title: Data summaries for on-demand queries over linked data

abstract: Typical approaches for querying structured Web Data collect (crawl) and pre-process (index) large amounts of data in a central data repository before allowing for query answering. However, this time-consuming pre-processing phase however leverages the benefits of Linked Data -- where structured data is accessible live and up-to-date at distributed Web resources that may change constantly -- only to a limited degree, as query results can never be current. An ideal query answering system for Linked Data should return current answers in a reasonable amount of time, even on corpora as large as the Web. Query processors evaluating queries directly on the live sources require knowledge of the contents of data sources. In this paper, we develop and evaluate an approximate index structure summarising graph-structured content of sources adhering to Linked Data principles, provide an algorithm for answering conjunctive queries over Linked Data on theWeb exploiting the source summary, and evaluate the system using synthetically generated queries. The experimental results show that our lightweight index structure enables complete and up-to-date query results over Linked Data, while keeping the overhead for querying low and providing a satisfying source ranking at no additional cost.


title: Transfer Learning in a Transductive Setting

abstract: Category models for objects or activities typically rely on supervised learning requiring sufficiently large training sets. Transferring knowledge from known categories to novel classes with no or only a few labels is far less researched even though it is a common scenario. In this work, we extend transfer learning with semi-supervised learning to exploit unlabeled instances of (novel) categories with no or only a few labeled instances. Our proposed approach Propagated Semantic Transfer combines three techniques. First, we transfer information from known to novel categories by incorporating external knowledge, such as linguistic or expert-specified information, e.g., by a mid-level layer of semantic attributes. Second, we exploit the manifold structure of novel classes. More specifically we adapt a graph-based learning algorithm - so far only used for semi-supervised learning -to zero-shot and few-shot learning. Third, we improve the local neighborhood in such graph structures by replacing the raw feature-based representation with a mid-level object- or attribute-based representation. We evaluate our approach on three challenging datasets in two different applications, namely on Animals with Attributes and ImageNet for image classification and on MPII Composites for activity recognition. Our approach consistently outperforms state-of-the-art transfer and semi-supervised approaches on all datasets.


title: ECPB: Efficient Conditional Privacy-Preserving Authentication Scheme Supporting Batch Verification for VANETs

abstract: In this paper, we introduce an efficient Conditional Privacy-Preserving authentication scheme (ECPB) based on group signature for vehicular ad hoc networks (VANETs). Although group signature is widely used in VANETs for security requirements, the existing schemes based on group signatures suffer longer computational delays in the certificate revocation list (CRL) checking and in the signature verification process, leading to lower verification efficiency. In our scheme, membership validity (a validity period) is required when a vehicle applies for a group member and this validity is used to check whether the vehicle is still a group member or not, which can be used as a substitute for the CRL checks. Neglecting the CRL checks will sharply decrease the costs incurred in the signatures verification. In addition, our proposed scheme also supports batch verification. Experimental analysis proves that our proposed scheme exhibit improved efficiency over the existing schemes, in terms of verification delay and average delay.


title: Nonlinear Control for DC–DC Converters Based on Hysteresis of the $C_{\rm OUT}$ Current With a Frequency Loop to Operate at Constant Frequency

abstract: The nonlinear and linear controls proposed by Alou and Soto provide very fast transient response (voltage step from 1 to 1.5 V in 2 μs). This nonlinear control is based on hysteretic control of Cout current. This system is very sensitive to effects, like aging, temperature, input and output voltage variation, etc., that modify the switching frequency. This paper proposes a frequency loop to avoid the frequency variation and to adjust the switching frequency to the nominal value by changing the hysteretic band. A 5-MHz buck converter is developed, and experimental results validate the loop design, obtaining the same fast transient response (from 1.5 to 2.5 V in 2 μs) while keeping switching frequency constant in steady state.


title: Concealed lung anatomy in Botticelli’s masterpieces The Primavera and The Birth of Venus

abstract: Sandro Botticelli was one of the most esteemed painters and draughtsmen among Renaissance artists. Under the patronage of the De' Medici family, he was active in Florence during the flourishing of the Renaissance trend towards the reclamation of lost medical and anatomical knowledge of ancient times through the dissection of corpses. Combining the typical attributes of the elegant courtly style with hallmarks derived from the investigation and analysis of classical templates, he left us immortal masterpieces, the excellence of which incomprehensibly waned and was rediscovered only in the 1890s. Few know that it has already been reported that Botticelli concealed the image of a pair of lungs in his masterpiece, The Primavera. The present investigation provides evidence that Botticelli embedded anatomic imagery of the lung in another of his major paintings, namely, The Birth of Venus. Both canvases were most probably influenced and enlightened by the neoplatonic philosophy of the humanist teachings in the De' Medici's circle, and they represent an allegorical celebration of the cycle of life originally generated by the Divine Wind or Breath. This paper supports the theory that because of the anatomical knowledge to which he was exposed, Botticelli aimed to enhance the iconographical meaning of both the masterpieces by concealing images of the lung anatomy within them.


title: An Inverse Shortest Path Problem on an Uncertain Graph

abstract: The inverse shortest path problem is to minimize the modification on the edge weights such that a predetermined path becomes the shortest one from the origin to the destination with respect to the new edge weights. In this paper, the inverse shortest path problem is considered on a graph with uncertain edge weights. It is shown that the model of the uncertain inverse shortest path problem can be transformed into a deterministic counterpart and then be solved efficiently. A numerical example is presented as well for illustration.


title: Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition

abstract: In this paper, we propose a deep progressive reinforcement learning (DPRL) method for action recognition in skeleton-based videos, which aims to distil the most informative frames and discard ambiguous frames in sequences for recognizing actions. Since the choices of selecting representative frames are multitudinous for each video, we model the frame selection as a progressive process through deep reinforcement learning, during which we progressively adjust the chosen frames by taking two important factors into account: (1) the quality of the selected frames and (2) the relationship between the selected frames to the whole video. Moreover, considering the topology of human body inherently lies in a graph-based structure, where the vertices and edges represent the hinged joints and rigid bones respectively, we employ the graph-based convolutional neural network to capture the dependency between the joints for action recognition. Our approach achieves very competitive performance on three widely used benchmarks.


title: NFC-Enabled Wireless Charging

abstract: This paper summarizes the results of our concept development for NFC-enabled wireless charging -- i.e., an add-on to the existing NFC operating modes that implements charging power transmission and charging power reception with associated control functionality. When compared to dedicated wireless charging solutions, integration of NFC and wireless charging into the same implementation will entail more compact and cost-efficient charging interfaces for portable devices, a more pervasive charger infrastructure in future with reduced costs, and the possibility of integrating NFC-based services into charging applications. We propose to the NFC community a closer investigation of use cases, the business potential, and technical implementation of NFC-enabled charging. This should lead to a road map for its wider adoption, pave the way to its standardization, technology development, and commercial application, and also support enhancement of power transfer capability in other NFC applications alongside wireless charging.


title: Design, implementation, and non-linear control of interior permanent magnet synchronous motor with flux concentration by improved PWM-rotor design

abstract: The design, implementation, and control of rotor topology for an interior permanent magnet synchronous motor (IPMSM) is presented in this paper. The method is an arrangement of buried magnets in a flux concentration structure, combined with Pulse Width Modulation (PWM), which act on the iron core of the rotor, known as the PWM-rotor. The unsatisfied harmonic contents of the flux density in the air-gap are reduced with this technique, and the total harmonics distortion (THD) of the back electromotive force (back-EMF) subsequently decreases. The design was chosen for a motor size of 350 W, 500 rpm, 36 slots stator lamination, and 12-poles. Moreover, a nonlinear control approach based on differential flatness properties is proposed for a vector control of the PMSM drive. Using the flatness property, we propose simple solutions to PMSM drive and stabilization problems. The design controller parameters are autonomous from the operating point; moreover, high dynamics in load torque disturbance rejection is achieved. To validate the proposed method, a hardware system was built, and digital estimation was accomplished with a dSPACE controller. Experimental and simulation results corroborate the excellence of the PMSM design and control scheme.


title: Tendon transmission efficiency of a two-finger haptic glove

abstract: This paper presents a two-fingered haptic interface named RML-glove. With this system, the operator can feel the shape and size of virtual 3D objects, and control a robot through force feedback. The tendon driven system makes this haptic glove a lighter and portable system that fits on a bare hand, and adds a haptic sense of force feedback to the fingers without constraining their natural movement. In order to explore the effect of cable friction and frictional losses in this system, experiments were conducted to investigate the impact of different variables including pulleys' active arc, tendon velocity, as well as cable tension and lubrication.


title: Positive Technology: Using Interactive Technologies to Promote Positive Functioning

abstract: Abstract It is generally assumed that technology assists individuals in improving the quality of their lives. However, the impact of new technologies and media on well-being and positive functioning is still somewhat controversial. In this paper, we contend that the quality of experience should become the guiding principle in the design and development of new technologies, as well as a primary metric for the evaluation of their applications. The emerging discipline of Positive Psychology provides a useful framework to address this challenge. Positive Psychology is the scientific study of optimal human functioning and flourishing. Instead of drawing on a “disease model” of human behavior, it focuses on factors that enable individuals and communities to thrive and build the best in life. In this paper, we propose the “Positive Technology” approach—the scientific and applied approach to the use of technology for improving the quality of our personal experience through its structuring, augmentation, and/or re...


title: Hierarchical double Dirichlet process mixture of Gaussian processes

abstract: We consider an infinite mixture model of Gaussian processes that share mixture components between nonlocal clusters in data. Meeds and Osindero (2006) use a single Dirichlet process prior to specify a mixture of Gaussian processes using an infinite number of experts. In this paper, we extend this approach to allow for experts to be shared non-locally across the input domain. This is accomplished with a hierarchical double Dirichlet process prior, which builds upon a standard hierarchical Dirichlet process by incorporating local parameters that are unique to each cluster while sharing mixture components between them. We evaluate the model on simulated and real data, showing that sharing Gaussian process components non-locally can yield effective and useful models for richly clustered non-stationary, non-linear data.


title: Fine-grained test case prioritization for integration testing of delta-oriented software product lines

abstract: Software product line (SPL) testing is a challenging task, due to the huge number of variants sharing common functionalities to be taken into account for efficient testing. By adopting the concept of regression testing, incremental SPL testing strategies cope with this challenge by exploiting the reuse potential of test artifacts between subsequent variants under test. In previous work, we proposed delta-oriented test case prioritization for incremental SPL integration testing, where differences between architecture test model variants allow for reasoning about the order of reusable test cases to be executed. However, the prioritization left two issues open, namely (1) changes to component behavior are ignored, which may also influence component interactions and, (2) the weighting and ordering of similar test cases result in an unintended clustering of test cases. In this paper, we extend the test case prioritization technique by (1) incorporating changes to component behavior allowing for a more fine-grained analysis and (2) defining a dissimilarity measure to avoid clustered test case orders. We prototyped our test case prioritization technique and evaluated its applicability and effectiveness by means of a case study from the automotive domain showing positive results.


title: A Brief Overview of Existing Tools for Testing the Internet-of-Things

abstract: Systems are error-prone. Big systems have lots of errors. The Internet-of-Things poses us one of the biggest and widespread systems, where errors directly impact people's lives. Testing and validating is how one deals with errors; but testing and validating a planetary-scale, heterogeneous, and evergrowing ecosystem has its own challenges and idiosyncrasies. As of today, the solutions available for testing these systems are insufficient and fragmentary. In this paper we provide an overview on test approaches, tools and methodologies for the Internet-of-Things, its software and its devices. Our conclusion is that we are still lagging behind on the best practices and lessons learned from the Software Engineering community in the past decades.


title: Land use and land cover spatiotemporal dynamic pattern and predicting changes using integrated CA-Markov model

abstract: Analyzing the process of land use and cover changes during long periods of time and predicting the future changes is highly important and useful for the land use managers. In this study, the land use maps in the Ardabil plain in north-west part of Iran for four periods (1989, 1998, 2009 and 2013) are extracted and analyzed through remote sensing technique, using the land-sat satellite images. Then, the future land use changes are simulated for 2030 using integrated CA-Markov model according to the scenario of continuing current management process. The results show that in the period between 1989 and 2009, i.e. since two-thirds of the plain was declared restricted till all of it was declared thus, the study area has experienced a total of about 58645.08 ha changes. After the whole plain was restricted (since 2009 till 2014), the changes have been estimated to be 22466.88 ha. The prediction also indicates that the changes will equal 8908.83 ha by 2030. Agricultural lands and human-built environment constitute the majority of changes and are increasing continuously. The obtained Kappa values for the model accuracy assessment (higher than 0.8) indicated the models capability to predict future Land use/cover changes in the study area. Thus, analyzing Land use and cover changes trends from past to near future using CA-Markov model can play a significant role in land use policy making, planning, and managing of the restricted plains especially in the proposed study area.


title: Assessing and Improving the Performance of Speech Recognition for Incremental Systems

abstract: In incremental spoken dialogue systems, partial hypotheses about what was said are required even while the utterance is still ongoing. We define measures for evaluating the quality of incremental ASR components with respect to the relative correctness of the partial hypotheses compared to hypotheses that can optimize over the complete input, the timing of hypothesis formation relative to the portion of the input they are about, and hypothesis stability, defined as the number of times they are revised. We show that simple incremental post-processing can improve stability dramatically, at the cost of timeliness (from 90 % of edits of hypotheses being spurious down to 10 % at a lag of 320 ms). The measures are not independent, and we show how system designers can find a desired operating point for their ASR. To our knowledge, we are the first to suggest and examine a variety of measures for assessing incremental ASR and improve performance on this basis.


title: A review on the applications of type-2 fuzzy logic in classification and pattern recognition

abstract: Abstract In this paper a review of type-2 fuzzy logic applications in pattern recognition, classification and clustering problems is presented. Recently, type-2 fuzzy logic has gained popularity in a wide range of applications due to its ability to handle higher degrees of uncertainty. In particular, there have been recent applications of type-2 fuzzy logic in the fields of pattern recognition, classification and clustering, where it has helped improving results over type-1 fuzzy logic. In this paper a concise and representative review of the most successful applications of type-2 fuzzy logic in these fields is presented.


title: Real-Word Spelling Correction using Google Web 1T 3-grams

abstract: We present a method for detecting and correcting multiple real-word spelling errors using the Google Web IT 3-gram data set and a normalized and modified version of the Longest Common Subsequence (LCS) string matching algorithm. Our method is focused mainly on how to improve the detection recall (the fraction of errors correctly detected) and the correction recall (the fraction of errors correctly amended), while keeping the respective precisions (the fraction of detections or amendments that are correct) as high as possible. Evaluation results on a standard data set show that our method outperforms two other methods on the same task.


title: Data-driven physics for human soft tissue animation

abstract: Data driven models of human poses and soft-tissue deformations can produce very realistic results, but they only model the visible surface of the human body and cannot create skin deformation due to interactions with the environment. Physical simulations can generalize to external forces, but their parameters are difficult to control. In this paper, we present a layered volumetric human body model learned from data. Our model is composed of a data-driven inner layer and a physics-based external layer. The inner layer is driven with a volumetric statistical body model (VSMPL). The soft tissue layer consists of a tetrahedral mesh that is driven using the finite element method (FEM). Model parameters, namely the segmentation of the body into layers and the soft tissue elasticity, are learned directly from 4D registrations of humans exhibiting soft tissue deformations. The learned two layer model is a realistic full-body avatar that generalizes to novel motions and external forces. Experiments show that the resulting avatars produce realistic results on held out sequences and react to external forces. Moreover, the model supports the retargeting of physical properties from one avatar when they share the same topology.


title: A framework for classifying and comparing architecture description languages

abstract: Software architectures shift developers' focus from lines-of-code to coarser-grained architectural elements and their interconnection structure. Architecture description languages (ADLs) have been proposed as modeling notations to support architecture-based development. There is, however, little consensus in the research community on what is an ADL, what aspects of an architecture should be modeled in an ADL, and which ADL is best suited for a particular problem. Furthermore, the distinction is rarely made between ADLs on one hand and formal specification, module interconnection, simulation, and programming languages on the other. This paper attempts to provide an answer to these questions. It motivates and presents a definition and a classification framework for ADLs. The utility of the definition is demonstrated by using it to differentiate ADLs from other modeling notations. The framework is used to classify and compare several existing ADLs.


title: From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood

abstract: Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself. Consequently, we must search the space of programs for those that output the correct result, while not being misled by spurious programs: incorrect programs that coincidentally output the correct result. We connect two common learning paradigms, reinforcement learning (RL) and maximum marginal likelihood (MML), and then present a new learning algorithm that combines the strengths of both. The new algorithm guards against spurious programs by combining the systematic search traditionally employed in MML with the randomized exploration of RL, and by updating parameters such that probability is spread more evenly across consistent programs. We apply our learning algorithm to a new neural semantic parser and show significant gains over existing state-of-the-art results on a recent context-dependent semantic parsing task.


title: Analysis of Cell Fate from Single-Cell Gene Expression Profiles in C. elegans

abstract: The C. elegans cell lineage provides a unique opportunity to look at how cell lineage affects patterns of gene expression. We developed an automatic cell lineage analyzer that converts high-resolution images of worms into a data table showing fluorescence expression with single-cell resolution. We generated expression profiles of 93 genes in 363 specific cells from L1 stage larvae and found that cells with identical fates can be formed by different gene regulatory pathways. Molecular signatures identified repeating cell fate modules within the cell lineage and enabled the generation of a molecular differentiation map that reveals points in the cell lineage when developmental fates of daughter cells begin to diverge. These results demonstrate insights that become possible using computational approaches to analyze quantitative expression from many genes in parallel using a digital gene expression atlas.


Regulatory watch: Challenges in drug development for central nervous system disorders: a European Medicines Agency perspective


title: Design of high voltage capacitor charger with improved efficiency, power density and reliability

abstract: This paper describes the design of a 48 kJ/s high-voltage capacitor charging power supply (CCPS), focusing on its efficiency, power density, and reliability. On the basis of a series-parallel resonant converter (SPRC) that provides high efficiency and high power density owing to its soft-switching, the design of the CCPS is explained in detail, including its input filter, resonant tank parameters, high-voltage transformer and rectifier, as well as its protection circuit. By using two resonances per switching cycle, which provides a trapezoidal instead of a sinusoidal waveform of the resonant current, the proposed CCPS can take advantage of the lower conduction loss and reduced switching loss by improving the crest factor and allowing a higher value of the snubber capacitor, respectively. In addition, the compact design of an input filter without bulky components such as a DC reactor and an electrolytic capacitor allows for high power density, a high power factor, and low cost. In addition, the control loops for the voltage and current were optimized with a fast response time in order to compensate for the low frequency ripple of the input voltage, which results from the reduced filter component. Experiments on the developed charger were carried out with both resistor and capacitor loads in order to measure not only its efficiency and power factor with respect to the output power but also its charging time, in order to estimate the average charging current. The experimental results obtained with a resistor load showed a maximum efficiency of 96% and a power factor of 0.96 for a full-load condition. For the measured charging time of a 4 mF capacitor, with 9.68 s for 10 kV charging, the average charging current was estimated as 4.13 A. Moreover, to verify the reliability of the developed CCPS, a variety of tests, including opening and shorting of the output terminal as well as misfiring of the discharge switch during the charging operation, were performed with a 200 kJ pulsed power system. Finally, it was experimentally confirmed that the developed CCPS shows high performance in terms of efficiency (96 %), power factor (0.96), and reliability with a high power density (820 W/L).


title: Querying IoT services: A smart carpark recommender use case

abstract: Variety and diversity of IoT services is growing along with ever increasing development and deployment of IoT ecosystems. In this paper, we propose a Context Service Description Language (CSDL) and a Context Service Matchmaking (CSM) mechanism which are core components of Context-as-a-Service (CoaaS) IoT platform. CoaaS is one of major IoT services in EU Horizon-2020 project called bIoTope. CSDL is an abstract language and supports definition of context as well as IoT services in terms of their semantic signatures, service characteristics, and contextual behavior specification. CoaaS enables applications to provide and consume contextual information by IoT-services seamlessly without requiring manual integration of IoT silos. We demonstrate and evaluate the proposed CoaaS IoT service via a smart parking use case that highlights how CoaaS can be used to discover and consume IoT-services.


title: Detection and localization of multiple short range targets using FMCW radar signal

abstract: In this paper, a 24 GHz frequency-modulated continuous wave radar is used to detect and localize both stationary and moving targets. Depending on the application, the implemented software offers different modes of operation. For example, it can simply output raw data samples for advanced offline processing or directly carry out a two dimensional fast Fourier transform to estimate the location and velocity of multiple targets. To suppress clutter and detect only moving targets, two methods based on the background reduction and the slow time processing techniques are implemented. A trade-off between the two methods is presented based on their performance and the required processing time.


title: Robustness of Conditional GANs to Noisy Labels

abstract: We study the problem of learning conditional generators from noisy labeled samples, where the labels are corrupted by random noise. A standard training of conditional GANs will not only produce samples with wrong labels, but also generate poor quality samples. We consider two scenarios, depending on whether the noise model is known or not. When the distribution of the noise is known, we introduce a novel architecture which we call Robust Conditional GAN (RCGAN). The main idea is to corrupt the label of the generated sample before feeding to the adversarial discriminator, forcing the generator to produce samples with clean labels. This approach of passing through a matching noisy channel is justified by corresponding multiplicative approximation bounds between the loss of the RCGAN and the distance between the clean real distribution and the generator distribution. This shows that the proposed approach is robust, when used with a carefully chosen discriminator architecture, known as projection discriminator. When the distribution of the noise is not known, we provide an extension of our architecture, which we call RCGAN-U, that learns the noise model simultaneously while training the generator. We show experimentally on MNIST and CIFAR-10 datasets that both the approaches consistently improve upon baseline approaches, and RCGAN-U closely matches the performance of RCGAN.


title: Role of Awareness and Universal Context in a Spiking Conscious Neural Network (SCNN): A New Perspective and Future Directions

abstract: Awareness plays a major role in human cognition and adaptive behaviour, though mechanisms involved remain unknown. Awareness is not an objectively established fact, therefore, despite extensive research, scientists have not been able to fully interpret its contribution in multisensory integration and precise neural firing, hence, questions remain: (1) How the biological neuron integrates the incoming multisensory signals with respect to different situations? (2) How are the roles of incoming multisensory signals defined (selective amplification/attenuation) that help neuron(s) to originate a precise neural firing complying with the anticipated behavioural-constraint of the environment? (3) How are the external environment and anticipated behaviour integrated? Recently, scientists have exploited deep learning to integrate multimodal cues and capture context-dependent meanings. Yet, these methods suffer from imprecise behavioural representation. In this research, we introduce a new theory on the role of awareness and universal context that can help answering the aforementioned crucial neuroscience questions. Specifically, we propose a class of spiking conscious neuron in which the output depends on three functionally distinctive integrated input variables: receptive field (RF), local contextual field (LCF), and universal contextual field (UCF). The RF defines the incoming ambiguous sensory signal, LCF defines the modulatory signal coming from other parts of the brain, and UCF defines the awareness. It is believed that the conscious neuron inherently contains enough knowledge about the situation in which the problem is to be solved based on past learning and reasoning and it defines the precise role of incoming multisensory signals to originate a precise neural firing (exhibiting switch-like behaviour). It is shown that the conscious neuron helps modelling a more precise human behaviour.


Interactive Narrative Personalization with Deep Reinforcement Learning


title: Adapting Open Information Extraction to Domain-Specific Relations

abstract: Information extraction (IE) can identify a set of relations from free text to support question answering (QA). Until recently, IE systems were domain-specific and needed a combination of manual engineering and supervised learning to adapt to each target domain. A new paradigm, Open IE operates on large text corpora without any manual tagging of relations, and indeed without any pre-specified relations. Due to its open-domain and open-relation nature, Open IE is purely textual and is unable to relate the surface forms to an ontology, if known in advance. We explore the steps needed to adapt Open IE to a domain-specific ontology and demonstrate our approach of mapping domain-independent tuples to an ontology using domains from DARPA’s Machine Reading Project. Our system achieves precision over 0.90 from as few as 8 training examples for an NFL-scoring domain.


title: UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem. Periodica Mathematica Hungarica

abstract: In the stochastic multi-armed bandit problem we consider a modification of the UCB algorithm of Auer et al. [4]. For this modified algorithm we give an improved bound on the regret with respect to the optimal reward. While for the original UCB algorithm the regret in K-armed bandits after T trials is bounded by const · \( \frac{{K\log (T)}} {\Delta } \), where Δ measures the distance between a suboptimal arm and the optimal arm, for the modified UCB algorithm we show an upper bound on the regret of const · \( \frac{{K\log (T\Delta ^2 )}} {\Delta } \).


title: Revisiting the Page & Schroeder model: the good, the bad and the unknowns in the periodontal host response 40 years later

abstract: In their classic 1976 paper, Page & Schroeder described the histopathologic events and the types of myeloid cells and lymphocytes involved in the initiation and progression of inflammatory periodontal disease. The staging of periodontal disease pathogenesis as ‘initial’, ‘early’, ‘established’ and ‘advanced’ lesions productively guided subsequent research in the field and remains fundamentally valid. However, major advances regarding the cellular and molecular mechanisms underlying the induction, regulation and effector functions of immune and inflammatory responses necessitate a reassessment of their work and its integration with emerging new concepts. We now know that each type of leukocyte is actually represented by functionally distinct subsets with different, or even conflicting, roles in immunity and inflammation. Unexpectedly, neutrophils, traditionally regarded as merely antimicrobial effectors in acute conditions and protagonists of the ‘initial’ lesion, are currently appreciated for their functional versatility and critical roles in chronic inflammation. Moreover, an entirely new field of study, osteoimmunology, has emerged and sheds light on the impact of immunoinflammatory events on the skeletal system. These developments and the molecular dissection of crosstalk interactions between innate and adaptive leukocytes, as well as between the immune system and local homeostatic mechanisms, offer a more nuanced understanding of the host response in periodontitis, with profound implications for treatment. At the same time, deeper insights have generated new questions, many of which remain unanswered. In this review, 40 years after Page & Schroeder proposed their model, we summarize enduring and emerging advances in periodontal disease pathogenesis.


title: Warpage Suppression during FO-WLP Fabrication Process

abstract: Composing material combination of the re-distribution layer first type fan out wafer level package with various die occupancy ratio during the fabrication process on the support was studied by both of the making test vehicle and the numerical simulation. The investigated TV composed of glass support, temporary bonding adhesive, 1st re-distribution dielectric layer, Cu layer, 2nd re-distribution dielectric layer, Si die with die attached adhesive and molding compound. The fabrication was performed by the steps of applying all the materials serially and the final step of grinding down of the mold surface. The die occupation ratio in the TV become larger, the warpage of the TV decreased. The CTE of the support influenced the TV warpage significantly comparing with the CTEs of the EMC, the RDL dielectric layer and the TBA. From the investigated results the strategy of the appropriate selection of the materials to the die occupation factor was made to know. It suggested that the die occupation ratios were 21, 40 and 66%, using the support with the CTE of about 8, 10, 11 x10-6/°C and the EMC with the CTE of 8.4 x10-6/°C could make non warped TV, respectively. It was also suggested that the warpage could be controlled within 1mm during all the fabrication process steps by using the support with the CTE of 8.0 to 8.7 x10-6/°C in case of that the die occupation ratio was 40% and the CTE of EMC was 8.4 x10-6/°C.


Field-weakening Control Algorithm for Interior Permanent Magnet Synchronous Motor Based on Space-Vector Modulation Technique


title: Subpixel Photometric Stereo

abstract: Conventional photometric stereo recovers one normal direction per pixel of the input image. This fundamentally limits the scale of recovered geometry to the resolution of the input image, and cannot model surfaces with subpixel geometric structures. In this paper, we propose a method to recover subpixel surface geometry by studying the relationship between the subpixel geometry and the reflectance properties of a surface. We first describe a generalized physically-based reflectance model that relates the distribution of surface normals inside each pixel area to its reflectance function. The distribution of surface normals can be computed from the reflectance functions recorded in photometric stereo images. A convexity measure of subpixel geometry structure is also recovered at each pixel, through an analysis of the shadowing attenuation. Then, we use the recovered distribution of surface normals and the surface convexity to infer subpixel geometric structures on a surface of homogeneous material by spatially arranging the normals among pixels at a higher resolution than that of the input image. Finally, we optimize the arrangement of normals using a combination of belief propagation and MCMC based on a minimum description length criterion on 3D textons over the surface. The experiments demonstrate the validity of our approach and show superior geometric resolution for the recovered surfaces.


title: Mobile Augmented Reality

abstract: Developing augmented reality (AR) applications for mobile devices and outdoor environments has historically required a number of technical trade-offs related to tracking. One approach is to rely on computer vision which provides very accurate tracking, but can be brittle, and limits the generality of the application. Another approach is to rely on sensor-based tracking which enables widespread use, but at the cost of generally poor tracking performance. In this paper we present and evaluate a new approach, which we call Indirect AR, that enables perfect alignment of virtual content in a much greater number of application scenarios. To achieve this improved performance we replace the live camera view used in video see through AR with a previously captured panoramic image. By doing this we improve the perceived quality of the tracking while still maintaining a similar overall experience. There are some limitations of this technique, however, related to the use of panoramas. We evaluate these boundaries conditions on both a performance and experiential basis through two user studies. The result of these studies indicates that users preferred Indirect AR over traditional AR in most conditions, and when conditions do degrade to the point the experience changes, Indirect AR can still be a very useful tool in many outdoor application scenarios.


Development of resource management server for production IaaS services based on OpenStack


title: Tube Thoracostomy: Complications and Its Management

abstract: Background. Tube thoracostomy is widely used throughout the medical, surgical, and critical care specialities. It is generally used to drain pleural collections either as elective or emergency. Complications resulting from tube thoracostomy can occasionally be life threatening. Aim. To present an update on the complications and management of complications of tube thoracostomy. Methods. A review of the publications obtained from Medline search, medical libraries, and Google on tube thoracostomy and its complications was done. Results. Tube thoracostomy is a common surgical procedure which can be performed by either the blunt dissection technique or the trocar technique. Complication rates are increased by the trocar technique. These complications have been broadly classified as either technical or infective. Technical causes include tube malposition, blocked drain, chest drain dislodgement, reexpansion pulmonary edema, subcutaneous emphysema, nerve injuries, cardiac and vascular injuries, oesophageal injuries, residual/postextubation pneumothorax, fistulae, tumor recurrence at insertion site, herniation through the site of thoracostomy, chylothorax, and cardiac dysrhythmias. Infective complications include empyema and surgical site infection. Conclusion. Tube thoracostomy, though commonly performed is not without risk. Blunt dissection technique has lower risk of complications and is hence recommended.


An overview of classification algorithms for imbalanced datasets


title: Silk Server - Adding missing Links while consuming Linked Data

abstract: The Web of Linked Data is built upon the idea that data items on the Web are connected by RDF links. Sadly, the reality on the Web shows that Linked Data sources set some RDF links pointing at data items in related data sources, but they clearly do not set RDF links to all data sources that provide related data. In this paper, we present Silk Server, an identity resolution component, which can be used within Linked Data application architectures to augment Web data with additional RDF links. Silk Server is designed to be used with an incoming stream of RDF instances, produced for example by a Linked Data crawler. Silk Server matches the RDF descriptions of incoming instances against a local set of known instances and discovers missing links between them. Based on this assessment, an application can store data about newly discovered instances in its repository or fuse data that is already known about an entity with additional data about the entity from the Web. Afterwards, we report on the results of an experiment in which Silk Server was used to generate RDF links between authors and publications from the Semantic Web Dog Food Corpus and a stream of FOAF profiles that were crawled from the Web.


title: Interpretation of wavelet analysis and its application in partial discharge detection

abstract: The objective of the paper is to discuss a tool which is proving extremely efficient in partial discharge measurement studies. Though the technique itself is not new, its application to partial discharge studies is. It will be demonstrated in this paper that it has tremendous power and this accounts for its rapid growth as an application in this field. The paper begins with the description of the fundamentals of wavelet analysis, wavelet categories and the properties of the associated wavelet transforms. PD pulses as acquired from detectors composed of different detection circuits are investigated and numerically simulated, and a method on how to select optimally the wavelet corresponding to the representative forms of PD pulse is then presented. Finally, applications of wavelet analysis to partial discharge studies are explored. The paper demonstrates that the wavelet based denoising method proposed in the paper can be employed in separating PD pulses from electrical noise successfully and can be used in pulse propagation studies of partial discharge in distributed impedance plant to provide enhanced information and further infer the original site of the PD pulse.


title: Multivalued dependencies and a new normal form for relational databases

abstract: A new type of dependency, which includes the well-known functional dependencies as a special case, is defined for relational databases. By using this concept, a new (“fourth”) normal form for relation schemata is defined. This fourth normal form is strictly stronger than Codd's “improved third normal form” (or “Boyce-Codd normal form”). It is shown that every relation schema can be decomposed into a family of relation schemata in fourth normal form without loss of information (that is, the original relation can be obtained from the new relations by taking joins).


title: Learning Model-free Robot Control by a Monte Carlo EM

abstract: We address the problem of learning robot control by model-free reinforcement learning (RL). We adopt the probabilistic model for model-free RL of Vlassis and Toussaint (Proceedings of the international conference on machine learning, Montreal, Canada, 2009), and we propose a Monte Carlo EM algorithm (MCEM) for control learning that searches directly in the space of controller parameters using information obtained from randomly generated robot trajectories. MCEM is related to, and generalizes, the PoWER algorithm of Kober and Peters (Proceedings of the neural information processing systems, 2009). In the finite-horizon case MCEM reduces precisely to PoWER, but MCEM can also handle the discounted infinite-horizon case. An interesting result is that the infinite-horizon case can be viewed as a ‘randomized’ version of the finite-horizon case, in the sense that the length of each sampled trajectory is a random draw from an appropriately constructed geometric distribution. We provide some preliminary experiments demonstrating the effects of fixed (PoWER) vs randomized (MCEM) horizon length in two simulated and one real robot control tasks.


title: ITU Turkish NLP Web Service

abstract: We present a natural language processing (NLP) platform, namely the “ITU Turkish NLP Web Service” by the natural language processing group of Istanbul Technical University. The platform (available at tools.nlp.itu.edu.tr) operates as a SaaS (Software as a Service) and provides the researchers and the students the state of the art NLP tools in many layers: preprocessing, morphology, syntax and entity recognition. The users may communicate with the platform via three channels: 1. via a user friendly web interface, 2. by file uploads and 3. by using the provided Web APIs within their own codes for constructing higher level applications.


title: Question Answering Based On Semantic Structures

abstract: The ability to answer complex questions posed in Natural Language depends on (1) the depth of the available semantic representations and (2) the inferential mechanisms they support. In this paper we describe a QA architecture where questions are analyzed and candidate answers generated by 1) identifying predicate argument structures and semantic frames from the input and 2) performing structured probabilistic inference using the extracted relations in the context of a domain and scenario model. A novel aspect of our system is a scalable and expressive representation of actions and events based on Coordinated Probabilistic Relational Models (CPRM). In this paper we report on the ability of the implemented system to perform several forms of probabilistic and temporal inferences to extract answers to complex questions. The results indicate enhanced accuracy over current state-of-the-art Q/A systems.


title: Design of a passive, iso-elastic upper limb exoskeleton for gravity compensation

abstract: An additional mechanical mechanism for a passive parallelogram-based exoskeleton arm-support is presented. It consists of several levers and joints and an attached extension coil spring. The additional mechanism has two favourable features. On the one hand it exhibits an almost iso-elastic behaviour whereby the lifting force of the mechanism is constant for a wide working range. Secondly, the value of the supporting force can be varied by a simple linear movement of a supporting joint. Furthermore a standard tension spring can be used to gain the desired behavior. The additional mechanism is a 4-link mechanism affixed to one end of the spring within the parallelogram arm-support. It has several geometrical parameters which influence the overall behaviour. A standard optimisation routine with constraints on the parameters is used to find an optimal set of geometrical parameters. Based on the optimized geometrical parameters a prototype was constructed and tested. It is a lightweight wearable system, with a weight of 1.9 kg. Detailed experiments reveal a difference between measured and calculated forces. These variations can be explained by a 60 % higher pre load force of the tension spring and a geometrical offset in the construction.


title: Energy Efficient Task Assignment with Guaranteed Probability Satisfying Timing Constraints for Embedded Systems

abstract: The trade-off between system performance and energy efficiency (service time) is critical for battery-based embedded systems. Most of the previous work focuses on saving energy in a deterministic way by taking the average or worst scenario into account. However, such deterministic approaches usually are inappropriate in modeling energy consumption because of uncertainties in conditional instructions on processors and time-varying external environments (e.g., fluctuant network bandwidth and different user inputs). By adopting a probabilistic approach, this paper proposes a model and a set of algorithms to address the Processor and Voltage Assignment with Probability (PVAP) problem of data-dependent aperiodic tasks in real-time embedded systems, ensuring that all the tasks can be done under the time constraint with a guaranteed probability. We adopt a task DAG (Directed Acyclic Graph) to model the PVAP problem. We first use a processor scheduling algorithm to map the task DAG onto a set of voltage-variable processors, and then use our dynamic programming algorithm to assign a proper voltage to each task. Finally, to escape from local optima, a local search with restarts searches the optimal solution from candidate solutions by updating the objective function, until the stop criteria are reached or a time bound is elapsed. The experimental results demonstrate that for probability 1.0, our approach yields slightly better results than the well-known algorithms like ASAP/ALAP (As Soon As Possible/As Late As Possible) and ILP (Integer Linear Programming) with/without DVS (Dynamic Voltage Scaling). However, for probabilities 0.8 and 0.9, our approach significantly outperforms those algorithms (maximum improvement of 50.3 percent).


title: Bruxism and Temporal Bone Hypermobility in Patients with Multiple Sclerosis

abstract: In this study, the authors investigated the link between jaw clenching/bruxism and temporal bone movement associated with multiple sclerosis (MS). Twenty-one subjects participated in this study (10 patients with MS and 11 controls). To quantify the change in intracranial dimension between the endocranial surfaces of the temporal bones during jaw clenching, an ultrasonic pulsed phase locked loop (PPLL) device was used. A sustained jaw clenching force of 100 lbs was used to measure the mean change in acoustic pathlength (ΔL) as the measure of intracranial distance. In the control subjects the mean ΔL was 0.27 mm±0.24. In subjects with MS the mean ΔL was 1.71 mm±1.18 (p<0.001). The increase in magnitude of bi-temporal bone intracranial expansion was approximately six times greater in subjects with MS compared to controls. Therefore, jaw clenching/bruxism is associated with more marked displacement of the temporal bones and expansion of the cranial cavity in patients with MS than in control subjects.


title: Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France

abstract: The growing usage of social media by a wider audience of citizens sharply increases the possibility to investigate the web as a device to explore and track policy preferences. In the present paper we apply the recent method proposed in Hopkins and King (2010) to three different scenarios, by analyzing on one side the on-line popularity of Italian political leaders throughout 2011, and on the other the voting intention of French internet-users in both the 2012 Presidential ballot and in the subsequent Legislative election. Despite internet users are not necessarily representative of the whole population of country’s citizens, our analysis shows a remarkable ability of social-media to forecast electoral results as well as a noteworthy correlation between social-media and traditional mass surveys results. We also illustrate that the predictive ability of social-media analysis strengthens as the number of citizens’ expressing on-line their opinion increases, provided they act consistently on that (i.e. apart from high abstention rates).


title: Ideals of facial beauty.

abstract: Facial aesthetic procedures are central to cosmetic dermatology. Success depends not only on improving individual structures but also on establishing facial harmony. Several canons of aesthetic dimensions have been described, and these concepts can provide a useful basis for procedural planning. Here, we review aesthetic facial measurements and proportions as well as the variations that may occur in different ethnic groups and the changes that develop with age.


title: Right nulled GLR parsers

abstract: The right nulled generalized LR parsing algorithm is a new generalization of LR parsing which provides an elegant correction to, and extension of, Tomita's GLR methods whereby we extend the notion of a reduction in a shift-reduce parser to include right nulled items. The result is a parsing technique which runs in linear time on LR(1) grammars and whose performance degrades gracefully to a polynomial bound in the presence of nonLR(1) rules. Compared to other GLR-based techniques, our algorithm is simpler and faster.


title: Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition

abstract: Retinal image of surrounding objects varies tremendously due to the changes in position, size, pose, illumination condition, background context, occlusion, noise, and non-rigid deformations. But despite these huge variations, our visual system is able to invariantly recognize any object in just a fraction of a second. To date, various computational models have been proposed to mimic the hierarchical processing of the ventral visual pathway, with limited success. Here, we show that the association of both biologically inspired network architecture and learning rule significantly improves the models' performance when facing challenging invariant object recognition problems. Our model is an asynchronous feedforward spiking neural network. When the network is presented with natural images, the neurons in the entry layers detect edges, and the most activated ones fire first, while neurons in higher layers are equipped with spike timing-dependent plasticity. These neurons progressively become selective to intermediate complexity visual features appropriate for object categorization. The model is evaluated on 3D-Object and ETH-80 datasets which are two benchmarks for invariant object recognition, and is shown to outperform state-of-the-art models, including DeepConvNet and HMAX. This demonstrates its ability to accurately recognize different instances of multiple object classes even under various appearance conditions (different views, scales, tilts, and backgrounds). Several statistical analysis techniques are used to show that our model extracts class specific and highly informative features.


Fuzzy Logic and Its Application to Approximate Reasoning.


title: The facilitated processing of threatening faces: an ERP analysis.

abstract: Threatening, friendly, and neutral faces were presented to test the hypothesis of the facilitated perceptual processing of threatening faces. Dense sensor event-related brain potentials were measured while subjects viewed facial stimuli. Subjects had no explicit task for emotional categorization of the faces. Assessing early perceptual stimulus processing, threatening faces elicited an early posterior negativity compared with nonthreatening neutral or friendly expressions. Moreover, at later stages of stimulus processing, facial threat also elicited augmented late positive potentials relative to the other facial expressions, indicating the more elaborate perceptual analysis of these stimuli. Taken together, these data demonstrate the facilitated perceptual processing of threatening faces. Results are discussed within


title: Structural Basis for Neurofibromatosis Type 2

abstract: Neurofibromatosis type 2 (NF2) is a dominantly inherited disease associated with the central nervous system. The NF2 gene product merlin is a tumor suppressor, and its mutation or inactivation causes this disease. We report here the crystal structure of the merlin FERM domain containing a 22-residue -helical segment. The structure reveals that the merlin FERM domain consists of three subdomains displaying notable features of the electrostatic surface potentials, although the overall surface potentials similar to those of ezrin/radixin/moesin (ERM) proteins indicate electrostatic membrane association. The structure also is consistent with inactivation mechanisms caused by the pathogenic mutations associated with NF2.


title: Bandwidth Broadening of Dual-Slot Antenna Using Substrate Integrated Waveguide (SIW)

abstract: This letter presents a dual-slot antenna etched on a substrate integrated waveguide. This antenna is designed, fabricated, and measured to operate at X-band. It uses two unequal slots in order to increase the bandwidth. The measured results show a bandwidth of 8.5%, which is twice that of the standard dual-slot antennas. Attractive features including low cost, compact size, and planar form make this antenna easy to integrate within microwave planar circuits.


title: Hybrid neural network models for bankruptcy predictions

abstract: Abstract The objective of this paper is to develop the hybrid neural network models for bankruptcy prediction. The proposed hybrid neural network models are (1) a MDA-assisted neural network, (2) an ID3-assisted neural network, and (3) a SOFM(self organizing feature map)-assisted neural network. Both the MDA-assisted neural network and the 11)3-assisted neural network are the neural network models operating with the input variables selected by the MDA method and 1133 respectively. The SOFM-assisted neural network combines a backpropagation model (supervised learning) with a SOFM model (unsupervised learning). The performance of the hybrid neural network model is evaluated using MDA and ID3 as a benchmark. Empirical results using Korean bankruptcy data show that hybrid neural network models are very promising neural network models for bankruptcy prediction in terms of predictive accuracy and adaptability.


title: High-Performance Cloud Computing: A View of Scientific Applications

abstract: Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.


title: Personalization: a taxonomy

abstract: Personalization is here defined as a process of changing a system to increase its personal relevance. This may have a work or social motivation. A taxonomy of motivations is developed and illustrated by application to mobile phones and e-commerce Web pages.


title: Time-domain neural network: A 48.5 TSOp/s/W neuromorphic chip optimized for deep learning and CMOS technology

abstract: Demand for highly energy-efficient hardware for the inference computation of deep neural networks is increasing. Ultimately, fully spatially unrolled architecture where each distributed weight memory has a processing element (PE) for its exclusive use is the most energy-efficient solution because i) it can completely eliminate the energy-hungry data moving for weight fetching, and ii) PEs can consist only of combinational logics generally consuming less power than flip-flops. However, this strategy has not been applied because it requires a prohibitively huge amount of both area and hardware resources. We propose TDNN, which enables the fully spatially unrolled architecture by using 3D stacked ReRAM and the time-domain analog-digital mixed-signal processing that uses delay time as signal. In TDNN, a PE that performs synaptic operation is composed of only 12 logic transistors, which are equivalent to 3 gates. The proof-of-concept chip with SRAM instead of ReRAM shows unprecedentedly high energy efficiency of 48.2 TSop/s/W.


title: Sketch classification and classification-driven analysis using Fisher vectors

abstract: We introduce an approach for sketch classification based on Fisher vectors that significantly outperforms existing techniques. For the TU-Berlin sketch benchmark [Eitz et al. 2012a], our recognition rate is close to human performance on the same task. Motivated by these results, we propose a different benchmark for the evaluation of sketch classification algorithms. Our key idea is that the relevant aspect when recognizing a sketch is not the intention of the person who made the drawing, but the information that was effectively expressed. We modify the original benchmark to capture this concept more precisely and, as such, to provide a more adequate tool for the evaluation of sketch classification techniques. Finally, we perform a classification-driven analysis which is able to recover semantic aspects of the individual sketches, such as the quality of the drawing and the importance of each part of the sketch for the recognition.


title: The architecture of complex weighted networks

abstract: Networked structures arise in a wide array of different contexts such as technological and transportation infrastructures, social phenomena, and biological systems. These highly interconnected systems have recently been the focus of a great deal of attention that has uncovered and characterized their topological complexity. Along with a complex topological structure, real networks display a large heterogeneity in the capacity and intensity of the connections. These features, however, have mainly not been considered in past studies where links are usually represented as binary states, i.e., either present or absent. Here, we study the scientific collaboration network and the world-wide air-transportation network, which are representative examples of social and large infrastructure systems, respectively. In both cases it is possible to assign to each edge of the graph a weight proportional to the intensity or capacity of the connections among the various elements of the network. We define appropriate metrics combining weighted and topological observables that enable us to characterize the complex statistical properties and heterogeneity of the actual strength of edges and vertices. This information allows us to investigate the correlations among weighted quantities and the underlying topological structure of the network. These results provide a better description of the hierarchies and organizational principles at the basis of the architecture of weighted networks.


title: Transmit power optimization in cellular networks with nomadic base stations

abstract: The increasing demand for cellular network capacity can be mitigated through the installation of nomadic eNodeB, which serve a temporal increase of traffic volume in specific area. When nomadic cells are deployed, the transmission power of neighbour base stations needs to be optimised to limit the inter-cell interferences. The authors analyse the problem of neighbourhood selection for the optimisation, to define what part of the networks needs to be reconfigured when new base station is added. They evaluate the iterative approach, with increasing range of neighbouring cells being reconfigured and propose a novel, sampling-based local TX power reconfiguration method, which is evaluated by a numerical model in both regular (honeycomb) topology and in realistic topology reflecting locations of cells in a city. The analysis confirms that the proposed algorithm allows to select small subset of neighbouring cells to be reconfigured (in majority of the cases < 10 cells), and achieve similar efficacy as global optimisation, with total network throughput different by < 1 % comparing to the global optimisation.


title: The effects of Caffeoylserotonin on inhibition of melanogenesis through the downregulation of MITF via the reduction of intracellular cAMP and acceleration of ERK activation in B16 murine melanoma cells

abstract: In this study, we evaluated the anti-melanogenesis effects of Caffeoylserotonin (CaS) in B16 melanoma cells. Treatment with CaS reduced the melanin content and tyrosinase (TYR) activity in B16 melanoma cells in a dose-dependent manner. CaS inhibited the expression of melanogenesis-related proteins, including microphthalmia-associated transcription factor (MITF), TYR, and tyrosinase-related protein-1 (TRP-1), but not TRP-2. α-MSH is known to interact with melanocortin 1 receptor (MC1R) thus activating adenylyl cyclase and increasing intracellular cyclic AMP (cAMP) levels. Furthermore, cAMP activates extracellular signal-regulated kinase 2 (ERK2) via phosphorylation, which phosphorylates MITF, thereby targeting the transcription factor to proteasomes for degradation. The CaS reduced intracellular cAMP levels to unstimulated levels and activated ERK phosphorylation within 30 min. The ERK inhibitor PD98059 abrogated the suppressive effect of CaS on α-MSH-induced melanogenesis. Based on this study, the inhibitory effects of CaS on melanogenesis are derived from the downregulation of MITF signaling via the inhibition of intracellular cAMP levels, as well as acceleration of ERK activation. [BMB Reports 2012; 45(12): 724-729]


Award from the American Association of Colleges for Teacher Education. PREPARING FOR CULTURALLY RESPONSIVE TEACHING


title: A Novel Approach for Youtube Video Spam Detection using Markov Decision Process

abstract: Social networking websites have become an integral part of the day to day lives of people. People turn to social media for interacting with other people, sharing ideas, gaining knowledge, for entertainment and staying informed about the events happening in the rest of the world. Among these sites, YouTube has emerged as the most popular website for sharing and viewing video content. This popularity of YouTube has also attracted spammers, who upload videos with the sole purpose of polluting the system content and causing dissatisfaction among other viewers. These spam videos may be unrelated to their title or may contain pornographic content. Therefore, it is very important to find a way to detect these videos and report them before they are viewed by innocent users. In this paper, we propose a Markov Decision Process approach to model the problem of YouTube video spam detection. We analyze the accuracy of the policy returned by the model and compare it with the accuracy of other data mining algorithms that have been proposed for video spam detection. We find that the proposed model gives a superior performance than the other models.


title: Factorized Attention: Self-Attention with Linear Complexities.

abstract: Recent works have been applying self-attention to various fields in computer vision and natural language processing. However, the memory and computational demands of existing self-attention operations grow quadratically with the spatiotemporal size of the input. This prohibits the application of self-attention on large inputs, e.g., long sequences, high-definition images, or large videos. To remedy this, this paper proposes a novel factorized attention (FA) module, which achieves the same expressive power as previous approaches with substantially less memory and computational consumption. The resource-efficiency allows more widespread and flexible application of it. Empirical evaluations on object recognition demonstrate the effectiveness of these advantages. FA-augmented models achieved state-of-the-art performance for object detection and instance segmentation on MS-COCO. Further, the resource-efficiency of FA democratizes self-attention to fields where the prohibitively high costs currently prevent its application. The state-of-the-art result for stereo depth estimation on the Scene Flow dataset exemplifies this.


title: Learning Texture Manifolds with the Periodic Spatial GAN

abstract: This paper introduces a novel approach to texture synthesis based on generative adversarial networks (GAN) (Goodfellow et al., 2014). We extend the structure of the input noise distribution by constructing tensors with different types of dimensions. We call this technique Periodic Spatial GAN (PSGAN). The PSGAN has several novel abilities which surpass the current state of the art in texture synthesis. First, we can learn multiple textures from datasets of one or more complex large images. Second, we show that the image generation with PSGANs has properties of a texture manifold: we can smoothly interpolate between samples in the structured noise space and generate novel samples, which lie perceptually between the textures of the original dataset. In addition, we can also accurately learn periodical textures. We make multiple experiments which show that PSGANs can flexibly handle diverse texture and image data sources. Our method is highly scalable and it can generate output images of arbitrary large size.


title: A Collaborative Multiagent Taxi-Dispatch System

abstract: This paper presents a novel multiagent approach to automating taxi dispatch that services current bookings in a distributed fashion. The existing system in use by a taxi operator in Singapore and elsewhere, attempts to increase customer satisfaction locally, by sequentially dispatching nearby taxis to service customers. The proposed dispatch system attempts to increase customer satisfaction more globally, by concurrently dispatching multiple taxis to the same number of customers in the same geographical region, and vis-a-vis human driver satisfaction. To realize the system, a multiagent architecture is proposed, populated with software collaborative agents that can actively negotiate on behalf of taxi drivers in groups of size N for available customer bookings. Theoretically, an analysis of the boundary and optimal multiagent taxi-dispatch situations is presented along with a discussion of their implications. Experimentally, the operational efficiency of the existing and proposed dispatch systems was evaluated through computer simulations. The empirical results, obtained for a 1000-strong taxi fleet over a discrete range of N , show that the proposed system can dispatch taxis with reduction in customer waiting and empty taxi cruising times of up to 33.1% and 26.3%, respectively; and up to 41.8% and 41.2% reduction when a simple negotiation speedup heuristic was applied.


title: BER Performance Analysis of Multiuser Diversity with Antenna Selection in MRC MIMO Systems

abstract: In this paper, we present a performance analysis for the user scheduling for the multiuser MRC MIMO systems exploiting the user and antenna diversities. We consider two scheduling schemes including absolute SNR-based scheduling and normalized SNR-based scheduling schemes. We propose the utilization of an antenna selection scheme to overcome the drawback of channel hardening in multiuser MIMO systems and to improve the system performance. We derive new closedform expressions for the average bit error rate of the presented schemes for two scenarios: Heterogeneous (independent nonidentically distributed SNRs- i.ni.d.) and Homogeneous (independent identically distributed SNRs- i.i.d.) cases. Using mathematical analysis and numerical simulations, we compare the presented schemes.


title: Predicting the psychophysical similarity of faces and non-face complex shapes by image-based measures

abstract: Shape representation is accomplished by a series of cortical stages in which cells in the first stage (V1) have local receptive fields tuned to contrast at a particular scale and orientation, each well modeled as a Gabor filter. In succeeding stages, the representation becomes largely invariant to Gabor coding (Kobatake & Tanaka, 1994). Because of the non-Gabor tuning in these later stages, which must be engaged for a behavioral response (Tong, 2003; Tong et al., 1998), a V1-based measure of shape similarity based on Gabor filtering would not be expected to be highly correlated with human performance when discriminating complex shapes (faces and teeth-like blobs) that differ metrically on a two-choice, match-to-sample task. Here we show that human performance is highly correlated with Gabor-based image measures (Gabor simple and complex cells), with values often in the mid 0.90s, even without discounting the variability in the speed and accuracy of performance not associated with the similarity of the distractors. This high correlation is generally maintained through the stages of HMAX, a model that builds upon the Gabor metric and develops units for complex features and larger receptive fields. This is the first report of the psychophysical similarity of complex shapes being predictable from a biologically motivated, physical measure of similarity. As accurate as these measures were for accounting for metric variation, a simple demonstration showed that all were insensitive to viewpoint invariant (nonaccidental) differences in shape.


title: Evaluation of OCPP and IEC 61850 for smart charging electric vehicles

abstract: Interoperability of charging infrastructures is a key success factor for E-Mobility. Standards like ISO/IEC 15118 and IEC 61851-1 are developed to ensure base level interoperability of front-end communication and signaling processes for smart charging between electric vehicles and charge spots. With the Open Charge Point Protocol (OCPP) a forum of European industry members also moves towards a common back-end protocol for charge spots intending to reduce and secure overall investment costs. However, in the current form OCPP lacks means for enabling grid services based on smart charging. In this paper the authors provide a review of today's state of the art in ISO/IEC standardization of the V2G Interface and furthermore detail how OCPP could leverage existing standardization efforts for grid automation from IEC 61850 in order to overcome its shortcomings.


title: Consensus as a Nash Equilibrium of a Dynamic Game

abstract: Consensus formation in a social network is modeled by a dynamic game of a prescribed duration played by members of the network. Each member independently minimizes a cost function that represents his/her motive. An integral cost function penalizes a member's differences of opinion from the others as well as from his/her own initial opinion, weighted by influence and stubbornness parameters. Each member uses its rate of change of opinion as a control input. This defines a dynamic non-cooperative game that turns out to have a unique Nash equilibrium. Analytic explicit expressions are derived for the opinion trajectory of each member for two representative cases obtained by suitable assumptions on the graph topology of the network. These trajectories are then examined under different assumptions on the relative sizes of the influence and stubbornness parameters that appear in the cost functions.


A Survey to Design Privacy Preserving Protocol Using Chaos Cryptography


title: Asia-Pacific Society for Computers in Education MOBILE DEVICES FOR LANGUAGE LEARNING: MULTIMEDIA APPROACHES

abstract: This paper examines the use of mobile devices for language learning. In particular we consider how different multimedia and interface modalities can be used to facilitate mobile language learning. The use of multimedia is considered within the context of Second Language Acquisition (SLA) and Computer Assisted Language Learning (CALL) theories. In particular, we examine the Interaction Framework, Schemata Theory, SLA Universal Grammar related theories and Multimedia Learning Theory. Two case studies serve to illuminate: the audio-based training of a system called MAC and the image-based training of another called PhotoStudy. We also suggest evaluation techniques that should be used to test the application of these theories to Mobile Assisted Language Learning (MALL).


title: Decentralization in Bitcoin and Ethereum Networks

abstract: Blockchain-based cryptocurrencies have demonstrated how to securely implement traditionally centralized systems, such as currencies, in a decentralized fashion. However, there have been few measurement studies on the level of decentralization they achieve in practice. We present a measurement study on various decentralization metrics of two of the leading cryptocurrencies with the largest market capitalization and user base, Bitcoin and Ethereum. We investigate the extent of decentralization by measuring the network resources of nodes and the interconnection among them, the protocol requirements affecting the operation of nodes, and the robustness of the two systems against attacks. In particular, we adapted existing internet measurement techniques and used the Falcon Relay Network as a novel measurement tool to obtain our data. We discovered that neither Bitcoin nor Ethereum has strictly better properties than the other. We also provide concrete suggestions for improving both systems.


title: Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks

abstract: Separation of speech embedded in non-stationary interference is a challenging problem that has recently seen dramatic improvements using deep network-based methods. Previous work has shown that estimating a masking function to be applied to the noisy spectrum is a viable approach that can be improved by using a signal-approximation based objective function. Better modeling of dynamics through deep recurrent networks has also been shown to improve performance. Here we pursue both of these directions. We develop a phase-sensitive objective function based on the signal-to-noise ratio (SNR) of the reconstructed signal, and show that in experiments it yields uniformly better results in terms of signal-to-distortion ratio (SDR). We also investigate improvements to the modeling of dynamics, using bidirectional recurrent networks, as well as by incorporating speech recognition outputs in the form of alignment vectors concatenated with the spectral input features. Both methods yield further improvements, pointing to tighter integration of recognition with separation as a promising future direction.


title: Networked Control System: Overview and Research Trends

abstract: Networked control systems (NCSs) have been one of the main research focuses in academia as well as in industry for many decades and have become a multidisciplinary area. With these growing research trends, it is important to consolidate the latest knowledge and information to keep up with the research needs. In this paper, the NCS and its different forms are introduced and discussed. The beginning of this paper discusses the history and evolution of NCSs. The next part of this paper focuses on different fields and research arenas such as networking technology, network delay, network resource allocation, scheduling, network security in real-time NCSs, integration of components on a network, fault tolerance, etc. A brief literature survey and possible future direction concerning each topic is included.


title: Exploiting Discourse Relations for Sentiment Analysis

abstract: The overall sentiment of a text is critically affected by its discourse structure . By splitting a text into text spans with different discourse relations, we automatically train the weig hts of different relations in accordance with their importance, and then make use of disco urse structure knowledge to improve sentiment classification. In this paper, we utilize explicit connectives to predict discourse relations, and then propose several methods to incorporate discourse relation knowledge to the task of sentiment analysis. All our methods integrating discourse relations perform better than the baseline methods, validating the effectiveness of us ing discourse relations in Chinese sentiment analysis. We also automatically find out the most influential discourse relations and connectives in sentiment analysis.


title: Education as action/the adventure of education: thinking with Arendt and Whitehead

abstract: Understanding and action are central themes in Hannah Arendt's thought and an idea that runs throughout her work is that whenever human beings act, they start processes. It is in this light that she saw education as a process whose aim is to make human beings feel at home in the world. Given the centrality of process in understanding action, early on in her work, Arendt reflected and drew upon the ideas of Alfred Whitehead, the philosopher of process. Education in his thought is an art and an adventure whose object should be to enable students to grasp the process of life itself and imagine different worlds. In this light, universities are crucial in creating conditions of possibility for imaginative learning and intellectual adventures. Taking action, process, imagination and adventure as my central ideas, in this paper, I make connections between Arendt and Whitehead in an attempt to think about education within and beyond ‘dark times’.


title: OmniKinect: real-time dense volumetric data acquisition and applications

abstract: Real-time three-dimensional acquisition of real-world scenes has many important applications in computer graphics, computer vision and human-computer interaction. Inexpensive depth sensors such as the Microsoft Kinect allow to leverage the development of such applications. However, this technology is still relatively recent, and no detailed studies on its scalability to dense and view-independent acquisition have been reported. This paper addresses the question of what can be done with a larger number of Kinects used simultaneously. We describe an interference-reducing physical setup, a calibration procedure and an extension to the KinectFusion algorithm, which allows to produce high quality volumetric reconstructions from multiple Kinects whilst overcoming systematic errors in the depth measurements. We also report on enhancing image based visual hull rendering by depth measurements, and compare the results to KinectFusion. Our system provides practical insight into achievable spatial and radial range and into bandwidth requirements for depth data acquisition. Finally, we present a number of practical applications of our system.


Easy-first Coreference Resolution


title: Redundancy in Perceptual and Linguistic Experience: Comparing Feature-Based and Distributional Models of Semantic Representation

abstract: Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We compare the representations of three feature-based and nine distributional models using a semantic clustering task. Several distributional models demonstrated semantic clustering comparable with clustering-based on feature-based representations. Furthermore, when trained on child-directed speech, the same distributional models perform as well as sensorimotor-based feature representations of children’s lexical semantic knowledge. These results suggest that, to a large extent, information relevant for extracting semantic categories is redundantly coded in perceptual and linguistic experience. Detailed analyses of the semantic clusters of the feature-based and distributional models also reveal that the models make use of complementary cues to semantic organization from the two data streams. Rather than conceptualizing feature-based and distributional models as competing theories, we argue that future focus should be on understanding the cognitive mechanisms humans use to integrate the two sources.


title: Metrics that matter

abstract: Within NASA, there is an increasing awareness that software is of growing importance to the success of missions. Much data has been collected, and many theories have been advanced on how to reduce or eliminate errors in code. However, learning requires experience. We document a new NASA initiative to build a centralized repository of software defect data; in particular, we document one specific case study on software metrics. Software metrics are used as a basis for prediction of errors in code modules, but there are many different metrics available. McCabe is one of the more popular tools used to produce metrics, but, other metrics can be more significant.


title: Turing Tests with Turing machines

abstract: Comparative tests work by finding the difference (or the absence of difference) between a reference subject and an evaluee. The Turing Test, in its standard interpretation, takes (a subset of) the human species as a reference. Motivated by recent findings and developments in the area of machine intelligence evaluation, we discuss what it would be like to have a Turing Test where the reference and the interrogator subjects are replaced by Turing Machines. This question sets the focus on several issues that are usually disregarded when dealing with the Turing Test, such as the degree of intelligence of reference and interrogator, the role of imitation (and not only prediction) in intelligence, its view from the perspective of game theory and others. Around these issues, this paper finally brings the Turing Test to the realm of Turing machines.


title: Creating GUI Testing Tools Using Accessibility Technologies

abstract: Since manual black-box testing of GUI-based APplications(GAPs) is tedious and laborious, and existing toolsdo not fully address different aspects of the testing process, test engineers create custom testing tools to automate the testing process. These tools interact with GAPs by performing actions on their GUI objects. An extra effort that test engineers put in writing test tools is paid off when these tools are run repeatedly on different GAPs. Unfortunately, creating custom GUI testing tools is a laborious and intellectually intensive process, during which test engineers use platform-specific libraries and techniques. As a result, these tools are expensive, difficult to maintain and evolve, and they often run only on specific platforms.We offer a universal approach for creating custom testingGUI tools. This approach is lightweight, portable, nonintrusive, universal, and cheap, and it combines a nonstandard use of accessibility technologies for accessing and controlling GAPs in a uniform way with a visualization mechanism that enables test personnel to interact with GUI objects by performing point-and-click, drag-and-drop operations on GAPs. We describe how we used this approach to create various GUI testing tools, delve into technical features of accessibility technologies, and review our experience with this approach.


title: Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT?

abstract: The phrase-based and N-gram-based SMT frameworks complement each other. While the former is better able to memorize, the latter provides a more principled model that captures dependencies across phrasal boundaries. Some work has been done to combine insights from these two frameworks. A recent successful attempt showed the advantage of using phrasebased search on top of an N-gram-based model. We probe this question in the reverse direction by investigating whether integrating N-gram-based translation and reordering models into a phrase-based decoder helps overcome the problematic phrasal independence assumption. A large scale evaluation over 8 language pairs shows that performance does significantly improve.


title: wARna — Mobile-based augmented reality colouring book

abstract: Usage of Augmented Reality (AR) in educational environment is not an alien thing recently, especially when considering the multimodality and interactivity nature of some AR application that proves to be a lot more immersive and engaging learning than its traditional counterpart. Regardless, another aspect of educational AR, or any AR app, that should not be overlooked is the robustness as well as the availability of the app itself. In this paper, we are discussing a type of mixed-reality book experience that augments a colouring book with user-manipulated three-dimensional (3D) contents in a mobile-based environment. We explore tracking approaches in AR for its efficiency as well as computational load that is most suitable for mobile environment, and we also investigate texture extraction and mapping method for 3D content. Our system is then based on a marker-based tracking system using fiducial marker and simple image processing technique for the texture extraction and mapping part.


title: GraphGen: Exploring Interesting Graphs in Relational Data

abstract: Analyzing interconnection structures among the data through the use of graph algorithms and graph analytics has been shown to provide tremendous value in many application domains. However, graphs are not the primary choice for how most data is currently stored, and users who want to employ graph analytics are forced to extract data from their data stores, construct the requisite graphs, and then use a specialized engine to write and execute their graph analysis tasks. This cumbersome and costly process not only raises barriers in using graph analytics, but also makes it hard to explore and identify hidden or implicit graphs in the data. Here we demonstrate a system, called GraphGen, that enables users to declaratively specify graph extraction tasks over relational databases, visually explore the extracted graphs, and write and execute graph algorithms over them, either directly or using existing graph libraries like the widely used NetworkX Python library. We also demonstrate how unifying the extraction tasks and the graph algorithms enables significant optimizations that would not be possible otherwise.


title: Dissociation of algorithmic and heuristic processes in language comprehension: Evidence from aphasia

abstract: Abstract Three groups of aphasic patients, Broca's, Conduction, and Wernicke's, and a nonaphasic patients control group were tested for comprehension of object-relative center-embedded sentences. The sentences were of three types: sentences in which semantic constraints between words allowed the subjects to assign a correct semantic reading of the sentence without decoding the syntax, sentences in which semantic constraints were relaxed and for which a correct reading was only possible with knowledge of syntactic relationships among words, and sentences which described highly improbable events. The subjects' task was to choose which of two pictures captured the meaning expressed in the sentence. Broca's and Conduction aphasics performed near perfectly on sentences where they could use semantic information. Their performance dropped to chance when they had to use syntactic information. These results support a neuropsychological dissociation of heuristic and algorithmic processes based primarily, though not exclusively, on semantic and syntactic information, respectively.


title: Benefits Assessment and Realization - in the digital business context

abstract: In the ever-changing digital business environment, organizations need to be able to adapt quickly and become more agile. The IT Capability Maturity Framework (IT-CMF) is an IT management framework comprised of 36 key areas or Critical Capabilities (CCs) [2], one of which is Benefits Assessment and Realization (BAR). This position paper presents an overview of key insights, to provide context and understanding of the value of organizations having an effective benefits assessment and realization capability in the digital business context, as derived from academic and practitioner literature.


title: Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

abstract: The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series.


title: A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

abstract: In this paper, we employ probabilistic neural network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.


title: RDF Query Answering Using Apache Spark: Review and Assessment

abstract: The explosion of the web and the abundance of linked data demand for effective and efficient methods for storage, management and querying. More specifically, the ever-increasing size and number of RDF data collections raises the need for efficient query answering, and dictates the usage of distributed data management systems for effectively partitioning and querying them. To this direction, Apache Spark is one of the most active big-data approaches, with more and more systems adopting it, for efficient, distributed data management. The purpose of this paper is to provide an overview of the existing works dealing with efficient query answering, in the area of RDF data, using Apache Spark. We discuss on the characteristics and the key dimension of such systems, we describe novel ideas in the area, and the corresponding drawbacks, and provide directions for future work.


On Training Classifiers for Linking Event Templates.


title: A Reconfigurable Circularly Polarized Microstrip Antenna With a Slotted Ground Plane

abstract: This letter describes a square patch antenna with a switchable circular polarization (CP) sense. The proposed antenna has four L-shaped slots on the ground plane, and the CP radiation can be generated by current perturbation due to the slotted ground plane. Because the CP sense of the proposed antenna is altered by the current path that is redirected with switched p-i-n diodes on the slots, the CP direction of the proposed antenna can be simply switched between the right-handed and left-handed CPs. As the slot and bias circuits are not placed on the patch side, the proposed antenna has a simple structure and can radiate a CP wave without altering the main beam direction. The experimental results show that antennas have excellent switchable radiation performance at 2.4 GHz.


title: Phishing through social bots on Twitter

abstract: This work investigates how social bots can phish employees of organizations, and thus endanger corporate network security. Current literature mostly focuses on traditional phishing methods (through e-mail, phone calls, and USB sticks). We address the serious organizational threats and security risks caused by phishing through online social media, specifically through Twitter. This paper first provides a review of current work. It then describes our experimental development, in which we created and deployed eight social bots on Twitter, each associated with one specific subject. For a period of four weeks, each bot published tweets about its subject and followed people with similar interests. In the final two weeks, our experiment showed that 437 unique users could have been phished, 33 of which visited our website through the network of an organization. Without revealing any sensitive or real data, the paper analyses some findings of this experiment and addresses further plans for research in this area.