light illumination, and the coating deposited with longer spray distance presented a better NO2 sensing performance.


title: Informatic parcellation of the network involved in the computation of subjective value

abstract: Understanding how the brain computes value is a basic question in neuroscience. Although individual studies have driven this progress, meta-analyses provide an opportunity to test hypotheses that require large collections of data. We carry out a meta-analysis of a large set of functional magnetic resonance imaging studies of value computation to address several key questions. First, what is the full set of brain areas that reliably correlate with stimulus values when they need to be computed? Second, is this set of areas organized into dissociable functional networks? Third, is a distinct network of regions involved in the computation of stimulus values at decision and outcome? Finally, are different brain areas involved in the computation of stimulus values for different reward modalities? Our results demonstrate the centrality of ventromedial prefrontal cortex (VMPFC), ventral striatum and posterior cingulate cortex (PCC) in the computation of value across tasks, reward modalities and stages of the decision-making process. We also find evidence of distinct subnetworks of co-activation within VMPFC, one involving central VMPFC and dorsal PCC and another involving more anterior VMPFC, left angular gyrus and ventral PCC. Finally, we identify a posterior-to-anterior gradient of value representations corresponding to concrete-to-abstract rewards.


title: The Multi-Dimensional Hartley Transform as a Basis for Volume Rendering

abstract: The Fast Hartley Transform (FHT), a discrete version of the Hartley Transform (HT), has been studied in various papers and shown to be faster and more convenient to implement and handle than the corresponding Fast Fourier Transform (FFT). As the HT is not as nicely separable as the FT, a multidimensional version of the HT needs to perform a final correction step to convert the result of separate HTs for each dimension into the final multi-dimensional transform. Although there exist algorithms for two and three dimensions, no generalization to arbitrary dimensions can be found in the literature. We demonstrate an easily comprehensible and efficient implementation of the fast HT and its multi-dimensional extension. By adapting this algorithm to volume rendering by the projection-slice theorem and by the use for filter analysis in frequency domain we further demonstrate the importance of the HT in this application area.


title: Simple Learning and Compositional Application of Perceptually Grounded Word Meanings for Incremental Reference Resolution

abstract: An elementary way of using language is to refer to objects. Often, these objects are physically present in the shared environment and reference is done via mention of perceivable properties of the objects. This is a type of language use that is modelled well neither by logical semantics nor by distributional semantics, the former focusing on inferential relations between expressed propositions, the latter on similarity relations between words or phrases. We present an account of word and phrase meaning that is perceptually grounded, trainable, compositional, and ‘dialogueplausible’ in that it computes meanings word-by-word. We show that the approach performs well (with an accuracy of 65% on a 1-out-of-32 reference resolution task) on direct descriptions and target/landmark descriptions, even when trained with less than 800 training examples and automatically transcribed utterances.


title: On peer-to-peer (P2P) content delivery

abstract: In both academia and industry, peer-to-peer (P2P) applications have attracted great attentions. P2P applications such as Napster, Gnutella, FastTrack, BitTorrent, Skype and PPLive, have witnessed tremendous success among the end users. Unlike a client-server based system, peers bring with them serving capacity. Therefore, as the demand of a P2P system grows, the capacity of the network grows, too. This enables a P2P application to be cheap to build and superb in scalability. In this paper, we survey the state of the art of the research and the development of P2P content delivery application. Using examples of the deployed P2P applications and research prototypes, we survey the best practices in P2P overlay building and P2P scheduling. We hope that the information may help the readers to build a reliable, robust P2P content delivery application.


title: Information flow and cooperative control of vehicle formations

abstract: We consider the problem of cooperation among a collection of vehicles performing a shared task using intervehicle communication to coordinate their actions. Tools from algebraic graph theory prove useful in modeling the communication network and relating its topology to formation stability. We prove a Nyquist criterion that uses the eigenvalues of the graph Laplacian matrix to determine the effect of the communication topology on formation stability. We also propose a method for decentralized information exchange between vehicles. This approach realizes a dynamical system that supplies each vehicle with a common reference to be used for cooperative motion. We prove a separation principle that decomposes formation stability into two components: Stability of this is achieved information flow for the given graph and stability of an individual vehicle for the given controller. The information flow can thus be rendered highly robust to changes in the graph, enabling tight formation control despite limitations in intervehicle communication capability.


title: Synthesizing Training Data for Object Detection in Indoor Scenes

abstract: Detection of objects in cluttered indoor environments is one of the key enabling functionalities for service robots. The best performing object detection approaches in computer vision exploit deep Convolutional Neural Networks (CNN) to simultaneously detect and categorize the objects of interest in cluttered scenes. Training of such models typically requires large amounts of annotated training data which is time consuming and costly to obtain. In this work we explore the ability of using synthetically generated composite images for training state-of-the-art object detectors, especially for object instance detection. We superimpose 2D images of textured object models into images of real environments at variety of locations and scales. Our experiments evaluate different superimposition strategies ranging from purely image-based blending all the way to depth and semantics informed positioning of the object models into real scenes. We demonstrate the effectiveness of these object detector training strategies on two publicly available datasets, the GMU-Kitchens and the Washington RGB-D Scenes v2. As one observation, augmenting some hand-labeled training data with synthetic examples carefully composed onto scenes yields object detectors with comparable performance to using much more hand-labeled data. Broadly, this work charts new opportunities for training detectors for new objects by exploiting existing object model repositories in either a purely automatic fashion or with only a very small number of human-annotated examples.


title: Global mutual information-based feature selection approach using single-objective and multi-objective optimization

abstract: Feature selection is an important preprocessing step in data mining. Mutual information-based feature selection is a kind of popular and effective approaches. In general, most existing mutual information-based techniques are greedy methods, which are proven to be efficient but suboptimal. In this paper, mutual information-based feature selection is transformed into a global optimization problem, which provides a new idea for solving feature selection problems. First, a single-objective feature selection algorithm combining relevance and redundancy is presented, which has well global searching ability and high computational efficiency. Furthermore, to improve the performance of feature selection, we propose a multi-objective feature selection algorithm. The method can meet different requirements and achieve a tradeoff among multiple conflicting objectives. On this basis, a hybrid feature selection framework is adopted for obtaining a final solution. We compare the performance of our algorithm with related methods on both synthetic and real datasets. Simulation results show the effectiveness and practicality of the proposed method.


title: A Solution to Single Point of Failure Using Voter Replication and Disagreement Detection

abstract: This paper suggests a method, called distributed voting, to overcome the problem of the single point of failure in a TMR system used in robotics and industrial control applications. It uses time redundancy and is based on TMR with disagreement detector feature. This method masks faults occurring in the voter where the TMR system can continue its function properly. The method has been evaluated by injecting faults into Vertex2Pro and Vertex4 Xilinx FPGAs. An analytical evolution is also performed. The results of both evaluation approaches show that the proposed method can improve the reliability and the mean time to failure (MTTF) of a TMR system by at least a factor of (2 R_v (t)) where R_v (t) is the reliability of the voter.


title: Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring

abstract: Given a set of objects P and a query point q, a k nearest neighbor (k-NN) query retrieves the k objects in P that lie closest to q. Even though the problem is well-studied for static datasets, the traditional methods do not extend to highly dynamic environments where multiple continuous queries require real-time results, and both objects and queries receive frequent location updates. In this paper we propose conceptual partitioning (CPM), a comprehensive technique for the efficient monitoring of continuous NN queries. CPM achieves low running time by handling location updates only from objects that fall in the vicinity of some query (and ignoring the rest). It can be used with multiple, static or moving queries, and it does not make any assumptions about the object moving patterns. We analyze the performance of CPM and show that it outperforms the current state-of-the-art algorithms for all problem settings. Finally, we extend our framework to aggregate NN (ANN) queries, which monitor the data objects that minimize the aggregate distance with respect to a set of query points (e.g., the objects with the minimum sum of distances to all query points).


title: Using Non-Local Features to Improve Named Entity Recognition Recall

abstract: Named Entity Recognition (NER) is always limited by its lower recall resulting from the asymmetric data distribution where the NONE class dominates the entity classes. This paper presents an approach that exploits non-local information to improve the NER recall. Several kinds of non-local features encoding entity token occurrence, entity boundary and entity class are explored under Conditional Random Fields (CRFs) framework. Experiments on SIGHAN 2006 MSRA (CityU) corpus indicate that non-local features can effectively enhance the recall of the state-of-the-art NER systems. Incorporating the non-local features into the NER systems using local features alone, our best system achieves a 23.56% (25.26%) relative error reduction on the recall and 17.10% (11.36%) relative error reduction on the F1 score; the improved F1 score 89.38% (90.09%) is significantly superior to the best NER system with F1 of 86.51% (89.03%) participated in the closed track.


title: Practical considerations for designing IPT system for EV battery charging

abstract: The paper proposes a hands-free Inductive Power Transfer system for charging the batteries of an Electrical Vehicle. A typical system comprises a power supply, a transfer pad on the ground, a receiving pad on the vehicle together and a power regulator. This paper describes the design of the power regulator which must ensure continuous power flow at high efficiency despite wide separation between the charging pads as a result of variation in the vehicle to ground heights. The paper also discusses the need to meet new and stringent electromagnetic field exposure regulations. Measurements show good efficiency across various separations and emissions compliance providing the pad is placed underneath the vehicle.


title: Being Barbie: The Size of One’s Own Body Determines the Perceived Size of the World

abstract: A classical question in philosophy and psychology is if the sense of one's body influences how one visually perceives the world. Several theoreticians have suggested that our own body serves as a fundamental reference in visual perception of sizes and distances, although compelling experimental evidence for this hypothesis is lacking. In contrast, modern textbooks typically explain the perception of object size and distance by the combination of information from different visual cues. Here, we describe full body illusions in which subjects experience the ownership of a doll's body (80 cm or 30 cm) and a giant's body (400 cm) and use these as tools to demonstrate that the size of one's sensed own body directly influences the perception of object size and distance. These effects were quantified in ten separate experiments with complementary verbal, questionnaire, manual, walking, and physiological measures. When participants experienced the tiny body as their own, they perceived objects to be larger and farther away, and when they experienced the large-body illusion, they perceived objects to be smaller and nearer. Importantly, despite identical retinal input, this "body size effect" was greater when the participants experienced a sense of ownership of the artificial bodies compared to a control condition in which ownership was disrupted. These findings are fundamentally important as they suggest a causal relationship between the representations of body space and external space. Thus, our own body size affects how we perceive the world.


title: A crucial role for adipose tissue p53 in the regulation of insulin resistance

abstract: A role for cell senescence and p53 in the development of insulin resistance (or prediabetes) has been obscure. Issei Komuro and colleagues now show that premature cell senescence occurs in the adipose tissue of obese mice and humans and that genetic deficiency of p53 is sufficient to prevent insulin resistance in mouse models of obesity, suggesting a new target to treat diabetes.


title: Attention-Aware Face Hallucination via Deep Reinforcement Learning

abstract: Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images. In contrast to existing methods that often learn a single patch-to-patch mapping from LR to HR images and are regardless of the contextual interdependency between patches, we propose a novel Attention-aware Face Hallucination (Attention-FH) framework which resorts to deep reinforcement learning for sequentially discovering attended patches and then performing the facial part enhancement by fully exploiting the global interdependency of the image. Specifically, in each time step, the recurrent policy network is proposed to dynamically specify a new attended region by incorporating what happened in the past. The state (i.e., face hallucination result for the whole image) can thus be exploited and updated by the local enhancement network on the selected region. The Attention-FH approach jointly learns the recurrent policy network and local enhancement network through maximizing the long-term reward that reflects the hallucination performance over the whole image. Therefore, our proposed Attention-FH is capable of adaptively personalizing an optimal searching path for each face image according to its own characteristic. Extensive experiments show our approach significantly surpasses the state-of-the-arts on in-the-wild faces with large pose and illumination variations.


title: Micro pneumatic curling actuator - Nematode actuator -

abstract: Micro rubber pneumatic actuators have attracted in medical and biotechnology fields, especially they have been expected for minimally invasive surgery and handling biological cells because of high safety caused by low mechanical impedance.


title: On Resilience of Split-Architecture Networks

abstract: The split architecture network assumes a logically centralized controller, which is physically separated from a large set of data plane forwarding switches. When the control plane becomes decoupled from the data plane, the requirement to the failure resilience and recovery mechanisms changes. In this work we investigate one of the most important practical issues in split architecture deployment, the placement of controllers in a given network. We first demonstrate that the location of controllers have high impact on the network resilience using a real network topology. Motivated by such observation, we propose a min-cut based controller placement algorithm and compare it with greedy based approach. Our simulation results show significant reliability improvements with an intelligent placement strategy. Our work is the first attempt on the resilience properties of a split architecture network.


title: 6LoWPAN security: adding compromise resilience to the 802.15.4 security sublayer

abstract: 6LoWPAN is a protocol stack for seamlessly integrating 802.15.4-based wireless sensor networks with IPv6 networks. The security of 6LoWPAN widely depends on the 802.15.4 security sublayer. This sublayer also supports pairwise keys so as to mitigate node compromises. Currently, the establishment of pairwise keys is however unspecified. Moreover, broadcast keys are shared among multiple nodes, which is not compromise resilient. In this paper, we propose two energy-efficient and DoS-resilient 802.15.4 add-ons to fill these gaps: First, a pairwise key establishment scheme, which is adaptable to different 6LoWPAN networks and threat models. Second, an easy-to-implement and compromise-resilient protocol for authenticating broadcast frames. Together, our add-ons contain the effects of node compromises and provide a basis for detecting compromised nodes autonomously. We implemented both add-ons in Contiki and tested them on TelosB motes.


title: Learning Visual Importance for Graphic Designs and Data Visualizations

abstract: Knowing where people look and click on visual designs can provide clues about how the designs are perceived, and where the most important or relevant content lies. The most important content of a visual design can be used for effective summarization or to facilitate retrieval from a database. We present automated models that predict the relative importance of different elements in data visualizations and graphic designs. Our models are neural networks trained on human clicks and importance annotations on hundreds of designs. We collected a new dataset of crowdsourced importance, and analyzed the predictions of our models with respect to ground truth importance and human eye movements. We demonstrate how such predictions of importance can be used for automatic design retargeting and thumbnailing. User studies with hundreds of MTurk participants validate that, with limited post-processing, our importance-driven applications are on par with, or outperform, current state-of-the-art methods, including natural image saliency. We also provide a demonstration of how our importance predictions can be built into interactive design tools to offer immediate feedback during the design process.


title: Stereo vision-based obstacle avoidance for micro air vehicles using disparity space

abstract: We address obstacle avoidance for outdoor flight of micro air vehicles. The highly textured nature of outdoor scenes enables camera-based perception, which will scale to very small size, weight, and power with very wide, two-axis field of regard. In this paper, we use forward-looking stereo cameras for obstacle detection and a downward-looking camera as an input to state estimation. For obstacle representation, we use image space with the stereo disparity map itself. We show that a C-space-like obstacle expansion can be done with this representation and that collision checking can be done by projecting candidate 3-D trajectories into image space and performing a z-buffer-like operation with the disparity map. This approach is very efficient in memory and computing time. We do motion planning and trajectory generation with an adaptation of a closed-loop RRT planner to quadrotor dynamics and full 3D search. We validate the performance of the system with Monte Carlo simulations in virtual worlds and flight tests of a real quadrotor through a grove of trees. The approach is designed to support scalability to high speed flight and has numerous possible generalizations to use other polar or hybrid polar/Cartesian representations and to fuse data from additional sensors, such as peripheral optical flow or radar.


title: APKLancet: tumor payload diagnosis and purification for android applications

abstract: A huge number of Android applications are bundled with relatively independent modules either during the development or by intentionally repackaging. Undesirable behaviors such as stealthily acquiring and distributing user's private information are frequently discovered in some bundled third-party modules, i.e., advertising libraries or malicious code (we call the module tumor payload in this work), which sabotage the integrity of the original app and lie as a threat to both the security of mobile system and the user's privacy. In this paper, we discuss how to purify an Android APK by resecting the tumor payload. Our work is based on two observations: 1) the tumor payload has its own characteristics, so it could be spotted through program analysis, and 2) the tumor payload is a relatively independent module so it can be resected without affecting the original app's function. We propose APKLancet, an automatic Android application diagnosis and purification system, to detect and resect the tumor payload. Relying on features extracting from ad libraries, analytics plugins and an approximately 8,000 malware samples, APKLancet is capable of diagnosing an APK and discovering unwelcome code fragment. Then it makes use of the code fragment as index to employ fine-grained program analysis and detaches the entire tumor payload. More precisely, it conducts an automatic app patching process to preserve the original normal functions while resecting tumor payload. We test APKLancet by the Android apps bundled with representative tumor payloads from online sandbox system. The result shows that the purification process is feasible to resect tumor payload and repair the apps. Moreover, all of the above do not require any Android system modification, and the purified app does not introduce any performance latency.


title: On Networking of Internet of Things: Explorations and Challenges

abstract: Internet of Things (IoT), as the trend of future networks, begins to be used in many aspects of daily life. It is of great significance to recognize the networking problem behind developing IoT. In this paper, we first analyze and point out the key problem of IoT from the perspective of networking: how to interconnect large-scale heterogeneous network elements and exchange data efficiently. Combining our on-going works, we present some research progresses on three main aspects: 1) the basic model of IoT architecture; 2) the internetworking model; and 3) the sensor-networking mode. Finally, we discuss two remaining challenges in this area.


title: A Cloud-Based Knowledge Discovery System for Monitoring Fine-Grained Air Quality

abstract: Many developing countries are suffering from air pollution recently. Governments have built a few air quality monitoring stations in cities to inform people the concentration of air pollutants. Unfortunately, urban air quality is highly skewed in a city, depending on multiple complex factors, such as the meteorology, traffic volume, and land uses. Building more monitoring stations is very costly in terms of money, land uses, and human resources. As a result, people do not really know the fine-grained air quality of a location without a monitoring station. In this paper, we introduce a cloud-based knowledge discovery system that infers the real-time and fine-grained air quality information throughout a city based on the (historical and realtime) air quality data reported by existing monitor stations and a variety of data sources observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). The system also provides a mobile client, with which a user can monitor the air quality of multiple locations in a city (e.g. the current location, home and work places), and a web service that allows other applications to call the air quality of any location. The system has been evaluated based on the real data from 9 cities in China, including Beijing, Shanghai, Guanzhou, and Shenzhen, etc. The system is running on Microsoft Azure and the mobile client is publicly available in Window Phone App Store, entitled Urban Air. Our system gives a cost-efficient example for enabling a knowledge discovery prototype involving big data on the cloud.


title: Manifestations of Personality in Online Social Networks: Self-Reported Facebook-Related Behaviors and Observable Profile Information

abstract: Abstract Despite the enormous popularity of Online Social Networking sites (OSNs; e.g., Facebook and Myspace), little research in psychology has been done on them. Two studies examining how personality is reflected in OSNs revealed several connections between the Big Five personality traits and self-reported Facebook-related behaviors and observable profile information. For example, extraversion predicted not only frequency of Facebook usage (Study 1), but also engagement in the site, with extraverts (vs. introverts) showing traces of higher levels of Facebook activity (Study 2). As in offline contexts, extraverts seek out virtual social engagement, which leaves behind a behavioral residue in the form of friends lists and picture postings. Results suggest that, rather than escaping from or compensating for their offline personality, OSN users appear to extend their offline personalities into the domains of OSNs.


title: Nonverbal Communication: How To Read What's Plain as the Nose… or Eyelid… or Chin… on Their Faces

abstract: For most people, “reading” others' nonverbal messages is an intuitive, “gut” process. But learning to do it consciously and deliberately can enhance understanding and thus greatly improve interpersonal relations in a wide variety of situations. At work, it can be a useful tool to facilitate decision making and ease work processes—for example, during interviews or at meetings. © 2001 John Wiley & Sons, Inc.


title: Emotion detection in suicide notes

abstract: The success of suicide prevention, a major public health concern worldwide, hinges on adequate suicide risk assessment. Online platforms are increasingly used for expressing suicidal thoughts, but manual monitoring is unfeasible given the information overload experts are confronted with. We investigate whether the recent advances in natural language processing, and more specifically in sentiment mining, can be used to accurately pinpoint 15 different emotions, which might be indicative of suicidal behavior. A system for automatic emotion detection was built using binary support vector machine classifiers. We hypothesized that lexical and semantic features could be an adequate way to represent the data, as emotions seemed to be lexicalized consistently. The optimal feature combination for each of the different emotions was determined using bootstrap resampling. Spelling correction was applied to the input data, in order to reduce lexical variation. Classification performance varied between emotions, with scores up to 68.86% F-score. F-scores above 40% were achieved for six of the seven most frequent emotions: thankfulness, guilt, love, information, hopelessness and instructions. The most salient features are trigram and lemma bags-of-words and subjectivity clues. Spelling correction had a slightly positive effect on classification performance. We showed that fine-grained automatic emotion detection benefits from classifier optimization and a combined lexico-semantic feature representation. The modest performance improvements obtained through spelling correction might indicate the robustness of the system to noisy input text. We conclude that natural language processing techniques have future application potential for suicide prevention.


Enabling ENVISAT deorbiting: a multi-spacecraft mission for inspection, capture, and detumbling


title: Searching for Biophysically Realistic Parameters for Dynamic Neuron Models by Genetic Algorithms from Calcium Imaging Recording

abstract: Individual Neurons in the nervous systems exploit various dynamics. To capture these dynamics for single neurons, we tune the parameters of an electrophysiological model of nerve cells, to fit experimental data obtained by calcium imaging. A search for the biophysical parameters of this model is performed by means of a genetic algorithm, where the model neuron is exposed to a predefined input current representing overall inputs from other parts of the nervous system. The algorithm is then constrained for keeping the ion-channel currents within reasonable ranges, while producing the best fit to a calcium imaging time series of the AVA interneuron, from the brain of the soil-worm, C. elegans. Our settings enable us to project a set of biophysical parameters to the the neuron kinetics observed in neuronal imaging.


title: A Gain/Efficiency-Improved Serial-Parallel Switched-Capacitor Step-Up DC–DC Converter

abstract: A new scheme of a gain/efficiency-improved serial- parallel switched-capacitor converter (SPSCC) is proposed by combining an adaptive-conversion-ratio (ACR) and pulse-width- modulation (PWM) control for step-up DC-DC conversion. For improving gain, the power part consists of one mc-stage cell and one nc-stage cell in cascade to obtain the step-up gain of (mc+1)×(nc+1) at most. For improving efficiency, the ACR control with adapting stage number m,n is presented to select a proper gain of (m+1)×(n+1) ( m=1,2,...,mc, n=1,2,...,nc) according to the desired output. Further, the PWM control is adopted to enhance output regulation as well as robustness to source/loading variation. Some theoretical analysis and control design include: formulation, steady-state analysis, conversion ratio, efficiency, output ripple, stability, capacitance selection, and control design. Finally, the performance of this scheme is verified experimentally on a SPSCC prototype, and all results are illustrated to show the efficacy of this scheme.


title: Abstractive Text Summarization based on Improved Semantic Graph Approach

abstract: The goal of abstractive summarization of multi-documents is to automatically produce a condensed version of the document text and maintain the significant information. Most of the graph-based extractive methods represent sentence as bag of words and utilize content similarity measure, which might fail to detect semantically equivalent redundant sentences. On other hand, graph based abstractive method depends on domain expert to build a semantic graph from manually created ontology, which requires time and effort. This work presents a semantic graph approach with improved ranking algorithm for abstractive summarization of multi-documents. The semantic graph is built from the source documents in a manner that the graph nodes denote the predicate argument structures (PASs)—the semantic structure of sentence, which is automatically identified by using semantic role labeling; while graph edges represent similarity weight, which is computed from PASs semantic similarity. In order to reflect the impact of both document and document set on PASs, the edge of semantic graph is further augmented with PAS-to-document and PAS-to-document set relationships. The important graph nodes (PASs) are ranked using the improved graph ranking algorithm. The redundant PASs are reduced by using maximal marginal relevance for re-ranking the PASs and finally summary sentences are generated from the top ranked PASs using language generation. Experiment of this research is accomplished using DUC-2002, a standard dataset for document summarization. Experimental findings signify that the proposed approach shows superior performance than other summarization approaches.


Relational Learning of Pattern-Match Rules for Information Extraction


The relationship between attachment scales and group therapy alliance growth differs by treatment type for women with binge-eating disorder.


title: Indoor Scene Parsing with Instance Segmentation, Semantic Labeling and Support Relationship Inference

abstract: Over the years, indoor scene parsing has attracted a growing interest in the computer vision community. Existing methods have typically focused on diverse subtasks of this challenging problem. In particular, while some of them aim at segmenting the image into regions, such as object or surface instances, others aim at inferring the semantic labels of given regions, or their support relationships. These different tasks are typically treated as separate ones. However, they bear strong connections: good regions should respect the semantic labels, support can only be defined for meaningful regions, support relationships strongly depend on semantics. In this paper, we therefore introduce an approach to jointly segment the instances and infer their semantic labels and support relationships from a single input image. By exploiting a hierarchical segmentation, we formulate our problem as that of jointly finding the regions in the hierarchy that correspond to instances and estimating their class labels and pairwise support relationships. We express this via a Markov Random Field, which allows us to further encode links between the different types of variables. Inference in this model can be done exactly via integer linear programming, and we learn its parameters in a structural SVM framework. Our experiments on NYUv2 demonstrate the benefits of reasoning jointly about all these subtasks of indoor scene parsing.


title: CommonRoad: Composable benchmarks for motion planning on roads

abstract: Numerical experiments for motion planning of road vehicles require numerous components: vehicle dynamics, a road network, static obstacles, dynamic obstacles and their movement over time, goal regions, a cost function, etc. Providing a description of the numerical experiment precise enough to reproduce it might require several pages of information. Thus, only key aspects are typically described in scientific publications, making it impossible to reproduce results — yet, re-producibility is an important asset of good science. Composable benchmarks for motion planning on roads (CommonRoad) are proposed so that numerical experiments are fully defined by a unique ID; all information required to reconstruct the experiment can be found on the CommonRoad website. Each benchmark is composed by a vehicle model, a cost function, and a scenario (including goals and constraints). The scenarios are partly recorded from real traffic and partly hand-crafted to create dangerous situations. We hope that CommonRoad saves researchers time since one does not have to search for realistic parameters of vehicle dynamics or realistic traffic situations, yet provides the freedom to compose a benchmark that fits one's needs.