title: Azure accelerated networking: SmartNICs in the public cloud

abstract: Modern cloud architectures rely on each server running its own networking stack to implement policies such as tunneling for virtual networks, security, and load balancing. However, these networking stacks are becoming increasingly complex as features are added and as network speeds increase. Running these stacks on CPU cores takes away processing power from VMs, increasing the cost of running cloud services, and adding latency and variability to network performance. ::: ::: We present Azure Accelerated Networking (AccelNet), our solution for offloading host networking to hardware, using custom Azure SmartNICs based on FPGAs. We define the goals of AccelNet, including programmability comparable to software, and performance and efficiency comparable to hardware. We show that FPGAs are the best current platform for offloading our networking stack as ASICs do not provide sufficient programmability, and embedded CPU cores do not provide scalable performance, especially on single network flows. ::: ::: Azure SmartNICs implementing AccelNet have been deployed on all new Azure servers since late 2015 in a fleet of >1M hosts. The AccelNet service has been available for Azure customers since 2016, providing consistent <15µs VM-VM TCP latencies and 32Gbps throughput, which we believe represents the fastest network available to customers in the public cloud. We present the design of AccelNet, including our hardware/software codesign model, performance results on key workloads, and experiences and lessons learned from developing and deploying AccelNet on FPGA-based Azure SmartNICs.


title: Sleep Deprivation and False Memories

abstract: Many studies have investigated factors that affect susceptibility to false memories. However, few have investigated the role of sleep deprivation in the formation of false memories, despite overwhelming evidence that sleep deprivation impairs cognitive function. We examined the relationship between self-reported sleep duration and false memories and the effect of 24 hr of total sleep deprivation on susceptibility to false memories. We found that under certain conditions, sleep deprivation can increase the risk of developing false memories. Specifically, sleep deprivation increased false memories in a misinformation task when participants were sleep deprived during event encoding, but did not have a significant effect when the deprivation occurred after event encoding. These experiments are the first to investigate the effect of sleep deprivation on susceptibility to false memories, which can have dire consequences.


Anomaly Detection Based on Trajectory Analysis Using Kernel Density Estimation and Information Bottleneck Techniques


title: Mixtures of ARMA models for model-based time series clustering

abstract: Clustering problems are central to many knowledge discovery and data mining tasks. However, most existing clustering methods can only work with fixed-dimensional representations of data patterns. In this paper we study the clustering of data patterns that are represented as sequences or time series possibly of different lengths. We propose a model-based approach to this problem using mixtures of autoregressive moving average (ARMA) models. We derive an expectation-maximization (EM) algorithm for learning the mixing coefficients as well as the parameters of component models. Experiments were conducted on simulated and real datasets. Results show that our method compares favorably with another method recently proposed by others for similar time series clustering problems.


title: Eh?Placer: A High-Performance Modern Technology-Driven Placer

abstract: The placement problem has become more complex and challenging due to a wide variety of complicated constraints imposed by modern process technologies. Some of the most challenging constraints and objectives were highlighted during the most recent ACM/IEEE International Symposium on Physical Design (ISPD) contests. In this article, the framework of Eh?Placer and its developed algorithms are elaborated, with the main focus on modern technology constraints and runtime. The technology constraints considered as part of Eh?Placer are fence region, target density, and detailed routability constraints. We present a complete description on how these constraints are considered in different stages of Eh?Placer. The results obtained from the contests indicate that Eh?Placer is able to efficiently handle modern technology constraints and ranks highly among top academic placement tools.


OpinionBlocks : Visualizing Consumer Reviews


title: An Efficient Algorithm for Matrix-Valued and Vector-Valued Optimal Mass Transport

abstract: We present an efficient algorithm for recent generalizations of optimal mass transport theory to matrix-valued and vector-valued densities. These generalizations lead to several applications including diffusion tensor imaging, color images processing, and multi-modality imaging. The algorithm is based on sequential quadratic programming (SQP). By approximating the Hessian of the cost and solving each iteration in an inexact manner, we are able to solve each iteration with relatively low cost while still maintaining a fast convergent rate. The core of the algorithm is solving a weighted Poisson equation, where different efficient preconditioners may be employed. We utilize incomplete Cholesky factorization, which yields an efficient and straightforward solver for our problem. Several illustrative examples are presented for both the matrix and vector-valued cases.


title: On optimization methods for deep learning

abstract: The predominant methodology in training deep learning advocates the use of stochastic gradient descent methods (SGDs). Despite its ease of implementation, SGDs are difficult to tune and parallelize. These problems make it challenging to develop, debug and scale up deep learning algorithms with SGDs. In this paper, we show that more sophisticated off-the-shelf optimization methods such as Limited memory BFGS (L-BFGS) and Conjugate gradient (CG) with line search can significantly simplify and speed up the process of pretraining deep algorithms. In our experiments, the difference between L-BFGS/CG and SGDs are more pronounced if we consider algorithmic extensions (e.g., sparsity regularization) and hardware extensions (e.g., GPUs or computer clusters). Our experiments with distributed optimization support the use of L-BFGS with locally connected networks and convolutional neural networks. Using L-BFGS, our convolutional network model achieves 0.69% on the standard MNIST dataset. This is a state-of-the-art result on MNIST among algorithms that do not use distortions or pretraining.


title: Phenotypic Characterization of a Genetically Diverse Panel of Mice for Behavioral Despair and Anxiety

abstract: Background ::: Animal models of human behavioral endophenotypes, such as the Tail Suspension Test (TST) and the Open Field assay (OF), have proven to be essential tools in revealing the genetics and mechanisms of psychiatric diseases. As in the human disorders they model, the measurements generated in these behavioral assays are significantly impacted by the genetic background of the animals tested. In order to better understand the strain-dependent phenotypic variability endemic to this type of work, and better inform future studies that rely on the data generated by these models, we phenotyped 33 inbred mouse strains for immobility in the TST, a mouse model of behavioral despair, and for activity in the OF, a model of general anxiety and locomotor activity.


title: Cybersecurity in healthcare: A systematic review of modern threats and trends

abstract: BACKGROUND ::: The adoption of healthcare technology is arduous, and it requires planning and implementation time. Healthcare organizations are vulnerable to modern trends and threats because it has not kept up with threats. ::: ::: ::: OBJECTIVE ::: The objective of this systematic review is to identify cybersecurity trends, including ransomware, and identify possible solutions by querying academic literature. ::: ::: ::: METHODS ::: The reviewers conducted three separate searches through the CINAHL and PubMed (MEDLINE) and the Nursing and Allied Health Source via ProQuest databases. Using key words with Boolean operators, database filters, and hand screening, we identified 31 articles that met the objective of the review. ::: ::: ::: RESULTS ::: The analysis of 31 articles showed the healthcare industry lags behind in security. Like other industries, healthcare should clearly define cybersecurity duties, establish clear procedures for upgrading software and handling a data breach, use VLANs and deauthentication and cloud-based computing, and to train their users not to open suspicious code. ::: ::: ::: CONCLUSIONS ::: The healthcare industry is a prime target for medical information theft as it lags behind other leading industries in securing vital data. It is imperative that time and funding is invested in maintaining and ensuring the protection of healthcare technology and the confidentially of patient information from unauthorized access.


title: The Well-Played MOBA: How DotA 2 and League of Legends use Dramatic Dynamics

abstract: This paper will analyse the two most popular games within the MOBA genre, DotA 2 and League of Legends, as performance-designed spaces. By analysing MOBAs as performance and using Marc LeBlanc’s (2006) Tools for Creating Dramatic Game Dynamics as an aesthetic framework the aim is to posit a greater understanding of the ways in which e-Sports and MOBAs specifically can be designed in order to create dramatic tension within the increasing variety of available viewing platforms. In this way, this paper helps present new ways to think about how games can be designed/structured in order to be satisfyingly performed and consumed through increasingly diverse viewing methods.


title: Dual Business Models: Going beyond Spatial Separation

abstract: Despite substantial literature on managing two conflicting business models within one company, little has been done regarding qualitative studies investigating the organizational context and the installed integration and separation mechanisms between these two. Based on organizational context factors and business model elements, using the organizational ambidexterity as theoretical lens, this research paper presents a longitudinal case study conducted at a magazine publisher that intends to advance the comprehension of the interplay between traditional and online business models. Our analysis suggests that integration and separation mechanisms occur along all elements of a business model. The organizational context factors moderate the integration and separation mechanisms between the traditional and the online business model and uncover details regarding the conflict inherent in those dual business models.


title: Mixing bandits: a recipe for improved cold-start recommendations in a social network

abstract: Recommending items to new or "cold-start" users is a challenging problem for recommender systems. Collaborative filtering approaches fail when the preference history of users is not available. A promising direction that has been explored recently [12] is to utilize the information in the social networks of users to improve the quality of cold-start recommendations. That is, given that users are part of a social network, a new user shows up in the network with no preference history and limited social links, the recommender system tries to learn the user's tastes as fast as possible. In this work, we model the learning of preferences of cold-start users using multi-armed bandits [5] embedded in a social network. We propose two novel strategies leveraging neighborhood estimates to improve the learning rate of bandits for cold-start users. Our first strategy, MixPair, combines estimates from pairs of neighboring bandits. It extends the well-known UCB1 algorithm [5] and inherits its asymptotically optimal guarantees. Although our second strategy, MixNeigh, is a heuristic based on consensus in the neighborhood of a user, it performed the best among the evaluated strategies. Our experiments on a dataset from Last.fm show that our strategies yield significant improvements, learning 2 to 5 times faster than our baseline, UCB1.


title: Customer churn prediction using improved balanced random forests

abstract: Churn prediction is becoming a major focus of banks in China who wish to retain customers by satisfying their needs under resource constraints. In churn prediction, an important yet challenging problem is the imbalance in the data distribution. In this paper, we propose a novel learning method, called improved balanced random forests (IBRF), and demonstrate its application to churn prediction. We investigate the effectiveness of the standard random forests approach in predicting customer churn, while also integrating sampling techniques and cost-sensitive learning into the approach to achieve a better performance than most existing algorithms. The nature of IBRF is that the best features are iteratively learned by altering the class distribution and by putting higher penalties on misclassification of the minority class. We apply the method to a real bank customer churn data set. It is found to improve prediction accuracy significantly compared with other algorithms, such as artificial neural networks, decision trees, and class-weighted core support vector machines (CWC-SVM). Moreover, IBRF also produces better prediction results than other random forests algorithms such as balanced random forests and weighted random forests.


title: Biomarkers to Monitor Gluten-Free Diet Compliance in Celiac Patients

abstract: Gluten-free diet (GFD) is the only treatment for celiac disease (CD). There is a general consensus that strict GFD adherence in CD patients leads to full clinical and histological remission accompanied by improvement in quality of life and reduced long-term complications. Despite the importance of monitoring the GFD, there are no clear guidelines for assessing the outcome or for exploring its adherence. Available methods are insufficiently accurate to identify occasional gluten exposure that may cause intestinal mucosal damage. Serological tests are highly sensitive and specific for diagnosis, but do not predict recovery and are not useful for follow-up. The use of serial endoscopies, it is invasive and impractical for frequent monitoring, and dietary interview can be subjective. Therefore, the detection of gluten immunogenic peptides (GIP) in feces and urine have been proposed as new non-invasive biomarkers to detect gluten intake and verify GFD compliance in CD patients. These simple immunoassays in human samples could overcome some key unresolved scientific and clinical problems in CD management. It is a significant advance that opens up new possibilities for the clinicians to evaluate the CD treatment, GFD compliance, and improvement in the quality of life of CD patients.


title: Control Law Design for Haptic Interfaces to Virtual Reality

abstract: The goal of control law design for haptic displays is to provide a safe and stable user interface while maximizing the operator's sense of kinesthetic immersion in a virtual environment. This paper outlines a control design approach which stabilizes a haptic interface when coupled to a broad class of human operators and virtual environments. Two-port absolute stability criteria are used to develop explicit control law design bounds for two different haptic display implementations: the impedance display and admittance display. The strengths and weaknesses of each approach are illustrated through numerical and experimental results for a three degree-of-freedom device. The example highlights the ability of the proposed design procedure to handle some of the more difficult problems in control law synthesis for haptics, including structural flexibility and noncollocation of sensors and actuators.


title: A TCP-friendly rate adjustment protocol for continuous media flows over best effort networks

abstract: TR: 98-047 Title: A TCP-Friendly Rate Adjustment Protocol for Continuous Media Flows over Best Effort Networks Authors: Jitendra Padhye, Jim Kurose, Don Towsley and Rajeev Koodli Address: Department of Computer Science University of Massachusetts LGRC, Box 34610. Amherst MA 01003-4610 Date: October 23, 1998 As networked multimedia applications become widespread, it becomes increasingly important to ensure that these applications can coexist with current TCP-based applications. The TCP protocol is designed to reduce its sending rate when congestion is detected. Networked multimedia applications should exhibit similar behavior, if they wish to co-exist with TCP-based applications [1]. Using TCP for multimedia applications is not practical, since the protocol combines error control and congestion control, in order to ensure reliability. In this paper we present a protocol that operates by measuring loss rates and round trip times and then sets the transmission rate to that which TCP would achieve under similar conditions. The analysis in [2] is used to determine this "TCP-friendly" rate. We evaluate the protocol under various traffic conditions, using simulations and implementation. References: [1] J. Madhavi and S. Floyd, "TCP-friendly unicast rate-based flow control", Note sent to end2end-interest mailing list, January 1997. [2] J. Padhye, V. Firoiu, D. Towsley and J. Kurose, "Modeling TCP throughput: A simple model and its empirical validation", In Proceedings of SIGCOMM''98.


title: A Simulation-Based Architecture for Smart Cyber-Physical Systems

abstract: In order to accurately predict future states of a smart cyber-physical system, which can change its behavior to a large degree in response to environmental influences, the existence of precise models of the system and its surroundings is demandable. In machine engineering, ultra-high fidelity simulations have been developed to better understand both constraints in system design and possible consequences of external influences during the system's operation. These digital twins enable further applications in software design for complex cyber-physical systems as online planning methods can utilize good simulations to continuously optimize the system behavior, yielding a software architecture framework based on the information flow between the cyber-physical system, its physical environment and the digital twin model.


title: Traffic Management and Forecasting System Based on 3D GIS

abstract: This is the preprint version of our paper on 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). This paper takes Shenzhen Futian comprehensive transportation junction as the case, and makes use of continuous multiple real-time dynamic traffic information to carry out monitoring and analysis on spatial and temporal distribution of passenger flow under different means of transportation and service capacity of junction from multi-dimensional space-time perspectives such as different period and special period. Virtual reality geographic information system is employed to present the forecasting result.


title: An offline planning approach to game plotline adaptation

abstract: Role-playing games, and other types of contemporary video games, usually contain a main storyline consisting of several causally related quests. As players have different motivations, tastes and preferences, it can be beneficial to customize game plotlines. In this paper, we present an offline algorithm for adapting human-authored game plotlines for computer role-playing games to suit the unique needs of individual players, thereby customizing gaming experiences and enhancing replayability. Our approach uses an plan refinement technique based on partial-order planning to (a) optimize the global structure of the plotline according to input from a player model, (b) maintain plotline coherence, and (c) facilitate authorial intent by preserving as much of the original plotline as possible. A theoretical analysis of the authorial leverage and a user study suggest the benefits of this approach.


title: Context-aware robot navigation using interactively built semantic maps

abstract: We discuss the process of building semantic maps, how to interactively label entities in it, and use them to enable new navigation behaviors for specific scenarios. We utilize planar surfaces such as walls and tables, and static objects such as door signs as features to our semantic mapping approach. Users can interactively annotate these features by having the robot follow him/her, entering the label through a mobile app and performing a pointing gesture toward the landmark of interest. These landmarks can later be used to generate context-aware motions. Our pointing gesture approach can reliably estimate the target object using human joint positions and detect ambiguous gestures with probabilistic modeling. Our person following method attempts to maximize future utility by searching future actions, assuming constant velocity model for the human. We describe a simple method to extract metric goals from a semantic map landmark and present a human aware path planner that considers the personal spaces of people to generate socially-aware paths. Finally, we demonstrate context-awareness for person following in two scenarios: interactive labeling and door passing. We believe as the sensing technology improves and maps with richer semantic information becomes commonplace, it would create new opportunities for intelligent navigation algorithms.


title: Oral S-adenosylmethionine in Primary Fibromyalgia. Double-blind Clinical Evaluation

abstract: S-adenosylmethionine is a relatively new anti-inflammatory drug with arjalgesic and antidepressant effects. Efficacy of 800 mg orally administered s-adenosylmethionine daily versus placebo for six weeks was investigated in 44 patients with primary fibromyalgia in double-blind settings. Tender point score, isokinetic muscle strength, disease activity, subjective symptoms (visual analog scale), mood parameters and side effects were evaluated. Improvements were seen for clinical disease activity (P=0.04), pain experienced during the last week (P=0.002), fatigue (P=0.02), morning stiffness (P=0.03) and mood evaluated by Face Scale (P=0.006) in the actively treated group compared to placebo. The tender point score, isokinetic muscle strength, mood evaluated by Beck Depression Inventory and side effects did not differ in the two treatment groups. S-adenosylmethionine has some beneficial effects on primary fibromyalgia and could be an important option in the treatment hereof.


title: The major bioactive components of seaweeds and their mosquitocidal potential

abstract: Seaweeds are one of the most widely studied natu- ral resources for their biological activities. Novel seaweed compounds with unique chemical structures have been report- ed for their pharmacological properties. The urge to search for novel insecticidal compound with a new mode of action for development of botanical insecticides supports the relevant scientific research on discovering the bioactive compounds in seaweeds. The mosquitocidal potential of seaweed extracts and their isolated compounds are documented in this review paper, along with the discussion on bioactivities of the major components of seaweeds such as polysaccharides, phenolics, proteins, terpenes, lipids, and halogenated compounds. The effects of seaweed extracts and compounds toward different life stages of mosquito (egg, larva, pupa, and adult), its growth, development, and reproduction are elaborated. The structure-activity relationships of mosquitocidal compounds are discussed to extrapolate the possible chemical character- istics of seaweed compounds responsible for insecticidal properties. Furthermore, the possible target sites and mode of actions of the mosquitocidal seaweed compounds are in- cluded in this paper. The potential synergistic effects between seaweeds and commercial insecticides as well as the toxic effects of seaweed extracts and compounds toward other insects and non-target organisms in the same habitat are also described. On top of that, various factors that influence the mosquitocidal potential of seaweeds, such as abiotic and biotic variables, sample preparation, test procedures, and con- siderations for a precise experimental design are discussed. Thepotentialofactiveseaweedextractsandcompoundsinthe development of effective bioinsecticide are also discussed.


title: Storylines for structuring massive streams of news

abstract: Stories are the most natural ways for people to deal with information about the changing world. They provide an efficient schematic structure to order and relate events according to some explanation. We describe (1) a formal model for representing storylines to handle streams of news and (2) a first implementation of a system that automatically extracts the ingredients of a storyline from news articles according to the model. Our model mimics the basic notions from narratology by adding bridging relations to timelines of events in relation to a climax point. We provide a method for defining the climax score of each event and the bridging relations between them. We generate a JSON structure for any set of news articles to represent the different stories they contain and visualize these stories on a timeline with climax and bridging relations. This visualization helps inspecting the validity of the generated structures.


Affordance theory in the IS discipline: a review and synthesis of the literature


title: Brain Connectivity Networks and the Aesthetic Experience of Music

abstract: Listening to music is above all a human experience, which becomes an aesthetic experience when an individual immerses himself/herself in the music, dedicating attention to perceptual-cognitive-affective interpretation and evaluation. The study of these processes where the individual perceives, understands, enjoys and evaluates a set of auditory stimuli has mainly been focused on the effect of music on specific brain structures, as measured with neurophysiology and neuroimaging techniques. The very recent application of network science algorithms to brain research allows an insight into the functional connectivity between brain regions. These studies in network neuroscience have identified distinct circuits that function during goal-directed tasks and resting states. We review recent neuroimaging findings which indicate that music listening is traceable in terms of network connectivity and activations of target regions in the brain, in particular between the auditory cortex, the reward brain system and brain regions active during mind wandering.


title: An automatic modeling of human bodies from sizing parameters

abstract: In this paper, we present an automatic, runtime modeler for modeling realistic, animatable human bodies. A user can generate a new model or modify an existing one simply by inputting a number of sizing parameters.We approach the problem by forming deformation functions that are devoted to the generation of appropriate shape and proportion of the body geometry by taking the parameters as input. Starting from a number of 3D scanned data of human body models as examples, we derive these functions by using radial basis interpolation. A prerequisite of such formulation is to have correspondence among example models in the database. We obtain the correspondence by fitting a template onto each scanned data. Throughout the paper, body geometry is considered to have two distinct entities, namely rigid and elastic component of the deformation. The rigid deformation is represented by the corresponding joint parameters, which will determine the linear approximation of the physique. The elastic deformation is essentially vertex displacements, which, when added to the rigid deformation, depicts the detail shape of the body.Having these interpolators formulated, the runtime modeling can be reduced to the function evaluation and application of the evaluated results to the template model. We demonstrate our method by applying different parameters to generate a wide range of different body models.


title: Hardware protection and authentication through netlist level obfuscation

abstract: Hardware intellectual property (IP) cores have emerged as an integral part of modern system-on-chip (SoC) designs. However, IP vendors are facing major challenges to protect hardware IPs and to prevent revenue loss due to IP piracy. In this paper, we propose a novel design methodology for hardware IP protection and authentication using netlist level authentication. The proposed methodology can be integrated in the SoC design and manufacturing flow to provide hardware protection to the IP vendors, the chip designer, and the system designer. Simulation results on ISCAS-89 benchmark circuits show that we can achieve high levels of security through a well-formulated obfuscation scheme at less than 10% area overhead under delay constraint.


title: Dual origins of the Japanese: common ground for hunter-gatherer and farmer Y chromosomes

abstract: Historic Japanese culture evolved from at least two distinct migrations that originated on the Asian continent. Hunter-gatherers arrived before land bridges were submerged after the last glacial maximum (>12,000 years ago) and gave rise to the Jomon culture, and the Yayoi migration brought wet rice agriculture from Korea beginning ~2,300 years ago. A set of 81 Y chromosome single nucleotide polymorphisms (SNPs) was used to trace the origins of Paleolithic and Neolithic components of the Japanese paternal gene pool, and to determine the relative contribution of Jomon and Yayoi Y chromosome lineages to modern Japanese. Our global sample consisted of >2,500 males from 39 Asian populations, including six populations sampled from across the Japanese archipelago. Japanese populations were characterized by the presence of two major (D and O) and two minor (C and N) clades of Y chromosomes, each with several sub-lineages. Haplogroup D chromosomes were present at 34.7% and were distributed in a U-shaped pattern with the highest frequency in the northern Ainu and southern Ryukyuans. In contrast, haplogroup O lineages (51.8%) were distributed in an inverted U-shaped pattern with a maximum frequency on Kyushu. Coalescent analyses of Y chromosome short tandem repeat diversity indicated that haplogroups D and C began their expansions in Japan ~20,000 and ~12,000 years ago, respectively, while haplogroup O-47z began its expansion only ~4,000 years ago. We infer that these patterns result from separate and distinct genetic contributions from both the Jomon and the Yayoi cultures to modern Japanese, with varying levels of admixture between these two populations across the archipelago. The results also support the hypothesis of a Central Asian origin of Jomonese ancestors, and a Southeast Asian origin of the ancestors of the Yayoi, contra previous models based on morphological and genetic evidence.


title: Large-scale malware classification using random projections and neural networks

abstract: Automatically generated malware is a significant problem for computer users. Analysts are able to manually investigate a small number of unknown files, but the best large-scale defense for detecting malware is automated malware classification. Malware classifiers often use sparse binary features, and the number of potential features can be on the order of tens or hundreds of millions. Feature selection reduces the number of features to a manageable number for training simpler algorithms such as logistic regression, but this number is still too large for more complex algorithms such as neural networks. To overcome this problem, we used random projections to further reduce the dimensionality of the original input space. Using this architecture, we train several very large-scale neural network systems with over 2.6 million labeled samples thereby achieving classification results with a two-class error rate of 0.49% for a single neural network and 0.42% for an ensemble of neural networks.


title: Bag Constrained Structure Pattern Mining for Multi-Graph Classification

abstract: This paper formulates a multi-graph learning task. In our problem setting, a bag contains a number of graphs and a class label. A bag is labeled positive if at least one graph in the bag is positive, and negative otherwise. In addition, the genuine label of each graph in a positive bag is unknown, and all graphs in a negative bag are negative. The aim of multi-graph learning is to build a learning model from a number of labeled training bags to predict previously unseen test bags with maximum accuracy. This problem setting is essentially different from existing multi-instance learning (MIL), where instances in MIL share well-defined feature values, but no features are available to represent graphs in a multi-graph bag. To solve the problem, we propose a Multi-Graph Feature based Learning (gMGFL) algorithm that explores and selects a set of discriminative subgraphs as features to transfer each bag into a single instance, with the bag label being propagated to the transferred instance. As a result, the multi-graph bags form a labeled training instance set, so generic learning algorithms, such as decision trees, can be used to derive learning models for multi-graph classification. Experiments and comparisons on real-world multi-graph tasks demonstrate the algorithm performance.


title: Reverse Engineering CAPTCHAs

abstract: CAPTCHAs are automated Turing tests used to determine if the end-user is human and not an automated program. Users are asked to read and answer Visual CAPTCHAs, which often appear as bitmaps of text characters, in order to gain access to a low-cost resource such as webmail or a blog. CAPTCHAs are generated by software and the structure of a CAPTCHA gives hints to its implementation. Thus due to these properties of image processing and image composition, the process that creates CAPTCHAs can often be reverse engineered. Once the implementation strategy of a family of CAPTCHAs has been reverse engineered the CAPTCHA instances may be solved automatically by leveraging weaknesses in the creation process or by comparing a CAPTCHA's output against itself. In this paper, we present a case study where we reverse engineer and solve real-world CAPTCHAs using simple image processing techniques such as bitmap comparison, thresholding, fill-flood segmentation, dilation, and erosion. We present black-box and white-box methodologies for reverse engineering and solving CAPTCHAs. As well we provide an open source toolkit for solving CAPTCHAs that we have used with a success rates of 99, 95, 61, 30%, and 27% on hundreds of CAPTCHAs from five real-world examples.


title: X-Eye: A reference format for eye tracking data to facilitate analyses across databases

abstract: Datasets of images annotated with eye tracking data constitute important ground truth for the development of saliency models, which have applications in many areas of electronic imaging. While comparisons and reviews of saliency models abound, similar comparisons among the eye tracking databases themselves are rare. In an earlier paper, we reviewed the content and purpose of over two dozen databases available in the public domain and discussed their commonalities and differences. A major issue is that the formats of the various datasets vary a lot owing to the nature of tools used for eye movement recordings, and often specialized code is required to use the data for further analysis. In this paper, we therefore propose a common reference format for eye tracking data, together with conversion routines for 16 existing image eye tracking databases to that format. Furthermore, we conduct a few analyses on these datasets as examples of what X-Eye facilitates.


title: Sigma Point Belief Propagation

abstract: The sigma point (SP) filter, also known as unscented Kalman filter, is an attractive alternative to the extended Kalman filter and the particle filter. Here, we extend the SP filter to nonsequential Bayesian inference corresponding to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a low-complexity approximation of the belief propagation (BP) message passing scheme. SPBP achieves approximate marginalizations of posterior distributions corresponding to (generally) loopy factor graphs. It is well suited for decentralized inference because of its low communication requirements. For a decentralized, dynamic sensor localization problem, we demonstrate that SPBP can outperform nonparametric (particle-based) BP while requiring significantly less computations and communications.


title: Low profile cavity-backed four-arm Archimedean spiral antenna with 8:1 bandwidth

abstract: A wideband circularly polarized, four-arm Archimedean spiral antenna backed by a low-profile metallic cavity of 15 mm (0.03λ at the lowest frequency) with a 8:1 bandwidth from 0.6 GHz to 5 GHz is presented. A feed network comprising of a 90° wideband hybrid connected to two 180° hybrids excites the spiral antenna in the fundamental mode. For the entire 8:1 bandwidth, the measured input return loss of the system is more than 11.4 dB. Despite the low-profile cavity, the measured boresight gain and axial ratio of the antenna is -1 to 8.7 dBi and less than 2 dB respectively


title: Deep embedding convolutional neural network for synthesizing CT image from T1‐Weighted MR image

abstract: Recently, more and more attention is drawn to the field of medical image synthesis across modalities. Among them, the synthesis of computed tomography (CT) image from T1-weighted magnetic resonance (MR) image is of great importance, although the mapping between them is highly complex due to large gaps of appearances of the two modalities. In this work, we aim to tackle this MR-to-CT synthesis by a novel deep embedding convolutional neural network (DECNN). Specifically, we generate the feature maps from MR images, and then transform these feature maps forward through convolutional layers in the network. We can further compute a tentative CT synthesis from the midway of the flow of feature maps, and then embed this tentative CT synthesis back to the feature maps. This embedding operation results in better feature maps, which are further transformed forward in DECNN. After repeat-ing this embedding procedure for several times in the network, we can eventually synthesize a final CT image in the end of the DECNN. We have validated our proposed method on both brain and prostate datasets, by also compar-ing with the state-of-the-art methods. Experimental results suggest that our DECNN (with repeated embedding op-erations) demonstrates its superior performances, in terms of both the perceptive quality of the synthesized CT image and the run-time cost for synthesizing a CT image.


Recent Progress in Deep Learning for NLP


title: Runner BIB number recognition system

abstract: This research represents the runner BIB number recognition system to develop image processing study which solves problems and increases efficiency about runner image management in running fairs. The runner BIB number recognition system processes runner image to recognize BIB number and time when runner appears in media. The information from processing has collected to applicative later. BIB number position is on BIB tag which attach on runner body. To recognize BIB number, the system detects runner position first. This process emphasize on runner face detection in images following to concept of researcher then find BIB number in body-thigh area of runner. The system recognizes BIB number from BIB tag which represents in media. This processing presents 0.80 in precision value, 0.81 in recall value and F-measure is 0.80. The results display the runner BIB number recognition system has developed with high efficiency and can be applied for runner online communities in actual situation. The runner BIB number recognition system decreases problems about runner image processing and increases comfortable for runners when find images from running fairs. Moreover, the system can be applied in commercial to increase benefits in running business.


title: Cooperative Mobile Robotics : Antecedents and Directions

abstract: There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of cooperative robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.


title: Dual Learning for Machine Translation

abstract: While neural machine translation (NMT) is making good progress in the past two years, tens of millions of bilingual sentence pairs are needed for its training. However, human labeling is very costly. To tackle this training data bottleneck, we develop a dual-learning mechanism, which can enable an NMT system to automatically learn from unlabeled data through a dual-learning game. This mechanism is inspired by the following observation: any machine translation task has a dual task, e.g., English-to-French translation (primal) versus French-to-English translation (dual); the primal and dual tasks can form a closed loop, and generate informative feedback signals to train the translation models, even if without the involvement of a human labeler. In the dual-learning mechanism, we use one agent to represent the model for the primal task and the other agent to represent the model for the dual task, then ask them to teach each other through a reinforcement learning process. Based on the feedback signals generated during this process (e.g., the language-model likelihood of the output of a model, and the reconstruction error of the original sentence after the primal and dual translations), we can iteratively update the two models until convergence (e.g., using the policy gradient methods). We call the corresponding approach to neural machine translation dual-NMT. Experiments show that dual-NMT works very well on English ↔ French translation; especially, by learning from monolingual data (with 10% bilingual data for warm start), it achieves a comparable accuracy to NMT trained from the full bilingual data for the French-to-English translation task.


title: Chopper: partitioning models into 3D-printable parts

abstract: 3D printing technology is rapidly maturing and becoming ubiquitous. One of the remaining obstacles to wide-scale adoption is that the object to be printed must fit into the working volume of the 3D printer. We propose a framework, called Chopper, to decompose a large 3D object into smaller parts so that each part fits into the printing volume. These parts can then be assembled to form the original object. We formulate a number of desirable criteria for the partition, including assemblability, having few components, unobtrusiveness of the seams, and structural soundness. Chopper optimizes these criteria and generates a partition either automatically or with user guidance. Our prototype outputs the final decomposed parts with customized connectors on the interfaces. We demonstrate the effectiveness of Chopper on a variety of non-trivial real-world objects.


A geometric perspective on the robustness of deep networks


title: Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation

abstract: In this work, we address the following matrix recovery problem: suppose we are given a set of data points containing two parts, one part consists of samples drawn from a union of multiple subspaces and the other part consists of outliers. We do not know which data points are outliers, or how many outliers there are. The rank and number of the subspaces are unknown either. Can we detect the outliers and segment the samples into their right subspaces, efficiently and exactly? We utilize a so-called {\em Low-Rank Representation} (LRR) method to solve this problem, and prove that under mild technical conditions, any solution to LRR exactly recovers the row space of the samples and detect the outliers as well. Since the subspace membership is provably determined by the row space, this further implies that LRR can perform exact subspace segmentation and outlier detection, in an efficient way.


title: Behavioral Clustering of HTTP-Based Malware and Signature Generation Using Malicious Network Traces

abstract: We present a novel network-level behavioral malware clustering system. We focus on analyzing the structural similarities among malicious HTTP traffic traces generated by executing HTTP-based malware. Our work is motivated by the need to provide quality input to algorithms that automatically generate network signatures. Accordingly, we define similarity metrics among HTTP traces and develop our system so that the resulting clusters can yield high-quality malware signatures. ::: ::: We implemented a proof-of-concept version of our network-level malware clustering system and performed experiments with more than 25,000 distinct malware samples. Results from our evaluation, which includes real-world deployment, confirm the effectiveness of the proposed clustering system and show that our approach can aid the process of automatically extracting network signatures for detecting HTTP traffic generated by malware-compromised machines.


title: A survey of event detection techniques in online social networks

abstract: The online social networks (OSNs) have become an important platform for detecting real-world event in recent years. These real-world events are detected by analyzing huge social-stream data available on different OSN platforms. Event detection has become significant because it contains substantial information which describes different scenarios during events or crisis. This information further helps to enable contextual decision making, regarding the event location, content and the temporal specifications. Several studies exist, which offers plethora of frameworks and tools for detecting and analyzing events used for applications like crisis management, monitoring and predicting events in different OSN platforms. In this paper, a survey is done for event detection techniques in OSN based on social text streams—newswire, web forums, emails, blogs and microblogs, for natural disasters, trending or emerging topics and public opinion-based events. The work done and the open problems are explicitly mentioned for each social stream. Further, this paper elucidates the list of event detection tools available for the researchers.


title: Interactive cloth rendering of microcylinder appearance model under environment lighting

abstract: This paper proposes an interactive rendering method of cloth fabrics under environment lighting. The outgoing radiance from cloth fabrics in the microcylinder model is calculated by integrating the product of the distant environment lighting, the visibility function, the weighting function that includes shadowing/masking effects of threads, and the light scattering function of threads. The radiance calculation at each shading point of the cloth fabrics is simplified to a linear combination of triple product integrals of two circular Gaussians and the visibility function, multiplied by precomputed spherical Gaussian convolutions of the weighting function. We propose an efficient calculation method of the triple product of two circular Gaussians and the visibility function by using the gradient of signed distance function to the visibility boundary where the binary visibility changes in the angular domain of the hemisphere. Our GPU implementation enables interactive rendering of static cloth fabrics with dynamic viewpoints and lighting. In addition, interactive editing of parameters for the scattering function e.g. thread's albedo that controls the visual appearances of cloth fabrics can be achieved.


title: What universities can learn from corporations about quality control of students. A proposal from a Japanese perspective

abstract: This article suggests that a new tool, called 'process control', be considered to assure the quality of university students. Until now, entrance and exit controls have been the focus, with process control being a relatively new concept. What is it? And what mechanisms would make it possible? This paper examines the possibility of universities borrowing the quality control concept from corporations, calling it 'process control'. The authors conclude that corporate methods are not directly applicable to universities due to fundamental differences. However, there are benefits to be gained from utilizing corporate experience in quality control. Corporations can, for example: (1) help universities articulate the goals of quality control; (2) help formulate a method for process control; (3) help ensure reciprocity in assessment; and (4) help enhance communication within universities. The study and practice of quality control for university students are still in the early stages of development with the potential...


title: Classification of time series by shapelet transformation

abstract: Time-series classification (TSC) problems present a specific challenge for classification algorithms: how to measure similarity between series. A shapelet is a time-series subsequence that allows for TSC based on local, phase-independent similarity in shape. Shapelet-based classification uses the similarity between a shapelet and a series as a discriminatory feature. One benefit of the shapelet approach is that shapelets are comprehensible, and can offer insight into the problem domain. The original shapelet-based classifier embeds the shapelet-discovery algorithm in a decision tree, and uses information gain to assess the quality of candidates, finding a new shapelet at each node of the tree through an enumerative search. Subsequent research has focused mainly on techniques to speed up the search. We examine how best to use the shapelet primitive to construct classifiers. We propose a single-scan shapelet algorithm that finds the best $$k$$ k shapelets, which are used to produce a transformed dataset, where each of the $$k$$ k features represent the distance between a time series and a shapelet. The primary advantages over the embedded approach are that the transformed data can be used in conjunction with any classifier, and that there is no recursive search for shapelets. We demonstrate that the transformed data, in conjunction with more complex classifiers, gives greater accuracy than the embedded shapelet tree. We also evaluate three similarity measures that produce equivalent results to information gain in less time. Finally, we show that by conducting post-transform clustering of shapelets, we can enhance the interpretability of the transformed data. We conduct our experiments on 29 datasets: 17 from the UCR repository, and 12 we provide ourselves.


title: Ground reaction force and kinematic analysis of limb loading on two different beach sand tracks in harness trotters

abstract: Summary ::: ::: Reasons for performing study: Although beach training is commonly used in horses, limb loading on beach sand has never been investigated. A dynamometric horseshoe (DHS) is well adapted for this purpose. ::: ::: ::: ::: Objectives: To compare ground reaction force (GRF) and fetlock kinematics measured in harness trotters on 2 tracks of beach sand with different water content. ::: ::: ::: ::: Methods: Two linear sand tracks were compared: firm wet sand (FWS, 19% moisture) vs. deep wet sand (DWS, 13.5% moisture). Four French trotters (550 ± 22 kg) were used. Their right forelimb was equipped with a DHS and skin markers. Each track was tested 3 times at 7 m/s. Each trial was filmed by a high-speed camera (600 Hz); DHS and speed data acquisition was performed at 10 kHz on 10 consecutive strides. All recordings were synchronised. The components Fx (parallel to the hoof solar surface) and Fz (perpendicular) of the GRF were considered. For 3 horses the fetlock angle and forelimb axis-track angle at landing were measured. Statistical differences were tested using the GLM procedure (SAS; P<0.05). ::: ::: ::: ::: Results: Stance duration was increased on DWS compared to FWS. Fzmax and Fxmax (oriented, respectively, downwards and forwards relatively to the solar surface) and the corresponding loading rates, were decreased on DWS and these force peaks occurred later. Fxmin (backwards) was not significantly different between both surfaces; the propulsive phase (Fx negative) was longer and the corresponding impulse higher, on DWS compared to FWS. The forelimb was more oblique to the track at landing and maximal fetlock extension was less and delayed on DWS. ::: ::: ::: ::: Conclusions: This study confirms that trotting on deep sand overall reduces maximal GRF and induces a more progressive limb loading. However, it increases the propulsive effort and likely superficial digital flexor tendon tension at the end of stance, which should be taken into account in beach training.


title: Personalization features on business-to-consumer e-commerce: Review and future directions

abstract: Personalization in e-commerce has potentials to increase sales, customers' purchase intention and acquisition, as well as improvement of customer interaction. It is understood that personalization is a controllable variable for successful e-commerce. However, previous research on personalization proposed diverse concepts from numerous fields. As a result, it leads to bias construct of e-commerce personalization development and evaluation by academia and industry. To address this gap, a study was conducted to unravel personalization features from various perspectives. A Kitchenham's systematic literature review was used to discover personalization research from Q1/Q2 journals and top conference papers between 2012-2017. A theory-driven approach was administered to extract 21 selected papers. This process classifies personalization features into four dimensions based on three characters i.e objective, method, and user model. They include architectural, relational, instrumental and commercial dimensions. The results show that instrumental and commercial personalizations have been proved as the most popular dimension in the academic literature. However, relational personalization has been consistently rising as a new interesting topic to study since the massive growth of social media data.


CYANIDE REMEDIATION: CURRENT AND PAST TECHNOLOGIES


title: Pattern Recognition Systems under Attack: Design Issues and Research Challenges

abstract: We analyze the problem of designing pattern recognition systems in adversarial settings, under an engineering viewpoint, motivated by their increasing exploitation in security-sensitive applications like spam and malware detection, despite their vulnerability to potential attacks has not yet been deeply understood. We first review previous work and report examples of how a complex system may be evaded either by leveraging on trivial vulnerabilities of its untrained components, e.g. parsing errors in the pre-processing steps, or by exploiting more subtle vulnerabilities of learning algorithms. We then discuss the need of exploiting both reactive and proactive security paradigms complementarily to improve the security by design. Our ultimate goal is to provide some useful guidelines for improving the security of pattern recognition in adversarial settings, and to suggest related open issues to foster research in this area.


title: Modified annular penile skin flap for repair of pan-anterior urethral stricture

abstract: Objective ::: To review our experience with a zigzag shape annular penile fasciocutaneous flap (modified McAninch flap) for reconstruction of pan-anterior urethral strictures.


title: Analysis and Calculation of DC-Link Current and Voltage Ripples for Three-Phase Inverter With Unbalanced Load

abstract: In this paper, an analysis and calculation of the dc-link current and voltage ripples are presented for a three-phase inverter with unbalanced load. A comparison of the dc-link average and root-mean-square (rms) currents between considering and ignoring high frequency harmonics of the output current is drawn. It is shown that high frequency harmonic currents have little effect on the dc-link current, and therefore, they can be ignored. Based on the symmetrical components method, the dc-link average and harmonic rms currents are derived, and the dc-link voltage ripple is compared between the balanced and unbalanced loads. It can be found that the dc-link current and voltage ripples consist of not only high frequency harmonics but also the double fundamental frequency harmonic, and the voltage ripple is independent of the positive-sequence component and determined by the negative-sequence component, under the unbalanced load. Experimental results are shown to verify the accuracy of the theoretical analysis.


title: Normal neutrophil differentiation and secondary granule gene expression in the EML and MPRO cell lines.

abstract: Abstract The EML and MPRO cell lines express a dominant negative retinoic acid receptor alpha that causes a block at specific stages of myelopoiesis. The EML cell line is multipotent and gives rise to erythroid, lymphoid, and myeloid lineages depending on the presence of appropriate cytokines. The MPRO cell line is promyelocytic and undergoes neutrophilic differentiation when induced with all-trans retinoic acid in the presence of granulocyte/macrophage colony-stimulating factor. Previous studies have shown that both of these cell lines undergo morphological differentiation into neutrophils. In this study, we show that unlike other models of neutrophil differentiation such as NB4 and HL60, both EML and MPRO cell lines undergo complete, normal granulocytic differentiation programs. Similar to HL60, MPRO and EML induce expression of CD11b/CD18 and also exhibit downregulation of CD34 on differentiation. In contrast to HL60 and NB4, EML and MPRO cell lines coordinately upregulate secondary granule transcripts for lactoferrin and neutrophil gelatinase. Furthermore, we have confirmed previous observations that serum can induce a low level of differentiation in MPRO cells and that it is possible to grow these cells in serum-free medium, thereby eliminating this effect. Based on these studies, it appears that these lines can serve as a model for normal retinoic acid-induced neutrophil differentiation and provide insight into the role of the retinoic acid-responsive pathway in normal and leukemic myelopoiesis.


title: Digital Business Ecosystem Transformation -- Towards Cloud Integration

abstract: Digital business ecosystems are becoming an increasingly popular concept as an open environment for modeling and building interoperable system integration. Business organizations have realized the importance of using standards as a cost-effective method for accelerating business process integration. Small and medium size enterprise (SME) participation in global trade is increasing, however, digital transactions are still at a low level. Cloud integration is expected to offer a cost-effective business model to form an interoperable digital supply chain. By observing the integration models, we can identify the large potential of cloud services to accelerate integration. An industrial case study is conducted. This paper investigates and contributes new knowledge on a how top-down approach by using a digital business ecosystem framework enables business managers to define new user requirements and functionalities for system integration. Through analysis, we identify the current cap of integration design. Using the cloud clustering framework, we identify how the design affects cloud integration services.


Social Media Analytics for Healthcare


How Does He Saw Me ? A Recommendation Engine for Picking Heroes in Dota 2


title: Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving

abstract: We tested the applicability and signal quality of a 16 channel dry electroencephalography (EEG) system in a laboratory environment and in a car under controlled, realistic conditions. The aim of our investigation was an estimation how well a passive Brain-Computer Interface pBCI) can work in an autonomous driving scenario. The evaluation considered speed and accuracy of self-applicability by an untrained person, quality of recorded EEG data, shifts of electrode positions on the head after driving-related movements, usability and complexity of the system as such and wearing comfort over time. An experiment was conducted inside and outside of a stationary vehicle with running engine, air-conditioning and muted radio. Signal quality was sufficient for standard EEG analysis in the time and frequency domain as well as for the use in pBCIs. While the influence of vehicle-induced interferences to data quality was insignificant, driving-related movements led to strong shifts in electrode positions. In general, the EEG system used allowed for a fast self-applicability of cap and electrodes. The assessed usability of the system was still acceptable while the wearing comfort decreased strongly over time due to friction and pressure to the head. From these results we conclude that the evaluated system should provide the essential requirements for an application in an autonomous driving context. Nevertheless, further refinement is suggested to reduce shifts of the system due to body movements and increase the headset’s usability and wearing comfort.


title: Scam Detection in Twitter

abstract: Twitter is one among the fastest growing social networking services.This growth has led to an increase in Twitter scams (e.g., intentional deception). There is relatively little effort in identifying scams in Twitter. In this chapter, we propose a semi-supervised Twitter scam detector based on a small labeled data. The scam detector combines self-learning and clustering analysis. A suffix tree data structure is used. Model building based on Akaike and Bayes Information Criteria is investigated and combined with the classification step. Our experiments show that 87 % accuracy is achievable with only 9 labeled samples and 4000 unlabeled samples, among other results.


title: BetrFS: a right-optimized write-optimized file system

abstract: The Be-tree File System, or BetrFS, (pronounced "better eff ess") is the first in-kernel file system to use a write-optimized index. Write optimized indexes (WOIs) are promising building blocks for storage systems because of their potential to implement both microwrites and large scans efficiently. ::: ::: Previous work on WOI-based file systems has shown promise but has also been hampered by several open problems, which this paper addresses. For example, FUSE issues many queries into the file system, superimposing read-intensive workloads on top of write-intensive ones, thereby reducing the effectiveness of WOIs. Moving to an in-kernel implementation can address this problem by providing finer control of reads. This paper also contributes several implementation techniques to leverage kernel infrastructure without throttling write performance. ::: ::: Our results show that BetrFS provides good performance for both arbitrary microdata operations, which include creating small files, updating metadata, and small writes into large or small files, and for large sequential I/O. On one microdata benchmark, BetrFS provides more than 4× the performance of ext4 or XFS. BetrFS is an ongoing prototype effort, and requires additional data-structure tuning to match current general-purpose file systems on some operations such as deletes, directory renames, and large sequential writes. Nonetheless, many applications realize significant performance improvements. For instance, an in-place rsync of the Linux kernel source realizes roughly 1.6-22× speedup over other commodity file systems.


title: Big data stream computing in healthcare real-time analytics

abstract: The healthcare industry is changing at a dramatic rate. There are multiple processes going on within the health sector. These processes not only impact the care of individuals but also help medical practitioners and the delivery of care and services. The industry can take advantage of big data analytics to ensure that all the multiple processes within the industry are running smoothly. Big data analytics is not just an opportunity but a necessity. Recently, big data stream computing has been studied in order to improve the quality of healthcare services and reduce costs by capability support prediction, thus making decisions in real-time. This paper proposes a generic architecture for big data healthcare analytic by using open sources, including Hadoop, Apache Storm, Kafka and NoSQL Cassandra. The combination of high throughput publish-subscribe messaging for streams, distributed real-time computing, and distributed storage system can effectively analyze a huge amount of healthcare data coming with a rapid rate.


title: Ensemble Learning for Low-Level Hardware-Supported Malware Detection

abstract: Recent work demonstrated hardware-based online malware detection using only low-level features. This detector is envisioned as a first line of defense that prioritizes the application of more expensive and more accurate software detectors. Critical to such a framework is the detection performance of the hardware detector. In this paper, we explore the use of both specialized detectors and ensemble learning techniques to improve performance of the hardware detector. The proposed detectors reduce the false positive rate by more than half compared to a single detector, while increasing the detection rate. We also contribute approximate metrics to quantify the detection overhead, and show that the proposed detectors achieve more than 11x reduction in overhead compared to a software only detector 1.87x compared to prior work, while improving detection time. Finally, we characterize the hardware complexity by extending an open core and synthesizing it on an FPGA platform, showing that the overhead is minimal.


title: Pareto Efficiency in Robust Optimization

abstract: This paper formalizes and adapts the well-known concept of Pareto efficiency in the context of the popular robust optimization RO methodology for linear optimization problems. We argue that the classical RO paradigm need not produce solutions that possess the associated property of Pareto optimality, and we illustrate via examples how this could lead to inefficiencies and suboptimal performance in practice. We provide a basic theoretical characterization of Pareto robustly optimal PRO solutions and extend the RO framework by proposing practical methods that verify Pareto optimality and generate solutions that are PRO. Critically important, our methodology involves solving optimization problems that are of the same complexity as the underlying robust problems; hence, the potential improvements from our framework come at essentially limited extra computational cost. We perform numerical experiments drawn from three different application areas portfolio optimization, inventory management, and project management, which demonstrate that PRO solutions have a significant potential upside compared with solutions obtained using classical RO methods. ::: ::: This paper was accepted by Gerard P. Cachon, optimization.


title: The effects of incentives, deal proneness, satisfaction and tie strength on word‐of‐mouth behaviour

abstract: Although more than 30 years of research has established the power of word‐of‐mouth (WOM), little work has focused on how it could be managed more effectively. This study examines how incentives would work to actively encourage WOM, and how incentives would potentially interact with other variables that have been shown to drive WOM. In particular, a 3 × 3 × 2 experiment was conducted to examine the impact of incentives, tie‐strength, and satisfaction on WOM behavior. Consumer deal proneness was investigated using a quasi‐experimental design. Suggests from the findings that satisfaction does not necessarily increase the likelihood of WOM being generated. Shows incentives to be an effective catalyst to increase the likelihood of WOM being generated by satisfied consumers and tie strength to be an important variable in explaining WOM behavior. Suggests from the findings that deal prone consumers generate more WOM, independent of incentives. The findings have important implications, and suggest that satisfied customers are a necessary but not sufficient condition for getting positive WOM, and that incentives may be an effective way to get satisfied customers to recommend a firm. Furthermore, incentive programs targeted at strong ties are likely to be more effective than those targeted at weak ties, provided customers are genuinely happy with the service provided.


title: Image Matching by Normalized Cross-Correlation

abstract: Correlation is widely used as an effective similarity measure in matching tasks. However, traditional correlation based matching methods are limited to the short baseline case. In this paper we propose a new correlation based method for matching two images with large camera motion. Our method is based on the rotation and scale invariant normalized cross-correlation. Both the size and the orientation of the correlation windows are determined according to the characteristic scale and the dominant direction of the interest points. Experimental results on real images demonstrate that the new method is effective for matching image pairs with significant rotation and scale changes as well as other common imaging conditions.


title: Direct 3D pose estimation of a planar target

abstract: Estimating 3D pose of a known object from a given 2D image is an important problem with numerous studies for robotics and augmented reality applications. While the state-of-the-art Perspective-n-Point algorithms perform well in pose estimation, the success hinges on whether feature points can be extracted and matched correctly on targets with rich texture. In this work, we propose a robust direct method for 3D pose estimation with high accuracy that performs well on both textured and textureless planar targets. First, the pose of a planar target with respect to a calibrated camera is approximately estimated by posing it as a template matching problem. Next, the object pose is further refined and disambiguated with a gradient descent search scheme. Extensive experiments on both synthetic and real datasets demonstrate the proposed direct pose estimation algorithm performs favorably against state-of-the-art feature-based approaches in terms of robustness and accuracy under several varying conditions.


title: POSTER: OFX: Enabling OpenFlow Extensions for Switch-Level Security Applications

abstract: Network Security applications that run on Software Defined Networks (SDNs) often need to analyze and process traffic in advanced ways. Existing approaches to adding such functionality to SDNs suffer from either poor performance, or poor deployability. In this paper, we propose and benchmark OFX: an OpenFlow extension framework that provides a better tradeoff between performance and deployability for SDN security applications by allowing them to dynamically install software modules onto network switches.


title: A multi-task model for simultaneous face identification and facial expression recognition

abstract: Regarded as two independent tasks, both face identification and facial expression recognition perform poorly given small size training sets. To address this problem, we propose a multi-task facial inference model (MT-FIM) for simultaneous face identification and facial expression recognition. In particular, face identification and facial expression recognition are learnt simultaneously by extracting and utilizing appropriate shared information across them in the framework of multi-task learning, in which the shared information refers to the parameter controlling the sparsity. MT-FIM simultaneously minimizes the within-class scatter and maximizes the distance between different classes to enable the robust performance of each individual task. We conduct comprehensive experiments on three face image databases. The experimental results show that our algorithm outperforms the state-of-the-art algorithms.


title: Clustered Multi-Task Learning: A Convex Formulation

abstract: In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of information across tasks, each task may benefit from the others. In the context of learning linear functions for supervised classification or regression, this can be achieved by including a priori information about the weight vectors associated with the tasks, and how they are expected to be related to each other. In this paper, we assume that tasks are clustered into groups, which are unknown beforehand, and that tasks within a group have similar weight vectors. We design a new spectral norm that encodes this a priori assumption, without the prior knowledge of the partition of tasks into groups, resulting in a new convex optimization formulation for multi-task learning. We show in simulations on synthetic examples and on the IEDB MHC-I binding dataset, that our approach outperforms well-known convex methods for multi-task learning, as well as related non-convex methods dedicated to the same problem.


title: Outcomes of endoscopic pilonidal sinus treatment (EPSiT): a systematic review

abstract: Background ::: Pilonidal sinus is a common disease of the natal cleft, which can lead to complications including infection and abscess formation. Various operative management options are available, but the ideal technique is still debatable. More recently minimally invasive approaches have been described. Our aim was to review the current literature on endoscopic pilonidal sinus treatment (EPSiT) and its outcomes.


title: Android Security Permissions – Can We Trust Them?

abstract: The popularity of the Android System in combination with the lax market approval process may attract the injection of malicious applications (apps) into the market. Android features a permission system allowing a user to review the permissions an app requests and grant or deny access to resources prior to installation. This system conveys a level of trust due to the fact that an app only has access to resources granted by the stated permissions. Thereby, not only the meaning of single permissions, but especially their combination plays an important role for understanding the possible implications. In this paper we present a method that circumvents the permission system by spreading permissions over two or more apps that communicate with each other via arbitrary communication channels. We discuss relevant details of the Android system, describe the permission spreading process, possible implications and countermeasures. Furthermore, we present three apps that demonstrate the problem and a possible detection method.


title: Cooperative Coevolution of Control for a Real Multirobot System

abstract: The potential of cooperative coevolutionary algorithms (CCEAs) as a tool for evolving control for heterogeneous multirobot teams has been shown in several previous works. The vast majority of these works have, however, been confined to simulation-based experiments. In this paper, we present one of the first demonstrations of a real multirobot system, operating outside laboratory conditions, with controllers synthesised by CCEAs. We evolve control for an aquatic multirobot system that has to perform a cooperative predator-prey pursuit task. The evolved controllers are transferred to real hardware, and their performance is assessed in a non-controlled outdoor environment. Two approaches are used to evolve control: a standard fitness-driven CCEA, and novelty-driven coevolution. We find that both approaches are able to evolve teams that transfer successfully to the real robots. Novelty-driven coevolution is able to evolve a broad range of successful team behaviours, which we test on the real multirobot system.


Outlier Detection with Autoencoder Ensembles.


A Taxonomy of Obfuscating Transformations


title: Correlated network data publication via differential privacy

abstract: With the increasing prevalence of information networks, research on privacy-preserving network data publishing has received substantial attention recently. There are two streams of relevant research, targeting different privacy requirements. A large body of existing works focus on preventing node re-identification against adversaries with structural background knowledge, while some other studies aim to thwart edge disclosure. In general, the line of research on preventing edge disclosure is less fruitful, largely due to lack of a formal privacy model. The recent emergence of differential privacy has shown great promise for rigorous prevention of edge disclosure. Yet recent research indicates that differential privacy is vulnerable to data correlation, which hinders its application to network data that may be inherently correlated. In this paper, we show that differential privacy could be tuned to provide provable privacy guarantees even in the correlated setting by introducing an extra parameter, which measures the extent of correlation. We subsequently provide a holistic solution for non-interactive network data publication. First, we generate a private vertex labeling for a given network dataset to make the corresponding adjacency matrix form dense clusters. Next, we adaptively identify dense regions of the adjacency matrix by a data-dependent partitioning process. Finally, we reconstruct a noisy adjacency matrix by a novel use of the exponential mechanism. To our best knowledge, this is the first work providing a practical solution for publishing real-life network data via differential privacy. Extensive experiments demonstrate that our approach performs well on different types of real-life network datasets.


title: Distributed Video Coding

abstract: Distributed coding is a new paradigm for video compression, based on Slepian and Wolf's and Wyner and Ziv's information-theoretic results from the 1970s. This paper reviews the recent development of practical distributed video coding schemes. Wyner-Ziv coding, i.e., lossy compression with receiver side information, enables low-complexity video encoding where the bulk of the computation is shifted to the decoder. Since the interframe dependence of the video sequence is exploited only at the decoder, an intraframe encoder can be combined with an interframe decoder. The rate-distortion performance is superior to conventional intraframe coding, but there is still a gap relative to conventional motion-compensated interframe coding. Wyner-Ziv coding is naturally robust against transmission errors and can be used for joint source-channel coding. A Wyner-Ziv MPEG encoder that protects the video waveform rather than the compressed bit stream achieves graceful degradation under deteriorating channel conditions without a layered signal representation.


title: The Energy Intensity of the Internet: Home and Access Networks

abstract: Estimates of the energy intensity of the Internet diverge by several orders of magnitude. We present existing assessments and identify diverging definitions of the system boundary as the main reason for this large spread. The decision of whether or not to include end devices influences the result by 1–2 orders of magnitude. If end devices are excluded, customer premises equipment (CPE) and access networks have a dominant influence. Of less influence is the consideration of cooling equipment and other overhead, redundancy equipment, and the amplifiers in the optical fibers. We argue against the inclusion of end devices when assessing the energy intensity of the Internet, but in favor of including CPE, access networks, redundancy equipment, cooling and other overhead as well as optical fibers. We further show that the intensities of the metro and core network are best modeled as energy per data, while the intensity of CPE and access networks are best modeled as energy per time (i.e., power), making overall assessments challenging. The chapter concludes with a formula for the energy intensity of CPE and access networks. The formula is presented both in generic form as well as with concrete estimates of the average case to be used in quick assessments by practitioners. The next chapter develops a similar formula for the core and edge networks. Taken together, the two chapters provide an assessment method of the Internet’s energy intensity that takes into account different modeling paradigms for different parts of the network.


Honeypots Aiding Network Forensics: Challenges and Notions


A Personalized Re-ranking Technique for Academic Paper Searching Based on User Profiles


title: Environmentally-Friendly Hospital Management : Case Studies in 3 University Hospitals

abstract: The purpose of this study is to investigate the strategies and the experiences of 3 university hospitals relevant to environmentally-friendly strategy in their hospitals and to find out the implication for spreading the environmentally-friendly hospital management to other Korean hospitals. The study was progressed from October to November 2010 through the in depth interview with middle manager of the hospitals. Interview items about environmentally-friendly hospital management were selected from ISO 14001, Korean Green Management Certification Scheme, Green Guide Operation Version 2.2 of GGHC, Eco-ChecklistTM, Self-Assessment Tool of "Practice Greenhealth" and some accreditation credits of LEED, BREEAM, GreenStar. The case study result implies that the following strategies are so critical to settle environmentally-friendly hospitals in Korea: 1)understanding about fundamental concept of environmentally-friendly hospital, 2)organizing green teams, 3)purchasing environmentally-friendly goods, 4)benchmarking other successful hospitals, 5)adopting easy & reasonable policy first, 6)managing accumulated data & performance about energy efficiency, and 7)educating the employee about environmentally-friendly hospitals strategies. Hospitals have to keep in pace with the change in government policy and scheme like as GHG & Energy Target Management System, Korean Green Management Certification Scheme, ESCO, and so on for achieving the successful environmentally-friendly hospitals.


title: A new inlier identification scheme for robust estimation problems

abstract: Common goal of many computer vision and robotics algorithms is to extract geometric information from the sensory data. Due to noisy measurements and errors in matching or segmentation, the available data are often corrupted with outliers. In such instances robust estimation methods are employed for the problem of parametric model estimation. In the presence of a large fraction of outliers sampling based methods are often the preferred choice. Traditionally used RANSAC algorithm however requires a large number of samples, prior knowledge of the outlier ratio and an additional, difficult to obtain, inlier threshold for hypothesis evaluation. To tackle these problems we propose a novel efficient sampling based method for the robust estimation of model parameters. The method is based on the observation that for each data point, the properties of the residual distribution with respect to the generated hypotheses reveal whether the point is an outlier or an inlier. The problem of inlier/outlier identification can then be formulated as a classification problem. The proposed method is demonstrated on motion estimation problems from image correspondences with a large percentage of outliers (70%) on both synthetic and real data and estimation of planar models from range data. The method is shown to be of an order of magnitude more efficient than currently existing methods and does not require a prior knowledge of the outlier ratio and the inlier threshold.


title: RADIAL BASIS FUNCTION NETWORKS AS CHAOTIC GENERATORS FOR SECURE COMMUNICATION SYSTEMS

abstract: This paper improves upon a new class of discrete chaotic systems (i.e. chaotic maps) recently introduced for effective information encryption. The nonlinearity and adaptability of these systems are achieved by designing proper radial basis function networks. The potential for automatic synchronization, the lack of periodicity and the extremely large parameter spaces of these chaotic maps offer robust transmission security. The Radial Basis Function (RBF) networks offer a large number of parameters (i.e. the centers and spreads of the RBF kernels and the weights of the linear layer) while at the same time as universal approximators they have the flexibility to implement any function. The RBF networks can learn the dynamics of chaotic systems (maps or flows) and mimic them accurately by using many more parameters than the original dynamical recurrence. Since the parameter space size increases exponentially with respect to the number of parameters, the RBF based systems greatly outperform previous designs in...


title: Human Activity Recognition Based on Spatial Distribution of Gradients at Sublevels of Average Energy Silhouette Images

abstract: The aim of this paper is to present a unified framework for human action and activity recognition by analysing the effect of computation of spatial distribution of gradients (SDGs) on average energy silhouette images (AESIs). Based on the analysis of SDGs computation at various decomposition levels, an effective approach to compute the SDGs is developed. The AESI is constructed for the representation of the shape of action and activity and these are the reflection of 3-D pose into 2-D pose. To describe the AESIs, the SDGs at various sublevels and sum of the directional pixels (SDPs) variations is computed. The temporal content of the activity is computed through R-transform (RT). Finally, the shape computed through SDGs and SDPs, and temporal evidences through RT of the human body is fused together at the recognition stage, which results in a new powerful unified feature map model. The performance of the proposed framework is evaluated on three different publicly available datasets, i.e., Weizmann, KTH, and Ballet and the recognition accuracy is computed using hybrid classifier. The highest recognition accuracy achieved on these datasets is compared with the similar state-of-the-art techniques and demonstrate the superior performance.


title: A Dual-Source Approach for 3D Pose Estimation from a Single Image

abstract: In this work we address the challenging problem of 3D human pose estimation from single images. Recent approaches learn deep neural networks to regress 3D pose directly from images. One major challenge for such methods, however, is the collection of training data. Specifically, collecting large amounts of training data containing unconstrained images annotated with accurate 3D poses is infeasible. We therefore propose to use two independent training sources. The first source consists of accurate 3D motion capture data, and the second source consists of unconstrained images with annotated 2D poses. To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient 3D pose retrieval. To this end, we first convert the motion capture data into a normalized 2D pose space, and separately learn a 2D pose estimation model from the image data. During inference, we estimate the 2D pose and efficiently retrieve the nearest 3D poses. We then jointly estimate a mapping from the 3D pose space to the image and reconstruct the 3D pose. We provide a comprehensive evaluation of the proposed method and experimentally demonstrate the effectiveness of our approach, even when the skeleton structures of the two sources differ substantially.


title: Automatic Acquisition of Taxonomies from Text: FCA meets NLP

abstract: We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from domain-specific texts based on Formal Concept Analysis (FCA). Our approach is based on the assumption that verbs pose more or less strong selectional restrictions on their arguments. The conceptual hierarchy is then built on the basis of the inclusion relations between the extensions of the selectional restrictions of all the verbs, while the verbs themselves provide intensional descriptions for each concept. We formalize this idea in terms of FCA and show how our approach can be used to acquire a concept hierarchy for the tourism domain out of texts. We then evaluate our method by considering an already existing ontology for this domain.


title: Evidence-based software engineering

abstract: Objective: Our objective is to describe how softwareengineering might benefit from an evidence-basedapproach and to identify the potential difficultiesassociated with the approach.Method: We compared the organisation and technicalinfrastructure supporting evidence-based medicine (EBM)with the situation in software engineering. We consideredthe impact that factors peculiar to software engineering(i.e. the skill factor and the lifecycle factor) would haveon our ability to practice evidence-based softwareengineering (EBSE).Results: EBSE promises a number of benefits byencouraging integration of research results with a view tosupporting the needs of many different stakeholdergroups. However, we do not currently have theinfrastructure needed for widespread adoption of EBSE.The skill factor means software engineering experimentsare vulnerable to subject and experimenter bias. Thelifecycle factor means it is difficult to determine howtechnologies will behave once deployed.Conclusions: Software engineering would benefit fromadopting what it can of the evidence approach providedthat it deals with the specific problems that arise from thenature of software engineering.


title: Automated Patent Categorization and Guided Patent Search using IPC as Inspired by MeSH and PubMed

abstract: Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Patent documents are another important information source, though they are considerably less accessible. One option to expand patent search beyond pure keywords is the inclusion of classification information: Since every patent is assigned at least one class code, it should be possible for these assignments to be automatically used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. This report describes our comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms. Our analysis shows a strong structural similarity of the hierarchies, but significant differences of terms and annotations. The low number of IPC class assignments and the lack of occurrences of class labels in patent texts imply that current patent search is severely limited. To overcome these limits, we evaluate a method for the automated assignment of additional classes to patent documents, and we propose a system for guided patent search based on the use of class co-occurrence information and external resources.


title: How to Know What Students Know.

abstract: Think about the traditional question-and-answer session that happens daily in many classrooms. We ask a question directed at the entire class, wait for hands to go up, and then select one of the few hands raised. It provides us with a great assessment of the students on whom we've called. But what about the other students? Are they with us? And how do we know? What evidence do we have that they're actively processing the content using higher-order thinking? Despite what we know about the importance of having students actively process concepts, we still get caught in the rut of the traditional Q & A. At times, it has its place in the classroom. But we prefer to think of it as the culmination of well-structured opportunities to process concepts—rather than the jumping-off point. In other words, if we start with the traditional question-and-answer session, we're doing things backward. One solution to this problem is the use of total participation techniques, which provide teachers with evidence of active participation and cognitive engagement from all students at the same time. These techniques function as formative assessments that help teachers accurately monitor progress, provide feedback, and modify instruction. Compared with the traditional question-and-answer scenario, they give teachers a lot more bang for their buck. The Ripple Effect


title: First-Person Activity Recognition: What Are They Doing to Me?

abstract: This paper discusses the problem of recognizing interaction-level human activities from a first-person viewpoint. The goal is to enable an observer (e.g., a robot or a wearable camera) to understand 'what activity others are performing to it' from continuous video inputs. These include friendly interactions such as 'a person hugging the observer' as well as hostile interactions like 'punching the observer' or 'throwing objects to the observer', whose videos involve a large amount of camera ego-motion caused by physical interactions. The paper investigates multi-channel kernels to integrate global and local motion information, and presents a new activity learning/recognition methodology that explicitly considers temporal structures displayed in first-person activity videos. In our experiments, we not only show classification results with segmented videos, but also confirm that our new approach is able to detect activities from continuous videos reliably.


title: Collaborative privacy management: mobile privacy beyond your own devices

abstract: As the development of mobile devices and applications, mobile privacy has become a very important issue. Current researches on mobile privacy mainly focus on potential leakages on a particular device. However, leakage of sensitive data on a mobile device not only violates the privacy of the phone (or data) owner, but also violates the privacy of many other people whose information are contained in the data directly or indirectly (they are called data involvers). To address such problems, we introduce a collaborative privacy management framework, which aims to provide fine-grained data privacy protection for both data owners and data involvers in a distributed manner. Based on individual privacy policies specified by each user, a collaborative privacy policy is generated and enforced on different devices automatically. As a proof-of-concept prototype, we implement the proposed framework on Android and demonstrate its applicability with two case studies.


title: Discoid lupus erythematosus developing in areas where fragments of windshield glass had become embedded in the skin.

abstract: We observed a 26-year-old female patient with discoid lupus erythematosus on her left cheek skin, where fine fragments of windshield glass had been embedded in an automobile accident 8 years previously. She gradually developed general fatigue, morning stiffness of fingers and anti-nuclear antibody. As an etio-pathogenesis of this patient, we speculate that a long exposure to quartz (silica) could give rise to discoid lupus erythematosus in only local damaged areas and at the same time induce systemic immunological changes in some genetically restricted persons; such as production of anti-nuclear antibody. Silica has multi-potential biological effects, especially on immunological functions.


title: Deep Dual Pyramid Network for Barcode Segmentation using Barcode-30k Database

abstract: Digital signs(such as barcode or QR code) are widely used in our daily life, and for many applications, we need to localize them on images. However, difficult cases such as targets with small scales, half-occlusion, shape deformation and large illumination changes cause challenges for conventional methods. In this paper, we address this problem by producing a large-scale dataset and adopting a deep learning based semantic segmentation approach. Specifically, a synthesizing method was proposed to generate well-annotated images containing barcode and QR code labels, which contributes to largely decrease the annotation time. Through the synthesis strategy, we introduce a dataset that contains 30000 images with Barcode and QR code - Barcode-30k. Moreover, we further propose a dual pyramid structure based segmentation network - BarcodeNet, which is mainly formed with two novel modules, Prior Pyramid Pooling Module(P3M) and Pyramid Refine Module(PRM). We validate the effectiveness of BarcodeNet on the proposed synthetic dataset, and it yields the result of mIoU accuracy 95.36\% on validation set. Additional segmentation results of real images have shown that accurate segmentation performance is achieved.


title: Progress&Compress: A scalable framework for continual learning

abstract: We introduce a conceptually simple and scalable framework for continual learning domains where tasks are learned sequentially. Our method is constant in the number of parameters and is designed to preserve performance on previously encountered tasks while accelerating learning progress on subsequent problems. This is achieved by training a network with two components: A knowledge base, capable of solving previously encountered problems, which is connected to an active column that is employed to efficiently learn the current task. After learning a new task, the active column is distilled into the knowledge base, taking care to protect any previously acquired skills. This cycle of active learning (progression) followed by consolidation (compression) requires no architecture growth, no access to or storing of previous data or tasks, and no task-specific parameters. We demonstrate the progress&compress approach on sequential classification of handwritten alphabets as well as two reinforcement learning domains: Atari games and 3D maze navigation.