title: Local Image Registration by Adaptive Filtering

abstract: We propose a new adaptive filtering framework for local image registration, which compensates for the effect of local distortions/displacements without explicitly estimating a distortion/displacement field. To this effect, we formulate local image registration as a two-dimensional (2-D) system identification problem with spatially varying system parameters. We utilize a 2-D adaptive filtering framework to identify the locally varying system parameters, where a new block adaptive filtering scheme is introduced. We discuss the conditions under which the adaptive filter coefficients conform to a local displacement vector at each pixel. Experimental results demonstrate that the proposed 2-D adaptive filtering framework is very successful in modeling and compensation of both local distortions, such as Stirmark attacks, and local motion, such as in the presence of a parallax field. In particular, we show that the proposed method can provide image registration to: a) enable reliable detection of watermarks following a Stirmark attack in nonblind detection scenarios, b) compensate for lens distortions, and c) align multiview images with nonparametric local motion


title: A Three-Layer Privacy Preserving Cloud Storage Scheme Based on Computational Intelligence in Fog Computing

abstract: Recent years witness the development of cloud computing technology. With the explosive growth of unstructured data, cloud storage technology gets more attention and better development. However, in current storage schema, user's data is totally stored in cloud servers. In other words, users lose their right of control on data and face privacy leakage risk. Traditional privacy protection schemes are usually based on encryption technology, but these kinds of methods cannot effectively resist attack from the inside of cloud server. In order to solve this problem, we propose a three-layer storage framework based on fog computing. The proposed framework can both take full advantage of cloud storage and protect the privacy of data. Besides, Hash-Solomon code algorithm is designed to divide data into different parts. Then, we can put a small part of data in local machine and fog server in order to protect the privacy. Moreover, based on computational intelligence, this algorithm can compute the distribution proportion stored in cloud, fog, and local machine, respectively. Through the theoretical safety analysis and experimental evaluation, the feasibility of our scheme has been validated, which is really a powerful supplement to existing cloud storage scheme.


title: Data Management for Internet of Things: Challenges, Approaches and Opportunities

abstract: Internet of Things (IoT) is an important part of the new generation information technology. Data management for IoT plays a crucial role in its effective operations and has become a key research topic of IoT. Much work has been done to enable effective and intelligent data processing and analysis when IoT is evolving from Radio Frequency Identification (RFID), Wireless Sensor Network (WSN) and other related technologies. In this paper, we start from the core definition and architecture of IoT, aiming at examining current research effort to derive a holistic view of existing literatures. We present a layered reference model for IoT data management and elaborate the related research topics and solutions in each layer. Based on our analysis, we identify research challenges and opportunities for future work.


title: Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt

abstract: Abstract In this study, maximum likelihood supervised classification and post-classification change detection techniques were applied to Landsat images acquired in 1987 and 2001, respectively, to map land cover changes in the Northwestern coast of Egypt. A supervised classification was carried out on the six reflective bands for the two images individually with the aid of ground truth data. Ground truth information collected during six field trips conducted between 1998 and 2002 and land cover map of 1987 were used to assess the accuracy of the classification results. Using ancillary data, visual interpretation and expert knowledge of the area through GIS further refined the classification results. Post-classification change detection technique was used to produce change image through cross-tabulation. Changes among different land cover classes were assessed. During the study period, a very severe land cover change has taken place as a result of agricultural and tourist development projects. These changes in land cover led to vegetation degradation and water logging in part of the study area.


title: A Graph Distance Metric Based on the Maximal Common Subgraph

abstract: Abstract Error-tolerant graph matching is a powerful concept that has various applications in pattern recognition and machine vision. In the present paper, a new distance measure on graphs is proposed. It is based on the maximal common subgraph of two graphs. The new measure is superior to edit distance based measures in that no particular edit operations together with their costs need to be defined. It is formally shown that the new distance measure is a metric. Potential algorithms for the efficient computation of the new measure are discussed.


title: Ensemble application of convolutional and recurrent neural networks for multi-label text categorization

abstract: Text categorization, or text classification, is one of key tasks for representing the semantic information of documents. Multi-label text categorization is finer-grained approach to text categorization which consists of assigning multiple target labels to documents. It is more challenging compared to the task of multi-class text categorization due to the exponential growth of label combinations. Existing approaches to multi-label text categorization fall short to extract local semantic information and to model label correlations. In this paper, we propose an ensemble application of convolutional and recurrent neural networks to capture both the global and the local textual semantics and to model high-order label correlations while having a tractable computational complexity. Extensive experiments show that our approach achieves the state-of-the-art performance when the CNN-RNN model is trained using a large-sized dataset.


title: Ising formulations of many NP problems

abstract: We provide Ising formulations for many NP-complete and NP-hard problems, including all of Karp's 21 NP-complete problems. This collects and extends mappings to the Ising model from partitioning, covering and satisfiability. In each case, the required number of spins is at most cubic in the size of the problem. This work may be useful in designing adiabatic quantum optimization algorithms.


title: Formal Specification for Online Food Ordering System Using Z Language

abstract: Natural Language Processing (NLP) directed to ambiguous representation for software requirements. Ambiguity at different levels creates different representation and meaning. This paper reduces the issues of ambiguity levels for the Software Requirements Specification (SRS) using formal methods. The end result shows the effectiveness in specifications through Z language. The Z specification is created for the commercial application of online food ordering system to improve the order details accuracy and efficiency. The stakeholder needs for food ordering system are gathered from the project goal. The system is designed using Unified Modeling Language (UML) illustration of use case diagram. The specification is created for the system behavior to remove the ambiguity. Along with this, Z/EVES tool is used for the evaluation of Z specifications for the demonstration.


title: Design and Analysis of Compact 108 Element Multimode Antenna Array for Massive MIMO Base Station

abstract: In this paper, we report the design and evaluation of a compact 108-element base station antenna array for massive multi-input multi-output (MIMO) system using a 3-port multimode antenna as a unit element. Of these three ports, port 1 and port 2 have orthogonally polarized broadside radiation pattern with measured peak gain of 6.5 dBi and impedance bandwidth of 254 MHz (2.268 GHz- 2.522 GHz) and 238 MHz (2.248 GHz-2.486 GHz), respectively, while port 3 has monopole-like radiation pattern having measured peak gain of 1.21 dBi and impedance bandwidth of 102 MHz (2.376 GHz- 2.478 GHz). Mutual coupling among all the ports is kept as less than �14 dB. Design of 108-element massive MIMO antenna array is evaluated as a base station antenna for multiuser urban street grid scenario in MIMO-OFDM downlink system, which is further modeled using Wireless World Initiative New Radio II (WINNER II) Channel models in MATLAB. Parameters like Singular value spread and Dirty Paper Coding (DPC) sum capacity was calculated and compared with i.i.d channel model. For 4 users case using same frequency and time resource, singular value spread and DPC sum capacity for presented antenna array converges to 7 dB and 11.6 bps/Hz at 10 dB SNR respectively.


title: Determination of differential leakage factors in electrical machines with non-symmetrical full and dead-coil windings

abstract: In this paper Gorges polygons are used in conjunction with masses geometry to find an easy and affordable way to compute the differential leakage factor of non symmetrical full and dead coil winding. By following the traditional way, the use of the Ossanna's infinite series which has to be obviously truncated under the bound of a predetermined accuracy is mandatory. In the presented method no infinite series is instead required. An example is then shown and discussed to demonstrate practically the effectiveness of the proposed method.


title: A THEORY OF INSTINCTIVE INFORMATION SHARING

abstract: Recent psychology research suggests that people have an innate tendency to share information. This brings a new perspective to existing information sharing research. We elaborated a new theory with the central construct of need for information sharing. This need for information sharing is a measure of an individual’s innate need to share information with others. We also developed measurement items for this construct and empirically tested it in a survey on sharing of information in forums. The measurement items show good reliability and validity. The new construct can help to explain people’s intention to share information. The results support our new theory for information sharing behaviours.


title: Public-Key Cryptosystems Based on Composite Degree Residuosity Classes

abstract: This paper investigates a novel computational problem, namely the Composite Residuosity Class Problem, and its applications to public-key cryptography. We propose a new trapdoor mechanism and derive from this technique three encryption schemes : a trapdoor permutation and two homomorphic probabilistic encryption schemes computationally comparable to RSA. Our cryptosystems, based on usual modular arithmetics, are provably secure under appropriate assumptions in the standard model.


title: Differential Microstrip Antenna for RF Energy Harvesting

abstract: A differential microstrip antenna with improved gain for RF energy harvesting is presented in this paper. The developed antenna can be used in either center grounded or differential configuration. The antenna is designed and fabricated for GSM900 band (890–960 MHz). The antenna has a gain of 8.5 dBi at the center frequency and exhibits VSWR $\boldsymbol{\leq} 2$ for frequencies between 870 MHz to 1.05 GHz. The efficiency of the antenna is 80%. The developed antenna finds its application in energy harvesting, RFID tags and in wireless communication circuits, where differential inputs/outputs are needed. A complete differential RF energy harvesting system with a peak efficiency of 65.3% for a load of $3 {\mathbf{k}}\boldsymbol{\Omega}$ is also developed.


title: A Delay-Locked Loop Synchronization Scheme for High-Frequency Multiphase Hysteretic DC-DC Converters

abstract: This paper reports a delay locked loop (DLL) based hysteretic controller for high-frequency multiphase buck DC-DC converters. The DLL control loop employs the switching frequency from a hysteretic comparator to automatically synchronize the remaining phases. A dedicated duty cycle control loop is used to enable current sharing and ripple cancellation. We demonstrate a 25-70 MHz 4-phase converter with fast hysteretic control and output conversion range of 17%-80% while achieving a peak efficiency of 83% and peak-to-peak ripple within 10% in standard 0.6 mum 5 V CMOS process.


title: MicroRNA-29a Counteracts Synovitis in Knee Osteoarthritis Pathogenesis by Targeting VEGF

abstract: Synovitis contributes to the development of osteoarthritis (OA) of the knee. MicroRNAs regulate joint microenvironment homeostasis and deterioration. This study was undertaken to characterize the actions of microRNA-29a (miR-29a) to synovial remodeling in OA joints. Synovial specimens isolated from patients with end-stage OA knees showed abundant fibrotic matrix and vessel histopathology concomitant with weak miR-29a expression. In vitro, miR-29a knockdown caused synovial fibroblasts to exhibit high expressions of collagen III, TGF-β1, MMP9, MMP13, and ADAMTS5, whereas miR-29a overexpression diminished these joint-deleterious factors. In collagenase-mediated OA pathogenesis, miR-29a-overexpressing transgenic mice showed minor responses to hyperplasia, macrophage infiltration, fibrosis, hyperangiogenesis, and VEGF expression in synovial lesions. These effects mitigated articular cartilage loss and gait aberrance of injured joints. Intra-articular administration of miR-29a precursor lessened the collagenase aggravation of excessive synovial remodeling reactions and thereby sustained joint tissue integrity. miR-29a lowered VEGF production and angiogenic activities in synovial fibroblasts through targeting the 3′-UTR of VEGF. Taken together, miR-29a deficiency exacerbated synovitis pathogenesis in the end-stage OA knees. miR-29a signaling fends off excessive synovial angiogenesis and fibrosis, which delays joint destruction. This study sheds new light on the protective effects against synovial deterioration and the therapeutic advantage of miR-29a in OA knees.


Moo : A Batteryless Computational RFID and Sensing Platform


title: Energy- and Cost-Efficiency Analysis of ARM-Based Clusters

abstract: General-purpose computing domain has experienced strategy transfer from scale-up to scale-out in the past decade. In this paper, we take a step further to analyze ARM-processor based cluster against Intel X86 workstation, from both energy-efficiency and cost-efficiency perspectives. Three applications are selected and evaluated to represent diversified applications, including Web server throughput, in-memory database, and video transcoding. Through detailed measurements, we make the observations that the energy-efficiency ratio of the ARM cluster against the Intel workstation varies from 2.6-9.5 in in-memory database, to approximately 1.3 in Web server application, and 1.21 in video transcoding. We also find out that for the Intel processor that adopts dynamic voltage and frequency scaling (DVFS) techniques, the power consumption is not linear with the CPU utilization level. The maximum energy saving achievable from DVFS is 20%. Finally, by utilizing a monthly cost model of data centers, we conclude that ARM cluster based data centers are feasible, and are advantageous in computationally lightweight applications, e.g. in-memory database and network-bounded Web applications. The cost advantage of ARM cluster diminishes progressively for computation-intensive applications, i.e. dynamic Web server application and video transcoding, because the number of ARM processors needed to provide comparable performance increases.


title: Subdivisions of auditory cortex and processing streams in primates.

abstract: The auditory system of monkeys includes a large number of interconnected subcortical nuclei and cortical areas. At subcortical levels, the structural components of the auditory system of monkeys resemble those of nonprimates, but the organization at cortical levels is different. In monkeys, the ventral nucleus of the medial geniculate complex projects in parallel to a core of three primary-like auditory areas, AI, R, and RT, constituting the first stage of cortical processing. These areas interconnect and project to the homotopic and other locations in the opposite cerebral hemisphere and to a surrounding array of eight proposed belt areas as a second stage of cortical processing. The belt areas in turn project in overlapping patterns to a lateral parabelt region with at least rostral and caudal subdivisions as a third stage of cortical processing. The divisions of the parabelt distribute to adjoining auditory and multimodal regions of the temporal lobe and to four functionally distinct regions of the frontal lobe. Histochemically, chimpanzees and humans have an auditory core that closely resembles that of monkeys. The challenge for future researchers is to understand how this complex system in monkeys analyzes and utilizes auditory information.


title: Evaluation of VANET-based advanced intelligent transportation systems

abstract: This paper aims at enabling accurate and efficient evaluation of emerging vehicular network applications such as Intelligent Transportation Systems (ITS). A distributed simulation platform that integrates transportation simulation and wireless network simulation is proposed and implemented, providing a user level simulation environment to evaluate the feasibility and performance limitations of VANETs in supporting ITS. The proposed simulation platform facilitates the dynamic interaction between the two simulation domains, allowing runtime control of vehicles' behavior in the transportation simulation as they react in real time to information exchange in the simulated communication network. Case studies are conducted within the proposed simulation platform to evaluate the performance of Dynamic Route Planning when deployed in VANETs, using metrics collected at the transportation system level such as travel and delay time. The effectiveness of three representative VANET dynamic adaptation protocols in enhancing the application performance in scenarios with high vehicle density are compared in the case studies. The experiment results show that Dynamic Route Planning can be effectively supported by a VANET system with up to 118% increase on the number of vehicles reaching the destination, 36.2% reduction on travel time and 56.1% reduction on delay time.


title: GARNET: A detailed on-chip network model inside a full-system simulator

abstract: Until very recently, microprocessor designs were computation-centric. On-chip communication was frequently ignored. This was because of fast, single-cycle on-chip communication. The interconnect power was also insignificant compared to the transistor power. With uniprocessor designs providing diminishing returns and the advent of chip multiprocessors (CMPs) in mainstream systems, the on-chip network that connects different processing cores has become a critical part of the design. Transistor miniaturization has led to high global wire delay, and interconnect power comparable to transistor power. CMP design proposals can no longer ignore the interaction between the memory hierarchy and the interconnection network that connects various elements. This necessitates a detailed and accurate interconnection network model within a full-system evaluation framework. Ignoring the interconnect details might lead to inaccurate results when simulating a CMP architecture. It also becomes important to analyze the impact of interconnection network optimization techniques on full system behavior. In this light, we developed a detailed cycle-accurate interconnection network model (GARNET), inside the GEMS full-system simulation framework. GARNET models a classic five-stage pipelined router with virtual channel (VC) flow control. Microarchitectural details, such as flit-level input buffers, routing logic, allocators and the crossbar switch, are modeled. GARNET, along with GEMS, provides a detailed and accurate memory system timing model. To demonstrate the importance and potential impact of GARNET, we evaluate a shared and private L2 CMP with a realistic state-of-the-art interconnection network against the original GEMS simple network. The objective of the evaluation was to figure out which configuration is better for a particular workload. We show that not modeling the interconnect in detail might lead to an incorrect outcome. We also evaluate Express Virtual Channels (EVCs), an on-chip network flow control proposal, in a full-system fashion. We show that in improving on-chip network latency-throughput, EVCs do lead to better overall system runtime, however, the impact varies widely across applications.


title: A Topic Modeling Approach for Traditional Chinese Medicine Prescriptions

abstract: In traditional Chinese medicine (TCM), prescriptions are the daughters of doctors’ clinical experiences, which have been the main way to cure diseases in China for several thousand years. In the long Chinese history, a large number of prescriptions have been invented based on TCM theories. Regularities in the prescriptions are important for both clinical practice and novel prescription development. Previous works used many methods to discover regularities in prescriptions, but rarely described how a prescription is generated using TCM theories. In this work, we propose a topic model which characterizes the generative process of prescriptions in TCM theories and further incorporate domain knowledge into the topic model. Using 33,765 prescriptions in TCM prescription books, the model can reflect the prescribing patterns in TCM. Our method can outperform several previous topic models and group recommendation methods on generalization performance, herbs recommendation, symptoms suggestion, and prescribing patterns discovery.


title: Aligning plot synopses to videos for story-based retrieval

abstract: We propose a method to facilitate search through the storyline of TV series episodes. To this end, we use human written, crowdsourced descriptions—plot synopses—of the story conveyed in the video. We obtain such synopses from websites such as Wikipedia and propose various methods to align each sentence of the plot to shots in the video. Thus, the semantic story-based video retrieval problem is transformed into a much simpler text-based search. Finally, we return the set of shots aligned to the sentences as the video snippet corresponding to the query. The alignment is performed by first computing a similarity score between every shot and sentence through cues such as character identities and keyword matches between plot synopses and subtitles. We then formulate the alignment as an optimization problem and solve it efficiently using dynamic programming. We evaluate our methods on the fifth season of a TV series Buffy the Vampire Slayer and show encouraging results for both the alignment and the retrieval of story events.


title: Advanced 300mm 0.13μm BCD technology from 5V to 80V with highly reliable embedded Flash

abstract: This paper demonstrates the advanced 300mm 0.13μm BCD platform with high flexibility. This platform brings about the various combinations from ten kinds of device options and three kinds of wiring options. Especially, for DMOS which plays an important role on the BCD platform, the best in class low Rdson is realized on Si-bulk. Furthermore, the highly reliable Flash memory cell is embedded on the 0.13μm BCD platform. This cell shows the excellent retention reliability of more than 20 years under 150°C after 100K erase/write cycles can be estimated.


title: Stakeholder participation for sustainable waste management

abstract: Inadequate environmental sanitation in many cities is a major cause of diseases and is a drain on the economy by way of lost workdays, cost of treatment and cleanup activities. Municipal authorities and policymakers need to act fast to address this issue. Sustainable waste management provides a comprehensive inter-disciplinary framework for addressing the problems of managing urban solid waste, in the resource constrained developing countries where quality of such services are poor and costs are high often with no effective means of recovering them. Upgrading the coverage of waste management and services and increasing their efficiency is a precondition for improving the environmental quality of cities. This paper highlights the fact that the involvement and participation of all the stakeholders such as the waste generators, waste processors, formal and informal agencies, non-governmental organisations and financing institutions is a key factor for the sustainable waste management.


title: Epidermoid cyst of the uvula in a child

abstract: Objective: Epidermoid cysts are rarely located in the uvula. To date, epidermoid cyst of the uvula has not been reported in a child at preschool age. We present clinical and histopathological characteristics of an epidermoid cyst in a child with uvula mass. Methods: Retrospective chart review. Results: A 5-year-old boy was seen in the pediatric otolaryngology clinic for assessment of a uvula mass. The mass was detected during a tonsillectomy and adenoidectomy performed for sleep-related breathing disorder. The mass was completely removed and the final diagnosis was epidermoid cyst. Conclusion: Pediatricians, otolaryngologists, and pathologists should be cognizant of the occurrence of uvular epidermoid cyst in preschool children.


title: Strategic Management Accounting: Lots in a Name?

abstract: Despite its thirty year history, strategic management accounting has yet to establish itself as a core element of managerial accounting. That it has the capacity to do so is immediately apparent from the term itself, which in turn explains its continuing appeal for those attracted to developing and promoting it. The purpose of this paper is to suggest that by reconceptualising strategic management accounting as 'accounting for strategic managementâ€™ it may be possible to better realise its potential. The paper is in very large part a literature review, in the course of which the accounting for strategic management perspective is explicated. A number of precursors in the extant strategic management accounting literature are also revisited. Central to this reformulation of strategic management accounting is the understanding that the accounting aspects of accounting for strategic management will differ radically from those normally associated with that designation.


title: Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control

abstract: Trial-and-error based reinforcement learning (RL) has seen rapid advancements in recent times, especially with the advent of deep neural networks. However, the majority of autonomous RL algorithms either rely on engineered features or a large number of interactions with the environment. Such a large number of interactions may be impractical in many real-world applications. For example, robots are subject to wear and tear and, hence, millions of interactions may change or damage the system. Moreover, practical systems have limitations in the form of the maximum torque that can be safely applied. To reduce the number of system interactions while naturally handling constraints, we propose a model-based RL framework based on Model Predictive Control (MPC). In particular, we propose to learn a probabilistic transition model using Gaussian Processes (GPs) to incorporate model uncertainties into long-term predictions, thereby, reducing the impact of model errors. We then use MPC to find a control sequence that minimises the expected long-term cost. We provide theoretical guarantees for the first-order optimality in the GP-based transition models with deterministic approximate inference for long-term planning. The proposed framework demonstrates superior data efficiency and learning rates compared to the current state of the art.


title: A User's Guide to Support Vector Machines

abstract: The Support Vector Machine (SVM) is a widely used classifier in bioinformatics. Obtaining the best results with SVMs requires an understanding of their workings and the various ways a user can influence their accuracy. We provide the user with a basic understanding of the theory behind SVMs and focus on their use in practice. We describe the effect of the SVM parameters on the resulting classifier, how to select good values for those parameters, data normalization, factors that affect training time, and software for training SVMs.


title: Content-adaptive low rank regularization for image denoising

abstract: Prior knowledge plays an important role in image denoising tasks. This paper utilizes the data of the input image to adaptively model the prior distribution. The proposed scheme is based on the observation that, for a natural image, a matrix consisted of its vectorized non-local similar patches is of low rank. We use a non-convex smooth surrogate for the low-rank regularization, and view the optimization problem from the empirical Bayesian perspective. In such framework, a parameter-free distribution prior is derived from the grouped non-local similar image contents. Experimental results show that the proposed approach is highly competitive with several state-of-art denoising methods in PSNR and visual quality.


title: Cybersecurity Games and Investments: A Decision Support Approach

abstract: In this paper we investigate how to optimally invest in cybersecurity controls. We are particularly interested in examining cases where the organization suffers from an underinvestment problem or inefficient spending on cybersecurity. To this end, we first model the cybersecurity environment of an organization. We then model non-cooperative cybersecurity control-games between the defender which abstracts all defense mechanisms of the organization and the attacker which can exploit different vulnerabilities at different network locations. To implement our methodology we use the SANS Top 20 Critical Security Controls and the 2011 CWE/SANS top 25 most dangerous software errors. Based on the profile of an organization, which forms its preferences in terms of indirect costs, its concerns about different kinds of threats and the importance of the assets given their associated risks we derive the Nash Equilibria of a series of control-games. These game solutions are then handled by optimization techniques, in particular multi-objective, multiple choice Knapsack to determine the optimal cybersecurity investment. Our methodology provides security effective and cost efficient solutions especially against commodity attacks. We believe our work can be used to advise security managers on how they should spend an available cybersecurity budget given their organization profile.


title: FlexSC: Flexible System Call Scheduling with Exception-Less System Calls

abstract: For the past 30+ years, system calls have been the de facto interface used by applications to request services from the operating system kernel. System calls have almost universally been implemented as a synchronous mechanism, where a special processor instruction is used to yield userspace execution to the kernel. In the first part of this paper, we evaluate the performance impact of traditional synchronous system calls on system intensive workloads. We show that synchronous system calls negatively affect performance in a significant way, primarily because of pipeline flushing and pollution of key processor structures (e.g., TLB, data and instruction caches, etc.). ::: ::: We propose a new mechanism for applications to request services from the operating system kernel: exception-less system calls. They improve processor efficiency by enabling flexibility in the scheduling of operating system work, which in turn can lead to significantly increased temporal and spacial locality of execution in both user and kernel space, thus reducing pollution effects on processor structures. Exception-less system calls are particularly effective on multicore processors. They primarily target highly threaded server applications, such as Web servers and database servers. ::: ::: We present FlexSC, an implementation of exceptionless system calls in the Linux kernel, and an accompanying user-mode thread package (FlexSC-Threads), binary compatible with POSIX threads, that translates legacy synchronous system calls into exception-less ones transparently to applications. We show how FlexSC improves performance of Apache by up to 116%, MySQL by up to 40%, and BIND by up to 105% while requiring no modifications to the applications.


title: Innateness, AlphaZero, and Artificial Intelligence

abstract: The concept of innateness is rarely discussed in the context of artificial intelligence. When it is discussed, or hinted at, it is often the context of trying to reduce the amount of innate machinery in a given system. In this paper, I consider as a test case a recent series of papers by Silver et al (Silver et al., 2017a) on AlphaGo and its successors that have been presented as an argument that a "even in the most challenging of domains: it is possible to train to superhuman level, without human examples or guidance", "starting tabula rasa." ::: I argue that these claims are overstated, for multiple reasons. I close by arguing that artificial intelligence needs greater attention to innateness, and I point to some proposals about what that innateness might look like.


A Parallel Hierarchical Attention Network for Style Change Detection: Notebook for PAN at CLEF 2018


title: An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints

abstract: Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary multiobjective optimization (EMO) algorithms for handling many-objective (having four or more objectives) optimization problems. In this paper, we recognize a few recent efforts and discuss a number of viable directions for developing a potential EMO algorithm for solving many-objective optimization problems. Thereafter, we suggest a reference-point-based many-objective evolutionary algorithm following NSGA-II framework (we call it NSGA-III) that emphasizes population members that are nondominated, yet close to a set of supplied reference points. The proposed NSGA-III is applied to a number of many-objective test problems with three to 15 objectives and compared with two versions of a recently suggested EMO algorithm (MOEA/D). While each of the two MOEA/D methods works well on different classes of problems, the proposed NSGA-III is found to produce satisfactory results on all problems considered in this paper. This paper presents results on unconstrained problems, and the sequel paper considers constrained and other specialties in handling many-objective optimization problems.


title: druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico.

abstract: Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints; (b) capacity of processing very large molecular data sets; and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models.


title: Olenid trilobites: The oldest known chemoautotrophic symbionts?

abstract: Late Cambrian to early Ordovician trilobites, the family Olenidae, were tolerant of oxygen-poor, sulfur-rich sea floor conditions, and a case is made that they were chemoautotrophic symbionts. Olenids were uniquely adapted to this habitat in the Lower Paleozoic, which was widespread in the Late Cambrian over Scandinavia. This life habit explains distinctive aspects of olenid morphology: wide thoraces and large numbers of thoracic segments, thin cuticle and, in some species, degenerate hypostome, and the occasional development of brood pouches. Geochemical and field evidence is consistent with this interpretation. Olenids occupied their specialized habitat for 60 million years until their extinction at the end of the Ordovician.


title: Spot the Difference by Object Detection

abstract: In this paper, we propose a simple yet effective solution to a change detection task that detects the difference between two images, which we call"spot the difference". Our approach uses CNN-based object detection by stacking two aligned images as input and considering the differences between the two images as objects to detect. An early-merging architecture is used as the backbone network. Our method is accurate, fast and robust while using very cheap annotation. We verify the proposed method on the task of change detection between the digital design and its photographic image of a book. Compared to verification based methods, our object detection based method outperforms other methods by a large margin and gives extra information of location. We compress the network and achieve 24 times acceleration while keeping the accuracy. Besides, as we synthesize the training data for detection using weakly labeled images, our method does not need expensive bounding box annotation.


title: Hyperbolic versus Parabolic Asymptotics in Kinetic Theory toward Fluid Dynamic Models

abstract: In this work we are interested in the hyperbolic limits in kinetic theory. We propose a nonstandard scaling to be understood as a sort of intermediate hyperbolic limit, which connects the (macroscopic) hyperbolic limiting behavior of the physical system with the microscopic properties usually obtained under parabolic scalings. We present our main result by means of a general kinetic frame for the intermediate hyperbolic limit which covers some well-known examples in kinetic theory (Vlasov--Poisson--Fokker--Planck systems and linear relaxation for Boltzmann-type equations in semiconductor theory, among others). We will also apply our methods to deal with the Kac approach to Boltzmann operators.


title: Extracting Top-K Insights from Multi-dimensional Data

abstract: OLAP tools have been extensively used by enterprises to make better and faster decisions. Nevertheless, they require users to specify group-by attributes and know precisely what they are looking for. This paper takes the first attempt towards automatically extracting top-k insights from multi-dimensional data. This is useful not only for non-expert users, but also reduces the manual effort of data analysts. In particular, we propose the concept of insight which captures interesting observation derived from aggregation results in multiple steps (e.g., rank by a dimension, compute the percentage of measure by a dimension). An example insight is: ``Brand B's rank (across brands) falls along the year, in terms of the increase in sales''. Our problem is to compute the top-k insights by a score function. It poses challenges on (i) the effectiveness of the result and (ii) the efficiency of computation. We propose a meaningful scoring function for insights to address (i). Then, we contribute a computation framework for top-k insights, together with a suite of optimization techniques (i.e., pruning, ordering, specialized cube, and computation sharing) to address (ii). Our experimental study on both real data and synthetic data verifies the effectiveness and efficiency of our proposed solution.


title: Design and learnability of vortex whistles for managing chronic lung function via smartphones

abstract: Spirometry is the gold standard for managing and diagnosing obstructive lung diseases. Clinical spirometers, however, are expensive and have limited portability. Vortex whistles have shown promise as a potential substitute for clinical spirometers. While vortex whistles are low-cost and are highly portable, only a subset of common spirometry measurements can be measured reliably. Moreover, no research studies have evaluated characteristics of human interaction with vortex whistles, such as maneuver learnability and mental effort. We present a modified 3D-printed vortex whistle design that enables estimation of spirometry measures not previously attainable with traditional vortex whistles. We evaluate the whistle using a pulmonary waveform generator (a commercial standard) and map parameters of the whistle construction to spirometry test endpoints. Through a human subjects trial we evaluate how to personalize whistle parameters for different subjects and assess cognitive workload while using a vortex whistle. We show that, with personalization, vortex whistles are as effective as clinical spirometers for identifying moderate airway obstruction and require similar cognitive load to use.


Direct manipulation of free-form deformations


title: INDIVIDUAL IDENTIFICATION OF DECAPOD CRUSTACEANS I: COLOR PATTERNS IN ROCK SHRIMP (RHYNCHOCINETES TYPUS)

abstract: Abstract We examined whether color patterns of a marine crustacean may serve to distinguish between individuals and to successfully identify individuals after one molt. Digital images of the rock shrimp Rhynchocinetes typus were digitally processed in order to obtain their diffraction pattern by means of Fast Fourier Transformation (FFT). All diffraction patterns were correlated with a phase-only filter using images of rock shrimp before and after one molt. To determine the degree of similarity of color patterns among rock shrimps, correlation of diffraction pattern was performed. This approach showed that among shrimps the color pattern of the cephalothorax is mainly homogenous (∼83%) in both distribution and intensity. However, the observed degree of variability (∼17%) was sufficient to distinguish between individuals. Furthermore the general color pattern of each individual persisted after one molt and all 14 individuals could be recognized based on their color pattern. It is concluded that this non-in...


title: Decoy document deployment for effective masquerade attack detection

abstract: Masquerade attacks pose a grave security problem that is a consequence of identity theft. Detecting masqueraders is very hard. Prior work has focused on profiling legitimate user behavior and detecting deviations from that normal behavior that could potentially signal an ongoing masquerade attack. Such approaches suffer from high false positive rates. Other work investigated the use of trap-based mechanisms as a means for detecting insider attacks in general. In this paper, we investigate the use of such trap-based mechanisms for the detection of masquerade attacks. We evaluate the desirable properties of decoys deployed within a user's file space for detection.We investigate the trade-offs between these properties through two user studies, and propose recommendations for effective masquerade detection using decoy documents based on findings from our user studies.


title: A Hybrid Document Feature Extraction Method Using Latent Dirichlet Allocation and Word2Vec

abstract: Latent Dirichlet Allocation (LDA) is a probabilistic topic model to discover latent topics from documents and describe each document with a probability distribution over the discovered topics. It defines a global hierarchical relationship from words to a topic and then from topics to a document. Word2Vec is a word-embedding model to predict a target word from its surrounding contextual words. In this paper, we propose a hybrid approach to extract features from documents with bag-of-distances in a semantic space. By using both Word2Vec and LDA, our hybrid method not only generates the relationships between documents and topics, but also integrates the contextual relationships among words. Experimental results indicate that document features generated by our hybrid method are useful to improve classification performance by consolidating both global and local relationships.


title: Capacitive skin sensors for robot impact monitoring

abstract: A new generation of robots is being designed for human occupied workspaces where safety is of great concern. This research demonstrates the use of a capacitive skin sensor for collision detection. Tests demonstrate that the sensor reduces impact forces and can detect and characterize collision events, providing information that may be used in the future for force reduction behaviors. Various parameters that affect collision severity, including interface friction, interface stiffness, end tip velocity and joint stiffness irrespective of controller bandwidth are also explored using the sensor to provide information about the contact force at the site of impact. Joint stiffness is made independent of controller bandwidth limitations using passive torsional springs of various stiffnesses. Results indicate a positive correlation between peak impact force and joint stiffness, skin friction and interface stiffness, with implications for future skin and robot link designs and post-collision behaviors.


title: Teaching Language and Culture with a Virtual Reality Game

abstract: Many people want to learn a language but find it difficult to stay engaged. Ideally, we would have language learning tools that can make language learning more enjoyable by simulating immersion in a foreign language environment. Therefore, we adapted Crystallize, a 3D video game for learning Japanese, so that it can be played in virtual reality with the Oculus Rift. Specifically, we explored whether we could leverage virtual reality technology to teach embodied cultural interaction, such as bowing in Japanese greetings. To evaluate the impact of our virtual reality game designs, we conducted a formative user study with 68 participants. We present results showing that the virtual reality design trained players how and when to bow, and that it increased participants' sense of involvement in Japanese culture. Our results suggest that virtual reality technology provides an opportunity to leverage culturally-relevant physical interaction, which can enhance the design of language learning technology and virtual reality games.


title: Anti-vaccinationists past and present

abstract: The British Vaccination Act of 1840 was the first incursion of the state, in the name of public health, into traditional civil liberties. The activities of today9s propagandists against immunisations are directly descended from, indeed little changed from, those of the anti-vaccinationists of the late nineteenth century, say Robert Wolfe and Lisa Sharp


title: Open-source surface mesh-based ultrasound-guided spinal intervention simulator

abstract: Purpose ::: Ultrasound is prevalent in image-guided therapy as a safe, inexpensive, and widely available imaging modality. However, extensive training in interpreting ultrasound images is essential for successful procedures. An open-source ultrasound image simulator was developed to facilitate the training of ultrasound-guided spinal intervention procedures, thereby eliminating the need for an ultrasound machine from the phantom-based training environment.


title: Management of spasticity revisited

abstract: Spasticity is common after stroke and other neurological conditions and causes considerable limitations of movement, activities of daily living and participation. Interaction with other components of the upper motor neurone syndrome (UMNS) and the heterogeneity of patients’ presentations together with limited tools for outcome measurement have hampered the production of randomised controlled trial data for management strategies. Specialist multi-disciplinary goal-centered management programmes are the mainstay of treatment. Pharmacological therapies have limited effect, and physical and positional management are crucial. Targeted intramuscular botulinum toxin injection is now the most popular pharmacological treatment. Intrathecal therapies also play a lesser role. A team approach and holistic assessment are essential to beneficial outcomes.


title: Expert knowledge for automatic detection of bullies in social networks

abstract: Cyberbullying is a serious social problem in online environments and social networks. Current approaches to tackle this problem are still inadequate for detecting bullying incidents or to flag bullies. In this study we used a multi-criteria evaluation system to obtain a better understanding of YouTube users‟ behaviour and their characteristics through expert knowledge. Based on experts‟ knowledge, the system assigns a score to the users, which represents their level of “bulliness” based on the history of their activities, The scores can be used to discriminate among users with a bullying history and those who were not engaged in hurtful acts. This preventive approach can provide information about users of social networks and can be used to build monitoring tools to aid finding and stopping potential bullies.


title: Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity

abstract: Modeling and predicting the popularity of online content is a significant problem for the practice of information dissemination, advertising, and consumption. Recent work analyzing massive datasets advances our understanding of popularity, but one major gap remains: To precisely quantify the relationship between the popularity of an online item and the external promotions it receives. This work supplies the missing link between exogenous inputs from public social media platforms, such as Twitter, and endogenous responses within the content platform, such as YouTube. We develop a novel mathematical model, the Hawkes intensity process, which can explain the complex popularity history of each video according to its type of content, network of diffusion, and sensitivity to promotion. Our model supplies a prototypical description of videos, called an endo-exo map. This map explains popularity as the result of an extrinsic factor -- the amount of promotions from the outside world that the video receives, acting upon two intrinsic factors -- sensitivity to promotion, and inherent virality. We use this model to forecast future popularity given promotions on a large 5-months feed of the most-tweeted videos, and found it to lower the average error by 28.6% from approaches based on popularity history. Finally, we can identify videos that have a high potential to become viral, as well as those for which promotions will have hardly any effect.


title: Learning Question Classifiers

abstract: In order to respond correctly to a free form factual question given a large collection of texts, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer.This paper presents a machine learning approach to question classification. We learn a hierarchical classifier that is guided by a layered semantic hierarchy of answer types, and eventually classifies questions into fine-grained classes. We show accurate results on a large collection of free-form questions used in TREC 10.


title: Bhopal Gas Tragedy: review of clinical and experimental findings after 25 years

abstract: The Bhopal gas tragedy is undoubtedly one of the worst industrial disasters in the history of mankind resulting in mortality of 2500-6000 and debilitating over 200 000 people. Inhabitants in the township were exposed to different degrees and there are more than 500 000 registered victims that survived the tragedy. Clinical studies have shown chronic illnesses such as pulmonary fibrosis, bronchial asthma, chronic obstructive pulmonary disease (COPD), emphysema, recurrent chest infections, keratopathy and corneal opacities in exposed cohorts. Survivors continue to experience higher incidence of reported health problems including febrile illnesses, respiratory, neurologic, psychiatric and ophthalmic symptoms. In-utero exposure to methyl isocyanate in the first trimester of pregnancy caused a persistent immune system hyper-responsiveness, which was in an evident way genetically linked with the organic exposure. Recent experimental studies have provided mechanistic understanding of methyl isocyanate exposure at a molecular level. Immunotoxic implications, toxico-genomic effect, inflammatory response, elicitation of mitochondrial oxidative stress, chromosomal and microsatellite instability have been studied comprehensively in cultured mammalian cells. Besides providing a framework for understanding potential mechanisms of toxicity of a host of other exposures, these studies may also uncover unique abnormalities thereby stimulating efforts to design newer and effective diagnostic and therapeutic strategies. The authors recommend long-term monitoring of the affected area and use of appropriate methods of investigation that include well-designed cohort studies, case-control studies for rare condition, characterization of personal exposure and accident analysis to determine the possible elements of the gas cloud.


title: Vision-based intelligent vehicles: State of the art and perspectives

abstract: Abstract Recently, a large emphasis has been devoted to Automatic Vehicle Guidance since the automation of driving tasks carries a large number of benefits, such as the optimization of the use of transport infrastructures, the improvement of mobility, the minimization of risks, travel time, and energy consumption. This paper surveys the most common approaches to the challenging task of Autonomous Road Following reviewing the most promising experimental solutions and prototypes developed worldwide using AI techniques to perceive the environmental situation by means of artificial vision. The most interesting results and trends in this field as well as the perspectives on the evolution of intelligent vehicles in the next decades are also sketched out.


title: Clustering very large multi-dimensional datasets with MapReduce

abstract: Given a very large moderate-to-high dimensionality dataset, how could one cluster its points? For datasets that don't fit even on a single disk, parallelism is a first class option. In this paper we explore MapReduce for clustering this kind of data. The main questions are (a) how to minimize the I/O cost, taking into account the already existing data partition (e.g., on disks), and (b) how to minimize the network cost among processing nodes. Either of them may be a bottleneck. Thus, we propose the Best of both Worlds -- BoW method, that automatically spots the bottleneck and chooses a good strategy. Our main contributions are: (1) We propose BoW and carefully derive its cost functions, which dynamically choose the best strategy; (2) We show that BoW has numerous desirable features: it can work with most serial clustering methods as a plugged-in clustering subroutine, it balances the cost for disk accesses and network accesses, achieving a very good tradeoff between the two, it uses no user-defined parameters (thanks to our reasonable defaults), it matches the clustering quality of the serial algorithm, and it has near-linear scale-up; and finally, (3) We report experiments on real and synthetic data with billions of points, using up to 1,024 cores in parallel. To the best of our knowledge, our Yahoo! web is the largest real dataset ever reported in the database subspace clustering literature. Spanning 0.2 TB of multi-dimensional data, it took only 8 minutes to be clustered, using 128 cores.


title: Are assumptions of consumer views impeding nano-based water treatment technologies?

abstract: Initial studies indicate that consumer concerns about nanomaterial use in point-of-use water systems may be overestimated by manufacturers. Nevertheless, manufacturers should continue to focus on building consumer confidence in the safety of their products.


title: Cloud robotics in industry using Raspberry Pi

abstract: Cloud robotics is a field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centered on the benefits of converged infrastructure and shared services for robotics. Our project is a cloud robot which is used in industrial and manufacturing environment. It works on a ROS platform. Here we use Raspberry Pi controller to control the various devices attached to it. For testing this implementation, Android phone, camera, DC motor, sensors and a Raspberry pi controller have been used. The movement of the robot is provided by DC motors and the direction is controlled from an android environment using Robot Operating System (ROS). The controller and the receiver end is connected by Wi-Fi. The data input from the gas, temperature and Infra Red sensors is given to the Raspberry Pi controller. A camera is used to provide visual input of the surrounding environment to the robot. The data obtained by the sensors and camera are processed by the controller and stored in cloud.


title: Environment and Usage Monitoring of Electronic Products for Health Assessment and Product Design

abstract: AbstractThe life-cycle environmental and usage conditions of a product or system can be monitored and analyzed to assess its on-going health, provide advance warning of failure, and provide information to improve the design and qualification of fielded and future products. The challenge lies in the implementation of this method in application conditions. This paper presents methods to effectively collect and analyze life-cycle environmental and usage data for in-situ health assessments. An integrated hardware-software micro-programmable module for health and usage monitoring of electronic products in their application environment is presented. The hardware incorporates local sensors, and on-board processing power using embedded software. These data processing capabilities are intended to enable immediate and localized processing of the raw sensor data to reduce the power and memory consumption anticipated during load monitoring. Guidelines are also provided to develop a life cycle monitoring plan, that en...


title: Kinetic analysis of the lower limbs during walking: What information can be gained from a three-dimensional model?

abstract: Abstract Kinetic analyses (joint moments, powers and work) of the lower limbs were performed during normal walking to determine what further information can be gained from a three-dimensional model over planar models. It was to be determined whether characteristic moment and power profiles exist in the frontal and transverse planes across subjects and how much work was performed in these planes. Kinetic profiles from nine subjects were derived using a three-dimensional inverse dynamics model of the lower limbs and power profiles were then calculated by a dot product of the angular velocities and joint moments resolved in a global reference system. Characteristic joint moment profiles across subjects were found for the hip, knee and ankle joints in all planes except for the ankle frontal moment. As expected, the major portion of work was performed in the plane of progression since the goal of locomotion is to support the body against gravity while generating movements which propel the body forward. However, the results also showed that substantial work was done in the frontal plane by the hip during walking (23% of the total work at that joint). The characteristic joint profiles suggest defined motor patterns and functional roles in the frontal and transverse planes. Kinetic analysis in three dimensions is necessary particularly if the hip joint is being examined as a substantial amount of work was done in the frontal plane of the hip to control the pelvis and trunk against gravitational forces.


title: A 2-approximation algorithm for interval data minmax regret sequencing problems with the total flow time criterion

abstract: In this paper we discuss a minmax regret version of the single-machine scheduling problem with the total flow time criterion. Uncertain processing times are modeled by closed intervals. We show that if the deterministic problem is polynomially solvable, then its minmax regret version is approximable within 2.


title: Power Control in Two-Tier Femtocell Networks

abstract: In a two tier cellular network -- comprised of a central macrocell underlaid with shorter range femtocell hotspots -- cross-tier interference limits overall capacity with universal frequency reuse. To quantify near-far effects with universal frequency reuse, this paper derives a fundamental relation providing the largest feasible cellular Signal-to-Interference-Plus-Noise Ratio (SINR), given any set of feasible femtocell SINRs. We provide a link budget analysis which enables simple and accurate performance insights in a two-tier network. A distributed utility-based SINR adaptation at femtocells is proposed in order to alleviate cross-tier interference at the macrocell from cochannel femtocells. The Foschini-Miljanic (FM) algorithm is a special case of the adaptation. Each femtocell maximizes their individual utility consisting of a SINR based reward less an incurred cost (interference to the macrocell). Numerical results show greater than 30% improvement in mean femtocell SINRs relative to FM. In the event that cross-tier interference prevents a cellular user from obtaining its SINR target, an algorithm is proposed that reduces transmission powers of the strongest femtocell interferers. The algorithm ensures that a cellular user achieves its SINR target even with 100 femtocells/cell-site, and requires a worst case SINR reduction of only 16% at femtocells. These results motivate design of power control schemes requiring minimal network overhead in two-tier networks with shared spectrum.


title: GestureSleeve: using touch sensitive fabrics for gestural input on the forearm for controlling smartwatches

abstract: Smartwatches provide quick and easy access to information. Due to their wearable nature, users can perceive the information while being stationary or on the go. The main drawback of smartwatches, however, is the limited input possibility. They use similar input methods as smartphones but thereby suffer from a smaller form factor. To extend the input space of smartwatches, we present GestureSleeve, a sleeve made out of touch enabled textile. It is capable of detecting different gestures such as stroke based gestures or taps. With these gestures, the user can control various smartwatch applications. Exploring the performance of the GestureSleeve approach, we conducted a user study with a running application as use case. In this study, we show that input using the GestureSleeve outperforms touch input on the smartwatch. In the future the GestureSleeve can be integrated into regular clothing and be used for controlling various smart devices.


title: An effective implementation of the linkernighan traveling salesman heuristic

abstract: Abstract This paper describes an implementation of the Lin–Kernighan heuristic, one of the most successful methods for generating optimal or near-optimal solutions for the symmetric traveling salesman problem (TSP). Computational tests show that the implementation is highly effective. It has found optimal solutions for all solved problem instances we have been able to obtain, including a 13,509-city problem (the largest non-trivial problem instance solved to optimality today).


title: Puberty suppression in gender identity disorder: the Amsterdam experience

abstract: The use of gonadotropin-releasing hormone analogs (GnRHa) to suppress puberty in adolescents with gender dysphoria is a fairly new intervention in the field of gender identity disorders or transsexualism. GnRHa are used to give adolescents time to make balanced decisions on any further treatment steps, and to obtain improved results in the physical appearance of those who opt to continue with sex reassignment. The effects of GnRHa are reversible. However, concerns have been raised about the risk of making the wrong treatment decisions, as gender identity could fluctuate during adolescence, adolescents in general might have poor decision-making abilities, and there are potential adverse effects on health and on psychological and psychosexual functioning. Proponents of puberty suppression emphasize the beneficial effects of GnRHa on the adolescents' mental health, quality of life and of having a physical appearance that makes it possible for the patients to live unobtrusively in their desired gender role. In this Review, we discuss the evidence pertaining to the debate on the effects of GnRHa treatment. From the studies that have been published thus far, it seems that the benefits outweigh the risks. However, more systematic research in this area is needed to determine the safety of this approach.


Leveraging LMSs to Enhance Campus-Based Student Engagement


title: Exercise Intolerance in Adult Congenital Heart Disease: Comparative Severity, Correlates, and Prognostic Implication

abstract: BACKGROUND ::: Although some patients with adult congenital heart disease (ACHD) report limitations in exercise capacity, we hypothesized that depressed exercise capacity may be more widespread than superficially evident during clinical consultation and could be a means of assessing risk. ::: ::: ::: METHODS AND RESULTS ::: Cardiopulmonary exercise testing was performed in 335 consecutive ACHD patients (age, 33+/-13 years), 40 non-congenital heart failure patients (age, 58+/-15 years), and 11 young (age, 29+/-5 years) and 12 older (age, 59+/-9 years) healthy subjects. Peak oxygen consumption (peak VO2) was reduced in ACHD patients compared with healthy subjects of similar age (21.7+/-8.5 versus 45.1+/-8.6; P<0.001). No significant difference in peak VO2 was found between ACHD and heart failure patients of corresponding NYHA class (P=NS for each NYHA class). Within ACHD subgroups, peak VO2 gradually declined from aortic coarctation (28.7+/-10.4) to Eisenmenger (11.5+/-3.6) patients (P<0.001). Multivariable correlates of peak VO2 were peak heart rate (r=0.33), forced expiratory volume (r=0.33), pulmonary hypertension (r=-0.26), gender (r=-0.23), and body mass index (r=-0.19). After a median follow-up of 10 months, 62 patients (18.5%) were hospitalized or had died. On multivariable Cox analysis, peak VO2 predicted hospitalization or death (hazard ratio, 0.937; P=0.01) and was related to the frequency and duration of hospitalization (P=0.01 for each). ::: ::: ::: CONCLUSIONS ::: Exercise capacity is depressed in ACHD patients (even in allegedly asymptomatic patients) on a par with chronic heart failure subjects. Lack of heart rate response to exercise, pulmonary arterial hypertension, and impaired pulmonary function are important correlates of exercise capacity, as is underlying cardiac anatomy. Poor exercise capacity identifies ACHD patients at risk for hospitalization or death.


title: DICOM relay over the cloud

abstract: Purpose ::: Healthcare institutions worldwide have adopted picture archiving and communication system (PACS) for enterprise access to images, relying on Digital Imaging Communication in Medicine (DICOM) standards for data exchange. However, communication over a wider domain of independent medical institutions is not well standardized. A DICOM-compliant bridge was developed for extending and sharing DICOM services across healthcare institutions without requiring complex network setups or dedicated communication channels.


Overview of communication science research in the Netherlands and Flanders


title: Ontology Matching with Knowledge Rules

abstract: Ontology matching is the process of automatically determining the semantic equivalences between the concepts of two ontologies. Most ontology matching algorithms are based on two types of strategies: terminology-based strategies, which align concepts based on their names or descriptions, and structure-based strategies, which exploit concept hierarchies to find the alignment. In many domains, there is additional information about the relationships of concepts represented in various ways, such as Bayesian networks, decision trees, and association rules. We propose to use the similarities between these relationships to find more accurate alignments. We accomplish this by defining soft constraints that prefer alignments where corresponding concepts have the same local relationships encoded as knowledge rules. We use a probabilistic framework to integrate this new knowledge-based strategy with standard terminology-based and structure-based strategies. Furthermore, our method is particularly effective in identifying correspondences between complex concepts. Our method achieves better F-score than the state-of-the-art on three ontology matching domains.


title: Superpixel-based video object segmentation using perceptual organization and location prior

abstract: In this paper we present an approach for segmenting objects in videos taken in complex scenes with multiple and different targets. The method does not make any specific assumptions about the videos and relies on how objects are perceived by humans according to Gestalt laws. Initially, we rapidly generate a coarse foreground segmentation, which provides predictions about motion regions by analyzing how superpixel segmentation changes in consecutive frames. We then exploit these location priors to refine the initial segmentation by optimizing an energy function based on appearance and perceptual organization, only on regions where motion is observed. We evaluated our method on complex and challenging video sequences and it showed significant performance improvements over recent state-of-the-art methods, being also fast enough to be used for “on-the-fly” processing.


title: Aperiodic task scheduling for Hard-Real-Time systems

abstract: This thesis develops the Sporadic Server (SS) algorithm for scheduling aperiodic tasks in real-time systems. The SS algorithm is an extension of the rate monotonic algorithm which was designed to schedule periodic tasks. This thesis demonstrates that the SS algorithm is able to guarantee deadlines for hard-deadline aperiodic tasks and provide good responsiveness for soft-deadline aperiodic tasks while avoiding the schedulability penalty and implementation complexity of previous aperiodic service algorithms. It is also proven that the aperiodic servers created by the SS algorithm can be treated as equivalently-sized periodic tasks when assessing schedulability. This allows all the scheduling theories developed for the rate monotonic algorithm to be used to schedule aperiodic tasks. For scheduling aperiodic and periodic tasks that share data, this thesis defines the interactions and schedulability impact of using the SS algorithm with the priority inheritance protocols. For scheduling hard-deadline tasks with short deadlines, an extension of the rate monotonic algorithm and analysis is developed. To predict performance of the SS algorithm, this thesis develops models and equations that allow the use of standard queueing theory models to predict the average response time of soft-deadline aperiodic tasks serviced with a high-priority sporadic server. Implementation methods are also developed to support the SS algorithm in Ada and on the Futurebus+.


title: Visualizing Fuzzy Overlapping Communities in Networks

abstract: An important feature of networks for many application domains is their community structure. This is because objects within the same community usually have at least one property in common. The investigation of community structure can therefore support the understanding of object attributes from the network topology alone. In real-world systems, objects may belong to several communities at the same time, i.e., communities can overlap. Analyzing fuzzy community memberships is essential to understand to what extent objects contribute to different communities and whether some communities are highly interconnected. We developed a visualization approach that is based on node-link diagrams and supports the investigation of fuzzy communities in weighted undirected graphs at different levels of detail. Starting with the network of communities, the user can continuously drill down to the network of individual nodes and finally analyze the membership distribution of nodes of interest. Our approach uses layout strategies and further visual mappings to graphically encode the fuzzy community memberships. The usefulness of our approach is illustrated by two case studies analyzing networks of different domains: social networking and biological interactions. The case studies showed that our layout and visualization approach helps investigate fuzzy overlapping communities. Fuzzy vertices as well as the different communities to which they belong can be easily identified based on node color and position.


title: A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time

abstract: Rapid growth of the aged population has caused an immense increase in the demand for healthcare services. Generally, the elderly are more prone to health problems compared to other age groups. With effective monitoring and alarm systems, the adverse effects of unpredictable events such as sudden illnesses, falls, and so on can be ameliorated to some extent. Recently, advances in wearable and sensor technologies have improved the prospects of these service systems for assisting elderly people. In this article, we review state-of-the-art wearable technologies that can be used for elderly care. These technologies are categorized into three types: indoor positioning, activity recognition and real time vital sign monitoring. Positioning is the process of accurate localization and is particularly important for elderly people so that they can be found in a timely manner. Activity recognition not only helps ensure that sudden events (e.g., falls) will raise alarms but also functions as a feasible way to guide people's activities so that they avoid dangerous behaviors. Since most elderly people suffer from age-related problems, some vital signs that can be monitored comfortably and continuously via existing techniques are also summarized. Finally, we discussed a series of considerations and future trends with regard to the construction of "smart clothing" system.


title: changepoint:an R package for changepoint analysis

abstract: One of the key challenges in changepoint analysis is the ability to detect multiple changes within a given time series or sequence. The changepoint package has been developed to provide users with a choice of multiple changepoint search methods to use in conjunction with a given changepoint method and in particular provides an implementation of the recently proposed PELT algorithm. This article describes the search methods which are implemented in the package as well as some of the available test statistics whilst highlighting their application with simulated and practical examples. Particular emphasis is placed on the PELT algorithm and how results differ from the binary segmentation approach.


title: Sinus Endoscopy - Application of Advanced GPU Volume Rendering for Virtual Endoscopy

abstract: For difficult cases in endoscopic sinus surgery, a careful planning of the intervention is necessary. Due to the reduced field of view during the intervention, the surgeons have less information about the surrounding structures in the working area compared to open surgery. Virtual endoscopy enables the visualization of the operating field and additional information, such as risk structures (e.g., optical nerve and skull base) and target structures to be removed (e.g., mucosal swelling). The Sinus Endoscopy system provides the functional range of a virtual endoscopic system with special focus on a realistic representation. Furthermore, by using direct volume rendering, we avoid time-consuming segmentation steps for the use of individual patient datasets. However, the image quality of the endoscopic view can be adjusted in a way that a standard computer with a modern standard graphics card achieves interactive frame rates with low CPU utilization. Thereby, characteristics of the endoscopic view are systematically used for the optimization of the volume rendering speed. The system design was based on a careful analysis of the endoscopic sinus surgery and the resulting needs for computer support. As a small standalone application it can be instantly used for surgical planning and patient education. First results of a clinical evaluation with ENT surgeons were employed to fine-tune the user interface, in particular to reduce the number of controls by using appropriate default values wherever possible. The system was used for preoperative planning in 102 cases, provides useful information for intervention planning (e.g., anatomic variations of the Rec. Frontalis), and closely resembles the intraoperative situation.


AR TICLE The Complete Genome Sequence of Escherichia coli K-12


title: Engaging Engineering Students with Gamification

abstract: Well-designed games are good motivators by nature, as they imbue players with clear goals and a sense of reward and fulfillment, thus encouraging them to persist and endure in their quests. Recently, this motivational power has started to be applied to non- game contexts, a practice known as Gamification. This adds gaming elements to non-game processes, motivating users to adopt new behaviors, such as improving their physical condition, working more, or learning something new. This paper describes an experiment in which game-like elements were used to improve the delivery of a Master's level College course, including scoring, levels, leaderboards, challenges and badges. To assess how gamification impacted the learning experience, we compare the gamified course to its non-gamified version from the previous year, using different performance measures. We also assessed student satisfaction as compared to other regular courses in the same academic context. Results were very encouraging, showing significant increases ranging from lecture attendance to online participation, proactive behaviors and perusing the course reference materials. Moreover, students considered the gamified instance to be more motivating, interesting and easier to learn as compared to other courses. We finalize by discussing the implications of these results on the design of future gamified learning experiences.


Reservoir Computing by Felix Grezes


Online Digital Game Addiction: How Does Social Relationship Impact Game Addiction


title: An active learning based TCM-KNN algorithm for supervised network intrusion detection

abstract: As network attacks have increased in number and severity over the past few years, intrusion detection is increasingly becoming a critical component of secure information systems and supervised network intrusion detection has been an active and difficult research topic in the field of intrusion detection for many years. However, it hasn't been widely applied in practice due to some inherent issues. The most important reason is the difficulties in obtaining adequate attack data for the supervised classifiers to model the attack patterns, and the data acquisition task is always time-consuming and greatly relies on the domain experts. In this paper, we propose a novel supervised network intrusion detection method based on TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) machine learning algorithm and active learning based training data selection method. It can effectively detect anomalies with high detection rate, low false positives under the circumstance of using much fewer selected data as well as selected features for training in comparison with the traditional supervised intrusion detection methods. A series of experimental results on the well-known KDD Cup 1999 data set demonstrate that the proposed method is more robust and effective than the state-of-the-art intrusion detection methods, as well as can be further optimized as discussed in this paper for real applications.


title: Chinese Poetry Generation with a Working Memory Model

abstract: As an exquisite and concise literary form, poetry is a gem of human culture. Automatic poetry generation is an essential step towards computer creativity. In recent years, several neural models have been designed for this task. However, among lines of a whole poem, the coherence in meaning and topics still remains a big challenge. In this paper, inspired by the theoretical concept in cognitive psychology, we propose a novel Working Memory model for poetry generation. Different from previous methods, our model explicitly maintains topics and informative limited history in a neural memory. During the generation process, our model reads the most relevant parts from memory slots to generate the current line. After each line is generated, it writes the most salient parts of the previous line into memory slots. By dynamic manipulation of the memory, our model keeps a coherent information flow and learns to express each topic flexibly and naturally. We experiment on three different genres of Chinese poetry: quatrain, iambic and chinoiserie lyric. Both automatic and human evaluation results show that our model outperforms current state-of-the-art methods.


title: Analyzing multiple configurations of a C program

abstract: Preprocessor conditionals are heavily used in C programs since they allow the source code to be configured for different platforms or capabilities. However, preprocessor conditionals, as well as other preprocessor directives, are not part of the C language. They need to be evaluated and removed, and so a single configuration selected, before parsing can take place. Most analysis and program understanding tools run on this preprocessed version of the code so their results are based on a single configuration. This paper describes the approach of CRefactory, a refactoring tool for C programs. A refactoring tool cannot consider only a single configuration: changing the code for one configuration may break the rest of the code. CRefactory analyses the program for all possible configurations simultaneously. CRefactory also preserves preprocessor directives and integrates them in the internal representations. The paper also presents metrics from two case studies to show that CRefactory's program representation is practical.


title: A model for types and levels of human interaction with automation

abstract: We outline a model for types and levels of automation that provides a framework and an objective basis for deciding which system functions should be automated and to what extent. Appropriate selection is important because automation does not merely supplant but changes human activity and can impose new coordination demands on the human operator. We propose that automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic. A particular system can involve automation of all four types at different levels. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design using our model. Secondary evaluative criteria include automation reliability and the costs of decision/action consequences, among others. Examples of recommended types and levels of automation are provided to illustrate the application of the model to automation design.


title: Minimally invasive direct restorations: a practical guide

abstract: The primary objectives of minimum intervention dentistry (MID) are to prevent or arrest active disease using non-operative management techniques. However, patients commonly present with cavitated caries lesions or failed restorations that are in need of operative intervention. Although much of clinical practice is devoted to preventing and managing the effects of caries and subsequent failure of the tooth-restoration complex, the clinical survival of restorations is often poor and becomes significantly worse as they increase in size and complexity. Minimally invasive (MI) restorative techniques present a range of well-documented advantages over more tissue-destructive traditional restorations by minimising unnecessary tooth tissue loss, insult to the dentine-pulp complex and reducing the risk of iatrogenic damage to adjacent hard and soft tissues. They also maximise the strength of the residual tooth structure by use of optimal adhesive restorative materials designed to restore function and aesthetics with durable, long-lasting restorations that are easy for the patient to maintain. In contemporary oral healthcare practice, if patients are to give valid consent for operative interventions, minimally invasive options must be offered, and may be expected to be the first choice of fully informed patients. This paper describes concepts of MID and provides an update of the latest materials, equipment and clinical techniques that are available for the minimally invasive restoration of anterior and posterior teeth with direct restorations.


title: AVFI: Fault Injection for Autonomous Vehicles

abstract: Autonomous vehicle (AV) technology is rapidly becoming a reality on U.S. roads, offering the promise of improvements in traffic management, safety, and the comfort and efficiency of vehicular travel. With this increasing popularity and ubiquitous deployment, resilience has become a critical requirement for public acceptance and adoption. Recent studies into the resilience of AVs have shown that though the AV systems are improving over time, they have not reached human levels of automation. Prior work in this area has studied the safety and resilience of individual components of the AV system (e.g., testing of neural networks powering the perception function). However, methods for holistic end-to-end resilience assessment of AV systems are still non-existent.


title: A Dual-Polarized Magneto-Electric Dipole With Dielectric Loading

abstract: A plusmn45degdual-polarized magneto-electric dipole loaded with dielectric substrate is presented. This antenna is composed of shorted-circuited patches and planar dipoles and is fed by four Gamma-shaped probes. The height of a prototype antenna operated at 1.9 GHz is only 24 mm (0.15lambdao). A wide impedance bandwidth of 24.9% is achieved. The average gain is about 8.2 dBi and the isolation between the two ports is higher than 29 dB over the operating bandwidth. It is demonstrated that the antenna performance can be improved by adding metallic side walls.


title: Virtualized in-cloud security services for mobile devices

abstract: Modern mobile devices continue to approach the capabilities and extensibility of standard desktop PCs. Unfortunately, these devices are also beginning to face many of the same security threats as desktops. Currently, mobile security solutions mirror the traditional desktop model in which they run detection services on the device. This approach is complex and resource intensive in both computation and power. This paper proposes a new model whereby mobile antivirus functionality is moved to an off-device network service employing multiple virtualized malware detection engines. Our argument is that it is possible to spend bandwidth resources to significantly reduce on-device CPU, memory, and power resources. We demonstrate how our in-cloud model enhances mobile security and reduces on-device software complexity, while allowing for new services such as platform-specific behavioral analysis engines. Our benchmarks on Nokia's N800 and N95 mobile devices show that our mobile agent consumes an order of magnitude less CPU and memory while also consuming less power in common scenarios compared to existing on-device antivirus software.


title: FMAP: Distributed Cooperative Multi-Agent Planning

abstract: This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by h D T G , a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.


title: Location-Aware Communications for 5G Networks: How location information can improve scalability, latency, and robustness of 5G

abstract: Fifth-generation (5G) networks will be the first generation to benefit from location information that is sufficiently precise to be leveraged in wireless network design and optimization. We argue that location information can aid in addressing several of the key challenges in 5G, complementary to existing and planned technological developments. These challenges include an increase in traffic and number of devices, robustness for mission-critical services, and a reduction in total energy consumption and latency. This article gives a broad overview of the growing research area of location-aware communications across different layers of the protocol stack. We highlight several promising trends, tradeoffs, and pitfalls.


title: Biological Processes Associated with Breast Cancer Clinical Outcome Depend on the Molecular Subtypes

abstract: Purpose: Recently, several prognostic gene expression signatures have been identified; however, their performance has never been evaluated according to the previously described molecular subtypes based on the estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2), and their biological meaning has remained unclear. Here we aimed to perform a comprehensive meta-analysis integrating both clinicopathologic and gene expression data, focusing on the main molecular subtypes. Experimental Design: We developed gene expression modules related to key biological processes in breast cancer such as tumor invasion, immune response, angiogenesis, apoptosis, proliferation, and ER and HER2 signaling, and then analyzed these modules together with clinical variables and several prognostic signatures on publicly available microarray studies (>2,100 patients). Results: Multivariate analysis showed that in the ER+/HER2− subgroup, only the proliferation module and the histologic grade were significantly associated with clinical outcome. In the ER−/HER2− subgroup, only the immune response module was associated with prognosis, whereas in the HER2+ tumors, the tumor invasion and immune response modules displayed significant association with survival. Proliferation was identified as the most important component of several prognostic signatures, and their performance was limited to the ER+/HER2− subgroup. Conclusions: Although proliferation is the strongest parameter predicting clinical outcome in the ER+/HER2− subtype and the common denominator of most prognostic gene signatures, immune response and tumor invasion seem to be the main molecular processes associated with prognosis in the ER−/HER2− and HER2+ subgroups, respectively. These findings may help to define new clinicogenomic models and to identify new therapeutic strategies in the specific molecular subgroups.


title: Knowledge management and organizational culture: a theoretical integrative framework

abstract: Purpose – Organizational culture is a critical factor in building and reinforcing knowledge management in organizations. However, there is no theoretical framework that comprehensively explains the effect of organizational culture on knowledge management in organizations. This paper endeavors to develop a theoretical integrative framework for organizational knowledge management and organizational culture.Design/methodology/approach – This is a conceptual paper. It modifies the “competing value framework” by adding a new dimension representing ethical and trusting culture, and then integrates it with the SECI model of knowledge creation and conversion by identifying the conceptual parallels between the two frameworks and then analyzing the interaction effects among the dimensions.Findings – Based on the congruity between the modified competing values framework and the knowledge creation and conversion framework, the paper formulates six propositions about the propensity of organizations of different domina...


title: Marketplace or reseller?

abstract: Intermediaries can choose between functioning as a marketplace in which suppliers sell their products directly to buyers or as a reseller by purchasing products from suppliers and selling them to buyers. We model this as a decision between whether control rights over a noncontractible decision variable the choice of some marketing activity are better held by suppliers the marketplace mode or by the intermediary the reseller mode. Whether the marketplace or the reseller mode is preferred depends on whether independent suppliers or the intermediary have more important information relevant to the optimal tailoring of marketing activities for each specific product. We show that this trade-off is shifted toward the reseller mode when marketing activities create spillovers across products and when network effects lead to unfavorable expectations about supplier participation. If the reseller has a variable cost advantage respectively, disadvantage relative to the marketplace, then the trade-off is shifted toward the marketplace for long-tail respectively, short-tail products. We thus provide a theory of which products an intermediary should offer in each mode. We also provide some empirical evidence that supports our main results. ::: ::: Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.2042 . ::: ::: This paper was accepted by Bruno Cassiman, business strategy.


title: Facial expression and Emotion

abstract: Cross-cultural research on facial expression and the developments of methods to measure facial expression are briefly summarized. What has been learned about emotion from this work on the face is then elucidated. Four questions about facial expression and emotion are discussed. What information does an expression typically convey? Can there be emotion without facial expression? Can there be a facial expression of emotion without emotion? How do individuals differ in their facial expressions of emotion?


title: Procedural Arrangement of Furniture for Real-Time Walkthroughs

abstract: This paper presents a procedural approach to generate furniture arrangements for large virtual indoor scenes. The interiors of buildings in 3D city scenes are often omitted. Our solution creates rich furniture arrangements for all rooms of complex buildings and even for entire cities. The key idea is to only furnish the rooms in the vicinity of the viewer while the user explores a building in real time. In order to compute the object layout we introduce an agent-based solution and demonstrate the flexibility and effectiveness of the agent approach. Furthermore, we describe advanced features of the system, like procedural furniture geometry, persistent room layouts, and styles for high-level control.


title: Human Grasp Choice and Robotic Grasp Analysis

abstract: In studying grasping and manipulation we find two very different approaches to the subject: knowledge-based approaches based primarily on empirical studies of human grasping and manipulation, and analytical approaches based primarily on physical models of the manipulation process. This chapter begins with a review of studies of human grasping, in particular our development of a grasp taxonomy and an expert system for predicting human grasp choice. These studies show how object geometry and task requirements (as well as hand capabilities and tactile sensing) combine to dictate grasp choice. We then consider analytic models of grasping and manipulation with robotic hands. To keep the mathematics tractable, these models require numerous simplifications which restrict their generality. Despite their differences, the two approaches can be correlated. This provides insight into why people grasp and manipulate objects as they do, and suggests different approaches for robotic grasp and manipulation planning. The results also bear upon such issues such as object representation and hand design.