title: Multi-objective model predictive control for grid-tied 15-level packed U cells inverter

abstract: This paper presents a multi-objective Model Predictive Control (MPC) for grid-tied 4-cell 15-level Packed U Cells (PUC) inverter. This challenging topology is characterized by high power quality with reduced number of components compared to conventional multilevel inverters. Compared to traditional PI controller, MPC is attracting more interest due to its good dynamic response and high accuracy of reference tracking, through the minimization of a flexible user-defined cost function. For the presented PUC topology, the grid current should be jointly controlled with the capacitors' voltages for ensuring proper operation of the inverter, which leads to an additional requirement of pre-charge circuits for the capacitors in case of using PI current controller (or using additional PI controllers). The proposed MPC achieves grid current injection, low current THD, unity PF, while balancing the capacitors' voltages.


title: DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess

abstract: We present an end-to-end learning method for chess, relying on deep neural networks. Without any a priori knowledge, in particular without any knowledge regarding the rules of chess, a deep neural network is trained using a combination of unsupervised pretraining and supervised training. The unsupervised training extracts high level features from a given position, and the supervised training learns to compare two chess positions and select the more favorable one. The training relies entirely on datasets of several million chess games, and no further domain specific knowledge is incorporated.


title: On Optimal Parameters for Ant Colony Optimization Algorithms

abstract: Ant Colony Optimization (ACO) is a metaheuristic introduced by Dorigo et al. [9] which uses ideas from nature to find solutions to instances of the Travelling Salesman Problem (TSP) and other combinatorial optimisation problems. In this paper we analyse the parameter settings of the ACO algorithm. These determine the behaviour of each ant and are critical for fast convergence to near optimal solutions of a given problem instance. We classify TSP instances using three measures of complexity and uniformity. We describe experimental work that attempts to correlate ‘types’ of TSP problems with parameter settings for fast convergence. We found these optimal parameter settings to be highly problemspecific and dependent on the required accuracy of the solution. This inspired us to explore techniques for automatically learning the optimal parameters for a given TSP instance. We devised and implemented a hybrid ACO algorithm, similar to the one independently developed in [16], which uses a genetic algorithm in the early stages to ‘breed’ a population of ants possessing near optimal behavioural parameter settings for a given problem. This hybrid algorithm converges rapidly for a wide range of problems when given a population of ants with diverse behavioural parameter settings.


title: A Security Framework for a Drone Delivery Service

abstract: Delivery drones are unmanned aerial vehicles (UAV) utilized to transport packages, food, medicine, or other goods. With high demand for a prompt and efficient delivery, a drone delivery system can be an effective solution for timely deliveries and especially for emergency management. However, current delivery drone systems lack crucial security functions. Drones may have to operate in unsupervised hostile areas, and therefore be vulnerable to physical capture in addition to conventional cyber attacks. A captured drone can be analyzed by a white-box attack model in which the attacker has full control over the execution environment of cryptographic modules in static and dynamic methods including all side-channel information. In this paper, we propose and evaluate a security framework which utilizes white-box cryptography in order to protect critical data and cryptographic keys in delivery drones from white-box attacks. The experimental results show that the proposed framework is cost effective in terms of resource usage and thus is suitable even for resource-limited UAV.


title: A case of partial trisomy of chromosome 8p associated with autism.

abstract: We report on a case of a 6-year-old female with partial trisomy 8p(21–23) associated with autism, mild dysmorphic features, and moderate learning disability. Although mental retardation is a common finding in patients with mosaic trisomy 8 or partial trisomy of various regions of chromosome 8, only two cases associated with autism have been reported so far. Also, in our case clinical manifestations were mild compared to other patients with duplication of the same region of chromosome 8. Although there has been no strong evidence for linkage on chromosome 8 in any of the genome-wide linkage studies so far, the possibility that this segment includes genes involved in the etiology of autism should be further explored.


title: Twitter Reveals: Using Twitter Analytics to Predict Public Protests

abstract: The right to protest is perceived as one of the primary civil rights. Citizens participate in mass demonstrations to express themselves and exercise their democratic rights. However, because of the large number of participants, protests may lead to violence and destruction, and hence can be costly. Thus, it is important to predict such demonstrations in advance to safeguard against such damages. Recent research has shown that about 75 percent of protests that are regarded as legal, are planned in advance. Twitter, the prominent micro-blogging website, has been used as a tool by protestors for planning, organizing, and announcing many of the recent protests worldwide such as those that led to the Arab Spring, Britain riots, and those against Mr. Trump after the presidential election in the U.S. In this paper, we aim to predict protests by means of machine learning algorithms. In particular, we consider the case of protests against the then-president-elect Mr. Trump after the results of the presidential election were announced in November 2016. We first identify the hashtags calling for demonstration from Trending Topics on Twitter, and download the corresponding tweets. We then apply four machine learning algorithms to make predictions. Our findings indicate that Twitter can be used as a powerful tool for predicting future protests with an average prediction accuracy of over 75 percent (up to 100 percent). We further validate our model by predicting the protests held in the U.S. airports after President Trump's executive order banning citizens of seven Muslim countries from entering the U.S. An important contribution of our study is the inclusion of event specific features for prediction purposes which helps to achieve high levels of accuracy.


title: The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program “CASCADE”

abstract: BACKGROUND ::: Understanding the cancer genome is seen as a key step in improving outcomes for cancer patients. Genomic assays are emerging as a possible avenue to personalised medicine in breast cancer. However, evolution of the cancer genome during the natural history of breast cancer is largely unknown, as is the profile of disease at death. We sought to study in detail these aspects of advanced breast cancers that have resulted in lethal disease. ::: ::: ::: METHODS AND FINDINGS ::: Three patients with oestrogen-receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer and one patient with triple negative breast cancer underwent rapid autopsy as part of an institutional prospective community-based rapid autopsy program (CASCADE). Cases represented a range of management problems in breast cancer, including late relapse after early stage disease, de novo metastatic disease, discordant disease response, and disease refractory to treatment. Between 5 and 12 metastatic sites were collected at autopsy together with available primary tumours and longitudinal metastatic biopsies taken during life. Samples underwent paired tumour-normal whole exome sequencing and single nucleotide polymorphism (SNP) arrays. Subclonal architectures were inferred by jointly analysing all samples from each patient. Mutations were validated using high depth amplicon sequencing. Between cases, there were significant differences in mutational burden, driver mutations, mutational processes, and copy number variation. Within each case, we found dramatic heterogeneity in subclonal structure from primary to metastatic disease and between metastatic sites, such that no single lesion captured the breadth of disease. Metastatic cross-seeding was found in each case, and treatment drove subclonal diversification. Subclones displayed parallel evolution of treatment resistance in some cases and apparent augmentation of key oncogenic drivers as an alternative resistance mechanism. We also observed the role of mutational processes in subclonal evolution. Limitations of this study include the potential for bias introduced by joint analysis of formalin-fixed archival specimens with fresh specimens and the difficulties in resolving subclones with whole exome sequencing. Other alterations that could define subclones such as structural variants or epigenetic modifications were not assessed. ::: ::: ::: CONCLUSIONS ::: This study highlights various mechanisms that shape the genome of metastatic breast cancer and the value of studying advanced disease in detail. Treatment drives significant genomic heterogeneity in breast cancers which has implications for disease monitoring and treatment selection in the personalised medicine paradigm.


title: SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

abstract: In this paper we propose a novel model for unconditional audio generation task that generates one audio sample at a time. We show that our model which profits from combining memory-less modules, namely autoregressive multilayer perceptron, and stateful recurrent neural networks in a hierarchical structure is de facto powerful to capture the underlying sources of variations in temporal domain for very long time on three datasets of different nature. Human evaluation on the generated samples indicate that our model is preferred over competing models. We also show how each component of the model contributes to the exhibited performance.


title: SMART: A Web-Based, Ontology-Driven, Semantic Web Query Answering Application

abstract: SMART (Semantic web information Management with automated Reasoning Tool) is an open-source project, which aims to provide intuitive tools for life scientists for represent, integrate, manage and query heterogeneous and distributed biological knowledge. SMART was designed with interoperability and extensibility in mind and uses AJAX, SVG and JSF technologies, RDF, OWL, SPARQL semantic web languages, triple stores (i.e. Jena) and DL reasoners (i.e. Pellet) for the automated reasoning. Features include semantic query composition and validation using DL reasoners, a graphical representation of the query, a mapping of DL queries to SPARQL, and the retrieval of pre-computed inferences from an RDF triple store. With a use case scenario, we illustrate how a biological scientist can intuitively query the yeast knowledge base and navigate the results. Continued development of this web-based resource for the biological semantic web will enable new information retrieval opportunities for the life sciences.


title: 1kW S-band Solid State radar amplifier

abstract: A GaN based highly integrated Solid State Power Amplifier (SSPA) is developed for S-band radar applications. The power amplifier delivers more than 1 KW within a 400 MHz bandwidth with 55 dB gain and a typical 34% PAE. The SSPA includes a negative voltage supply, sequential bias circuit, and temperature sensor circuit. Its compact and lightweight design is suitable for many radar applications.


title: SR-based binary classification in credit scoring

abstract: Credit scoring is an important process in every financial institution and bank. Its high accuracy in classifying customers helps decrease the credit risk and increase reliability and profit. In this paper, we propose a binary classification approach that can classify customers who apply for loans. A statistical technique called Stepwise Regression (SR) is used as a pre-process to select important features for the classifier. Artificial Neural Network (ANN) is the classification model type that has been chosen. The concept of confusion matrix combined with business rules has been used for obtaining an appropriate classification model. Based on the experimentation, we find that our method, SR-based Binary Classification, has the accuracy rate of 95.65%, which is higher than using ANN alone (91.30%).


title: Inertial hidden markov models: modeling change in multivariate time series

abstract: Faced with the problem of characterizing systematic changes in multivariate time series in an unsupervised manner, we derive and test two methods of regularizing hidden Markov models for this task. Regularization on state transitions provides smooth transitioning among states, such that the sequences are split into broad, contiguous segments. Our methods are compared with a recent hierarchical Dirichlet process hidden Markov model (HDP-HMM) and a baseline standard hidden Markov model, of which the former suffers from poor performance on moderate-dimensional data and sensitivity to parameter settings, while the latter suffers from rapid state transitioning, over-segmentation and poor performance on a segmentation task involving human activity accelerometer data from the UCI Repository. The regularized methods developed here are able to perfectly characterize change of behavior in the human activity data for roughly half of the real-data test cases, with accuracy of 94% and low variation of information. In contrast to the HDP-HMM, our methods provide simple, drop-in replacements for standard hidden Markov model update rules, allowing standard expectation maximization (EM) algorithms to be used for learning.


title: Domain Adaptation through Synthesis for Unsupervised Person Re-identification

abstract: Drastic variations in illumination across surveillance cameras make the person re-identification problem extremely challenging. Current large scale re-identification datasets have a significant number of training subjects, but lack diversity in lighting conditions. As a result, a trained model requires fine-tuning to become effective under an unseen illumination condition. To alleviate this problem, we introduce a new synthetic dataset that contains hundreds of illumination conditions. Specifically, we use 100 virtual humans illuminated with multiple HDR environment maps which accurately model realistic indoor and outdoor lighting. To achieve better accuracy in unseen illumination conditions we propose a novel domain adaptation technique that takes advantage of our synthetic data and performs fine-tuning in a completely unsupervised way. Our approach yields significantly higher accuracy than semi-supervised and unsupervised state-of-the-art methods, and is very competitive with supervised techniques.


title: FPGA-Based Remote Power Side-Channel Attacks

abstract: The rapid adoption of heterogeneous computing has driven the integration of Field Programmable Gate Arrays (FPGAs) into cloud datacenters and flexible System-on-Chips (SoCs). This paper shows that the integrated FPGA introduces a new security vulnerability by enabling software-based power side-channel attacks without physical proximity to a target system. We first demonstrate that an on-chip power monitor can be built on a modern FPGA using ring oscillators (ROs), and characterize its ability to observe the power consumption of other modules on the FPGA or the SoC. Then, we show that the RO-based FPGA power monitor can be used for a successful power analysis attack on an RSA cryptomodule on the same FPGA. Additionally, we show that the FPGA-based power monitor can observe the power consumption of a CPU on the same SoC, and demonstrate that the FPGA-to-CPU power side-channel attack can break timing-channel protection for a RSA program running on a CPU. This work introduces and demonstrates remote power side-channel attacks using an FPGA, showing that the common assumption that power side-channel attacks require specialized equipment and physical access to the victim hardware is not true for systems with an integrated FPGA.


title: A Survey of the Application of Machine Learning in Decision Support Systems

abstract: Machine learning is a useful technology for decision support systems and assumes greater importance in research and practice. Whilst much of the work focuses technical implementations and the adaption of machine learning algorithms to application domains, the factors of machine learning design affecting the usefulness of decision support are still understudied. To enhance the understanding of machine learning and its use in decision support systems, we report the results of our content analysis of design-oriented research published between 1994 and 2013 in major Information Systems outlets. The findings suggest that the usefulness of machine learning for supporting decision-makers is dependent on the task, the phase of decision-making, and the applied technologies. We also report about the advantages and limitations of prior research, the applied evaluation methods and implications for future decision support research. Our findings suggest that future decision support research should shed more light on organizational and people-related evaluation criteria.


title: Code Generation from Specifications in Higher-Order Logic

abstract: Code generation denotes the translation of an abstract formal specification into a corresponding executable program. The aim of this thesis is to present a code generator framework for the interactive proof assistant Isabelle/HOL, an implementation of higher-order logic. It comes with two substantial novelties: a general but lightweight concept for datatype abstraction and support for Isabelle type classes in the manner of Haskell type classes. Code can be generated for functional programming languages supporting pattern matching, concrete instances for SML, OCaml and Haskell are presented.


title: Scaling description of generalization with number of parameters in deep learning

abstract: We provide a description for the evolution of the generalization performance of fixed-depth fully-connected deep neural networks, as a function of their number of parameters $N$. As $N$ gets large, we observe that increasing $N$ at fixed depth reduces the fluctuations of the output function $f_N$ induced by initial conditions, with $\|f_N-{\bar f}_N\|\sim N^{-1/4}$ where ${\bar f}_N$ denotes an average over initial conditions. We explain this asymptotic behavior in terms of the fluctuations of the so-called Neural Tangent Kernel that controls the dynamics of the output function. For the task of classification, we predict these fluctuations to increase the true test error $\epsilon$ as $\epsilon_{N}-\epsilon_{\infty}\sim N^{-1/2} + \mathcal{O}( N^{-3/4})$. This prediction is consistent with our empirical results on the MNIST dataset, and it explains in a concrete case the puzzling observation that the predictive power of deep networks improves as the number of fitting parameters grows. For smaller $N$, this asymptotic description breaks down at a so-called jamming transition which takes place at a critical $N=N^*$, below which the training error is non-zero. In the absence of regularization, we observe an apparent divergence $\|f_N\| \sim (N-N^*)^{-\alpha}$ and provide a simple argument suggesting $\alpha=1$, consistent with empirical observations. This result leads to a plausible explanation for the cusp in test error known to occur at $N^*$. Overall, our analysis suggests that once models are averaged, the optimal model complexity is reached just beyond the point where the data can be perfectly fitted, a result of practical importance that needs to be tested in a wide range of architectures and dataset.


Semantic Space: A Semantic Web-Based Infrastructure for Smart Spaces


title: Detecting Personality Traces in Users’ Social Activity

abstract: The effect that social media have in our lives nowadays is apparent. Many studies focused on how the differences we hold as people due to our personality, reflect our activities online. In this work we aim to exploit reports of previous work to implicitly build a personality model of Facebook users, based on their Facebook activity. An initial evaluation study shows that using Facebook activity data, we can extract information on user personality and at the same time points in further improvements necessary for more accurate personality prediction.


title: Using Information Content to Evaluate Semantic Similarity in a Taxonomy

abstract: This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r = 0.90 for human subjects performing the same task), and significantly better than the traditional edge counting approach (r = 0.66).


title: 3D human face modeling for facial animation using regional adaptation

abstract: This paper proposes an efficient method to reconstruct human face models from 3D face scans using an adaptation algorithm based on regional information. The resulting model can represent individual facial shapes with various expressions, establishing dense correspondences across the whole facial expression sequences. We first initialize the global shape of the face model by implementing RBF interpolation. An efficient vertex mapping is then developed to reconstruct the surface details. The new 3D face are finally created by remapping the scan texture to the facial surface. Experiments show that our method can create effective 3D face models for continuous and realistic 3D facial animation.


title: Structured Training for Neural Network Transition-Based Parsing

abstract: We present structured perceptron training for neural network transition-based dependency parsing. We learn the neural network representation using a gold corpus augmented by a large number of automatically parsed sentences. Given this fixed network representation, we learn a final layer using the structured perceptron with beam-search decoding. On the Penn Treebank, our parser reaches 94.26% unlabeled and 92.41% labeled attachment accuracy, which to our knowledge is the best accuracy on Stanford Dependencies to date. We also provide indepth ablative analysis to determine which aspects of our model provide the largest gains in accuracy.


title: When polyhedral transformations meet SIMD code generation

abstract: Data locality and parallelism are critical optimization objectives for performance on modern multi-core machines. Both coarse-grain parallelism (e.g., multi-core) and fine-grain parallelism (e.g., vector SIMD) must be effectively exploited, but despite decades of progress at both ends, current compiler optimization schemes that attempt to address data locality and both kinds of parallelism often fail at one of the three objectives. We address this problem by proposing a 3-step framework, which aims for integrated data locality, multi-core parallelism and SIMD execution of programs. We define the concept of vectorizable codelets, with properties tailored to achieve effective SIMD code generation for the codelets. We leverage the power of a modern high-level transformation framework to restructure a program to expose good ISA-independent vectorizable codelets, exploiting multi-dimensional data reuse. Then, we generate ISA-specific customized code for the codelets, using a collection of lower-level SIMD-focused optimizations. We demonstrate our approach on a collection of numerical kernels that we automatically tile, parallelize and vectorize, exhibiting significant performance improvements over existing compilers.


title: ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection

abstract: As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers' attention because of its descriptive power and clear structure. It localizes the objects and captures their interactions with a subject-predicate-object triplet, e.g. person-ride-horse. In this paper, the visual relationship is considered as a phrase with three components. So we formulate the visual relationship detection as three inter-connected recognition problems and propose a Visual Phrase reasoning Convolutional Neural Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a Visual Phrase Reasoning Structure (VPRS) to set up the connection among the relationship components and help the model consider the three problems jointly. Corresponding non-maximum suppression method and model training strategy are also proposed. Experimental results show that our ViP-CNN outperforms the state-of-art method both in speed and accuracy. We further pretrain our model on our cleansed Visual Genome Relationship dataset, which is found to perform better than the pretraining on the ImageNet for this task.


title: A reinvestigation of non-disjunction resulting in 47, XXY males of paternal origin

abstract: We have used polymorphisms within the Xp/Yp pseudoautosomal region (PAR 1) to determine the frequency and location of recombination in 80 paternally derived 47, XXY males. Of 64 informative results, there were 10 single cross-overs, one double cross-over and 53 without a cross-over. Therefore 2/3 of 47, XXY males of paternal origin result from meiosis in which the X and Y chromosomes fail to recombine. This failure was not associated with the presence of an increase in recombination in the smaller Xq/Yq pseudoautosomal region (PAR 2) or with the presence of microdeletions within PAR 1.


title: 100 lines of code for shape-based object localization

abstract: We introduce a simple and effective concept for localizing objects in densely cluttered edge images based on shape information. The shape information is characterized by a binary template of the object's contour, provided to search for object instances in the image. We adopt a segment-based search strategy, in which the template is divided into a set of segments. In this work, we propose our own segment representation that we call one-pixel segment (OPS), in which each pixel in the template is treated as a separate segment. This is done to achieve high flexibility that is required to account for intra-class variations. OPS representation can also handle scale changes effectively. A dynamic programming algorithm uses the OPS representation to realize the search process, enabling a detailed localization of the object boundaries in the image. The concept's simplicity is reflected in the ease of implementation, as the paper's title suggests. The algorithm works directly with very noisy edge images extracted using the Canny edge detector, without the need for any preprocessing or learning steps. We present our experiments and show that our results outperform those of very powerful, state-of-the-art algorithms. HighlightsNew segment-based representation that we call OPS.Used to achieve high flexibility that is required to account for intra-class variations.OPS representation is fed to a dynamic programming algorithm for robust detection.Very competitive localization results under extensive noise and clutter.The ability to accurately localize detailed object boundaries.


title: A smartphone based technique to monitor driving behavior using DTW and crowdsensing

abstract: Safety issues while driving in smart cities are considered to be top-notch priority in contrast to traveling. Todays fast paced society, often leads to accidents. In order to reduce the road accidents, one key area of research is monitoring the driving behavior of drivers. Understanding the driver behavior is an essential component in Intelligent Driver Assistance Systems. One of potential cause of traffic fatalities is aggressive driving behavior. However, drivers are not fully aware of their aggressive actions. So, in order to increase awareness and to promote driver safety, a novel system has been proposed. In this work, we focus on DTW based event detection technique, which have not been researched in motion sensors based time series data to a great extent. Our motivation is to improve the classification accuracy to detect sudden braking and aggressive driving behaviors using sensory data collected from smartphone. A very significant feature of DTW is to be able to automatically cope with time deformations and different speeds associated with time-dependent data which makes it suitable for our chosen application where data might get affected due to factors such as: high variability in road and vehicle conditions, heterogeneous smartphone sensors, etc. Our technique is novel as it uses fusion of sensors to enhance detection accuracy. The experimental results show that proposed algorithm outperforms the existing machine learning and threshold-based techniques with 100% detection rate of braking events and 97% & 86.67% detection rate of normal left & right turns and aggressive left & right turns respectively.


title: The Programming Language Aspects of ThingLab, a Constraint-Oriented Simulation Laboratory

abstract: The programming language aspects of a graphic simulation laboratory named ThingLab are presented. The design and implementation of ThingLab are extensions to Smalltalk. In ThingLab, constraints are used to specify the relations that must hold among the parts of the simulation. The system is object-oriented and employs inheritance and part-whole hierarchies to describe the structure of a simulation. An interactive, graphic user interface is provided that allows the user to view and edit a simulation.


title: The fundamental matrix: Theory, algorithms, and stability analysis

abstract: In this paper we analyze in some detail the geometry of a pair of cameras, i.e., a stereo rig. Contrarily to what has been done in the past and is still done currently, for example in stereo or motion analysis, we do not assume that the intrinsic parameters of the cameras are known (coordinates of the principal points, pixels aspect ratio and focal lengths). This is important for two reasons. First, it is more realistic in applications where these parameters may vary according to the task (active vision). Second, the general case considered here, captures all the relevant information that is necessary for establishing correspondences between two pairs of images. This information is fundamentally projective and is hidden in a confusing manner in the commonlyused formalism of the Essential matrix introduced by Longuet-Higgins (1981). This paper clarifies the projective nature of the correspondence problem in stereo and shows that the epipolar geometry can be summarized in one 3 x 3 matrix of rank 2 which we propose to call the Fundamental matrix. After this theoretical analysis, we embark on the task of estimating the Fundamental matrix from point corre- spondences, a task which is of practical importance. We analyze theoretically, and compare experimentally using synthetic and real data, several methods of estimation. The problem of the stability of the estimation is studied from two complementary viewpoints. First we show that there is an interesting relationship between the Fundamental matrix and three-dimensional planes which induce homographies between the images and create unstabilities in the estimation procedures. Second, we point to a deep relation between the unstability of the estimation procedure and the presence in the scene of so-called critical surfaces which have been studied in the context of motion analysis. Finally we conclude by stressing the fact that we believe that the Fundamental matrix will play a crucial role in future applications of three-dimensional Computer Vision by greatly increasing its versatility, robustness and hence applicability to real difficult problems.


title: Generalized Fisher Score for Feature Selection

abstract: Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature independently according to their scores under the Fisher criterion, which leads to a suboptimal subset of features. In this paper, we present a generalized Fisher score to jointly select features. It aims at finding an subset of features, which maximize the lower bound of traditional Fisher score. The resulting feature selection problem is a mixed integer programming, which can be reformulated as a quadratically constrained linear programming (QCLP). It is solved by cutting plane algorithm, in each iteration of which a multiple kernel learning problem is solved alternatively by multivariate ridge regression and projected gradient descent. Experiments on benchmark data sets indicate that the proposed method outperforms Fisher score as well as many other state-of-the-art feature selection methods.


title: Hands-on chemistry activities with real-life applications

abstract: ABOUT THE AUTHORS. ACKNOWLEDGMENTS. ABOUT THIS RESOURCE. UNIT ONE: MEASUREMENT. 1.1 Language of Chemistry. 1.1.1 Nomenclature: Elements. 1.1.2 Nomenclature: Compounds. 1.1.3 Chemical Terminology: Understanding the Language of Chemistry. For the Teacher (Discussion and Answer Keys for Activities 1.1.1 p 1.1.3). Applications to Everyday Life. 1.2 Units of Measurement. 1.2.1 The Importance of Units. 1.2.2 Units in Chemistry. 1.2.3 Problem Solving (Dimensional Analysis). For the Teacher (Discussion and Answer Keys for Activities 1.2.1 p 1.2.3). Applications to Everyday Life. 1.3 Measurement. 1.3.1 Length: Estimating and Measuring. 1.3.2 Area: Estimating and Measuring. 1.3.3 Volume: Estimating and Measuring. 1.3.4 Measuring Mass 1.3.5 Density of Solids. 1.3.6 Density of Liquids. 1.3.7 Density of Solutions. 1.3.8 Density of Solutions: The Mystery of the Strait of Gibraltar. 1.3.9 Density of Gases. For the Teacher (Discussion and Answer Keys for Activities 1.3.1 p 1.3.9). Applications to Everyday Life. UNIT TWO: MATTER. 2.1 Atomic Structure 2.1.1 Atoms are Mostly Empty Space. 2.1.2 Atomic Spectra. 2.1.3 Wave Characteristics. 2.1.4 Quantum Numbers and Electron Orbitals. 2.1.5 Electron Configuration. For the Teacher (Discussion and Answer Keys for Activities 2.1.1 p 2.1.5). Applications to Everyday Life. 2.2 The Periodic Law. 2.2.1 Finding the "Missing Elements". 2.2.2 Designing Your Own Periodic Table. 2.2.3 Family Characteristics. 2.2.4 Trends Within Families. For the Teacher (Discussion and Answer Keys for Activities 2.2.1 p 2.2.4). Applications to Everyday Life. 2.3 Diffusion and Osmosis. 2.3.1 Brownian Motion. 2.3.2 Diffusion of Gases. 2.3.3 The Effect of Temperature on Diffusion. 2.3.4 The Effect of Molecular Size on Diffusion Rate. 2.3.5 Osmosis. 2.3.6 Osmotic Pressure. 2.3.7 Dialysis. For the Teacher (Discussion and Answer Keys for Activities 2.3.1 p 2.3.7). Applications to Everyday Life. 2.4 Changes of State. 2.4.1 Relative Volumes of Solid, Liquid and Gas Phase. 2.4.2 Heats of Fusion and Vaporization. 2.4.3 Boiling: The Influence of Pressure. 2.4.4 Distillation. 2.4.5 Sublimation of Carbon Dioxide. 2.4.6 Triple Point Phase Diagram of Carbon Dioxide. 2.4.7 Regelation: Phase Diagram of Water. For the Teacher (Discussion and Answer Keys for Activities 2.4.1 p 2.4.7). Applications to Everyday Life. 2.5 Classification of Matter. 2.5.1 Physical and Chemical Changes. 2.5.2 Separation of Pure Substances and Mixtures: Chromatography. 2.5.3 Identification of Physical and Chemical Changes. 2.5.4 Elements and Compounds. 2.5.5 Extensive and Intensive Physical Properties. 2.5.6 Identifying Unknowns by Physical and Chemical Properties. For the Teacher (Discussion and Answer Keys for Activities 2.5.1 p 2.5.6). Applications to Everyday Life. UNIT THREE: CHEMICAL AND NUCLEAR REACTIONS. 3.1 Chemical Bonding 3.1.1 Law of Definite Proportions. 3.1.2 Bond Formation. 3.1.3 Nonpolar and Polar Covalent Bonds. 3.1.4 Covalent and Ionic Bonds. 3.1.5 Metallic Bonding. 3.1.6 Intermolecular Forces. For the Teacher (Discussion and Answer Keys for Activities 3.1.1 p 3.1.6). Applications to Everyday Life. 3.2 Types of Chemical Reactions. 3.2.1 Composition (Synthesis, Combination) Reactions. 3.2.2 Decomposition Reactions. 3.2.3 Single Displacement (Single Replacement) Reactions. 3.2.4 Double Replacement (Ion Combining) Reactions. 3.2.5 Combustion Reactions. 3.2.6 Addition Reactions. For the Teacher (Discussion and Answer Keys for Activities 3.2.1 p 3.2.6). Applications to Everyday Life. 3.3 Oxidation Reduction (Redox) Reactions. 3.3.1 Oxidation States. 3.3.2 Oxidation and Reduction. 3.3.3 Dramatic Oxidation Reactions. 3.3.4 Balancing Oxidation Reduction Reactions. For the Teacher (Discussion and Answer Keys for Activities 3.3.1 p 3.3.4). Applications to Everyday Life. 3.4 Electrochemistry. 3.4.1 Electrode Potentials. 3.4.2 Energy Transformation. 3.4.3 Voltaic Cell. 3.4.4 Batteries. 3.4.5 Electrolysis. 3.4.6 Electroplating. For the Teacher (Discussion and Answer Keys for Activities 3.4.1 p 3.4.6). Applications to Everyday Life. 3.5 Polymers. 3.5.1 Polymerization. 3.5.2 Cross Linking Polymers. 3.5.3 Uses of Polymers. 3.5.4 Recycling Plastic Polymers. For the Teacher (Discussion and Answer Keys for Activities 3.5.1 p 3.5.4). Applications to Everyday Life. 3.6 Nuclear Chemistry. 3.6.1 Detecting Radiation with a Cloud Chamber. 3.6.2 Detecting Radiation with an Electroscope. 3.6.3 Detecting Radiation with a Geiger Counter. 3.6.4 Modeling Nuclear Reactions. For the Teacher (Discussion and Answer Keys for Activities 3.6.1 p 3.6.4). Applications to Everyday Life. UNIT FOUR: THERMODYNAMICS AND KINETICS. 4.1 Thermochemistry 4.1.1 Endothermic Reactions. 4.1.2 Heats of Dilution and Solution. 4.1.3 Chemical Hand Warmer. 4.1.4 Heat of Reaction. 4.1.5 Caloric Content of Foods. 4.1.6 Entropy. 4.1.7 Entropy Driven Reactions. 4.1.8 Entropy and Enthalpy. For the Teacher (Discussion and Answer Keys for Activities 4.1.1 p 4.1.8). Applications to Everyday Life. 4.2 Heat Transfer. 4.2.1 Heat Conduction in Metals. 4.2.2 Conduction and Combustion. 4.2.3 Conduction: Davy Safety Lamp. 4.2.4 Convection in Gases. 4.2.5 Convection in Liquids. 4.2.6 Radiation. For the Teacher (Discussion and Answer Keys for Activities 4.2.1 p 4.2.6) Applications to Everyday Life. 4.3 Chemical Kinetics. 4.3.1 The Effect of Concentration on Reaction Rate. 4.3.2 The Effect of Temperature on Reaction Rate. 4.3.3 The Effect of Temperature and Concentration on Reaction Rate. 4.3.4 Catalysts, Reaction Rates and Activation Energy. 4.3.5 Enzymes. 4.3.6 Oscillating Chemical Reactions. For the Teacher (Discussion and Answer Keys for Activities 4.3.1 p 4.3.6). Applications to Everyday Life. 4.4 Chemical Equilibrium. 4.4.1 Models of Dynamic Equilibrium. 4.4.2 Disruption of Equilibrium. 4.4.3 The Effect of Concentration on Equilibrium. 4.4.4 The Effect of Pressure on Equilibrium. 4.4.5 The Effect of Temperature on Equilibrium. For the Teacher (Discussion and Answer Keys for Activities 4.4.1 p 4.4.5). Applications to Everyday Life. UNIT FIVE: GASES, LIQUIDS, SOLIDS, AND MIXTURES. 5.1.1 Properties of Gases. 5.1.2 Pressure Volume Relationship of Gases (Boyle s Law). 5.1.3 Temperature Volume Relationship of Gases (Charles s Law). 5.1.4 Hot Air Balloons: An Application of Charles s Law. 5.1.5 Temperature Pressure Relationships of Gases (Gay Lussac s Law). 5.1.6 Avogadro s Principle. 5.1.7 Measuring Atmospheric Pressure. For the Teacher (Discussion and Answer Keys for Activities 5.1.1 p 5.1.7). Applications to Everyday Life. 5.2 Colloids and Suspensions. 5.2.1 Tyndall Effect. 5.2.2 Classification of Dispersions: Solutions, Colloids, and Suspensions. 5.2.3 Brownian Motion in Colloids. 5.2.4 Making Colloids. For the Teacher (Discussion and Answer Keys for Activities 5.2.1 p 5.2.4). Applications to Everyday Life. 5.3 Solutes, Solvents, Solutions. 5.3.1 Solubility (Solute/Solvent Interaction). 5.3.2 Surface Area and Dissolution Rate. 5.3.3 Temperature and Dissolution Rate. 5.3.4 Enthalpy of Solution. 5.3.5 Solubility of Gases in Liquids. For the Teacher (Discussion and. Applications to Everyday Life. 5.4 Colligative Properties. 5.4.1 Raoult s Law. 5.4.2 Freezing Point Depression. 5.4.3 Boiling Point Elevation. 5.4.4 Osmotic Pressure. For the Teacher (Discussion and Answer Keys for Activities 5.4.1 p 5.4.4). Applications to Everyday Life. 5.5 Acids and Bases. 5.5.1 Acids. 5.5.2 Bases. 5.5.3 pH Scale. 5.5.4 Acid/Base Concentration, Equivalence, and Strength. 5.5.5 Acid Rain. For the Teacher (Discussion and Answer Keys for Activities 5.5.1 p 5.5.5) Applications to Everyday Life. 5.6 Acid and Base Reactions. 5.6.1 Neutralization. 5.6.2 Titration. 5.6.3 Buffering. 5.6.4 Soil pH. 5.6.5 pH Control of Respiration. For the Teacher (Discussion and Answer Keys for Activities 5.6.1 p 5.6.5). Applications to Everyday Life. 5.7 Crystals and Crystallization. 5.7.1 Supersaturation and Crystallization. 5.7.2 Crystallization by Evaporation. 5.7.3 Growing Crystals Crystal Shapes. 5.7.4 Metallic Crystals. 5.7.5 Silicate Crystals. 5.7.6 Stalactites and Stalagmites. 5.7.7 Rate of Crystallization and Crystal Size. For the Teacher (Discussion and Answer Keys for Activities 5.7.1 p 5.7.7). Applications to Everyday Life. APPENDIX. A.1 Units, Quantities, and Conversions. 1.1 Physical Quantities and Their SI Units. 1.2 Metric System Prefixes. 1.3 SI and Customary Units and Conversions. 1.4 Common Conversions. 1.5 Units of Pressure. 1.6 Vapor Pressure of Water. A.2 Language of Chemistry. 2.1 Writing Style Guidelines. 2.2 Root Words Used Frequently in Chemistry. 2.3 Etymology of the Names of the Elements. 2.4 Balancing Equations. 3.1 Sample Safety Contract. 3.2 Chemical Storage. 3.3 Disposa.


title: Super Resolving of the Depth Map for 3D Reconstruction of Underwater Terrain Using Kinect

abstract: In recent years, sonar has been widely used for restoring the underwater terrain. Sonar imaging has the benefits such as long-range photographing, robust for turbidity water. However, it is not suitable for short-range imaging. Meanwhile, it also cannot meet the need of mining machine. Therefore, it is important to develop a 3D reconstruction method for short-range imaging. In this paper, we propose a Kinect-based underwater 3D image reconstruction method. To overcome the drawbacks of low accuracy of depth maps, we propose a novel super-resolution (SR) method, which uses the underwater dark channel prior dehazing, weight guided image SR, and inpainting. The proposed method considered the influence of mud sediments in water, it performs better than the traditional methods. The experimental results demonstrated that, after inpainting, dehazing and the super-resolution, it can obtain high accuracy depth maps.


title: On the Semantification of 5-Star Technical Documentation

abstract: Technical documentation is a special purpose content de- scribing machines and plants with high complexity. The documentation covers operation, maintenance and repair of the technical artifacts. The high complexity of the machines yields a voluminous documentation, where it increasingly becomes dicult to nd the relevant information for a given problem. The paper discusses the use of semantic technologies to organize the documentation on a syntactic and semantic level. Also, a scheme for the assessment of the maturity of existing documentation is proposed, that simplies the application of semantic technologies.


title: A Monitoring System for Temperature Data Coming from IoT Agents

abstract: The paper presents a system for collecting temperature data from IoT agents. In the article the architecture of the agent and the BigData type system for analysing recorded data are described. The functional and performance tests carried out on the system make it possible to assess its functionality.


title: PAM decomposition of M-ary multi-h CPM

abstract: It is known that any multilevel continuous phase-modulated (CPM) signal with a single modulation index can be exactly represented by a sum of pulse-amplitude modulated (PAM) waveforms. In this paper, we show how multi-h CPM signals can also be represented in this manner. The decomposition is presented in general terms as a function of the alphabet size, modulation indexes, and phase pulse of the CPM scheme. The number of pulses required to exactly construct the signal is shown to increase over that previously given for single-h schemes; this increase is in proportion to the number of modulation indexes. We propose an approximation which significantly reduces the number of signal pulses and which minimizes the mean-squared error for an arbitrary set of modulation indexes. We show that this approximation can have two objectives: 1) to reduce the number of pulses in the same manner as has been proposed for single-h schemes; and/or 2) to reduce the number of multi-h pulses; we also show the conditions where this latter objective is most practical. We compare this minimum mean-squared error approximation with another method which was recently proposed for CPM. We also give numerical results on detection performance which demonstrate the practicality of the proposed approximation.


title: A highly efficient and linear 15 GHz GaN power amplifier design for 5G communications

abstract: A highly efficient two-stage 15 GHz fully-integrated GaN power amplifier (PA) designed for 5G communication is reported. SPICE simulations show this two-stage PA achieves an output 1 dB compression P OUT, 1dB = 32.2 dBm with 28.2 dB gain and 30.0% PAE (power-added efficiency) for CW operation at 15 GHz. Its PAE reaches 38.7% at P OUT = 34.0 dBm with a gain of 22.0 dB. Simulations also suggest that dynamic supply modulation from 28 V to 10 V may modestly improve the PA's efficiency at power back-off. When the PA is driven with 5/10/20 MHz LTE 16QAM modulated signals, the simulated output spectra and adjacent channel leakage ratio (ACLR) at P OUT, 1dB = 32.2 dBm passed the LTE spectrum emission mask (SEM) without any predistortion (P in = 4dBm), and the wider modulated signal bandwidth from 5 MHz to 20 MHz does not noticeably worsen the PA linearity. State-of-the-art literature survey suggests this highly efficient and linear GaN PA may be quite attractive for 5G PA applications at 15 GHz.


title: An IoT-Aware Architecture for Smart Healthcare Systems

abstract: Over the last few years, the convincing forward steps in the development of Internet of Things (IoT)-enabling solutions are spurring the advent of novel and fascinating applications. Among others, mainly radio frequency identification (RFID), wireless sensor network (WSN), and smart mobile technologies are leading this evolutionary trend. In the wake of this tendency, this paper proposes a novel, IoT-aware, smart architecture for automatic monitoring and tracking of patients, personnel, and biomedical devices within hospitals and nursing institutes. Staying true to the IoT vision, we propose a smart hospital system (SHS), which relies on different, yet complementary, technologies, specifically RFID, WSN, and smart mobile, interoperating with each other through a Constrained Application Protocol (CoAP)/IPv6 over low-power wireless personal area network (6LoWPAN)/representational state transfer (REST) network infrastructure. The SHS is able to collect, in real time, both environmental conditions and patients’ physiological parameters via an ultra-low-power hybrid sensing network (HSN) composed of 6LoWPAN nodes integrating UHF RFID functionalities. Sensed data are delivered to a control center where an advanced monitoring application (MA) makes them easily accessible by both local and remote users via a REST web service. The simple proof of concept implemented to validate the proposed SHS has highlighted a number of key capabilities and aspects of novelty, which represent a significant step forward compared to the actual state of the art.


title: Better region proposals for pedestrian detection with R-CNN

abstract: Region-based convolutional neural networks (R-CNNs) have achieved great success in object detection recently. These deep models depend on region proposal algorithms to hypothesize object locations. In this paper, we combine a special region proposal algorithm with R-CNN, and apply it to pedestrian detection. The special algorithm is used to generate region proposals only for pedestrian class. It is different from the popular region proposal algorithm selective search that detects generic object locations. The experimental results prove that region proposals generated by our method are more applicable than selective search for pedestrian detection. Our method performs faster training and testing than the deep model based on, and it achieves competitive performance compared to the state-of-the-arts in pedestrian detection.


title: Towards using blockchain technology for eHealth data access management

abstract: eHealth is a technology that is growing in importance over time, varying from remote access to Medical Records, such as Electronic Health Records (EHR), or Electronic Medical Records (EMR), to real-time data exchange from different on-body sensors coming from different patients. With this huge amount of critical data being exchanged, problems and challenges arise. Privacy and confidentiality of this critical medical data are of high concern to the patients and authorized persons to use this data. On the other hand, scalability and interoperability are also important problems that should be considered in the final solution. This paper illustrates the specific problems and highlights the benefits of the blockchain technology for the deployment of a secure and a scalable solution for medical data exchange in order to have the best performance possible.


Double Forward Propagation for Memorized Batch Normalization


title: oPass: A User Authentication Protocol Resistant to Password Stealing and Password Reuse Attacks

abstract: Text password is the most popular form of user authentication on websites due to its convenience and simplicity. However, users' passwords are prone to be stolen and compromised under different threats and vulnerabilities. Firstly, users often select weak passwords and reuse the same passwords across different websites. Routinely reusing passwords causes a domino effect; when an adversary compromises one password, she will exploit it to gain access to more websites. Second, typing passwords into untrusted computers suffers password thief threat. An adversary can launch several password stealing attacks to snatch passwords, such as phishing, keyloggers and malware. In this paper, we design a user authentication protocol named oPass which leverages a user's cellphone and short message service to thwart password stealing and password reuse attacks. oPass only requires each participating website possesses a unique phone number, and involves a telecommunication service provider in registration and recovery phases. Through oPass, users only need to remember a long-term password for login on all websites. After evaluating the oPass prototype, we believe oPass is efficient and affordable compared with the conventional web authentication mechanisms.


title: Model-Based Fault Detection and Identification for Switching Power Converters

abstract: We present the analysis, design, and experimental validation of a model-based fault detection and identification (FDI) method for switching power converters using a model-based state estimator approach. The proposed FDI approach is general in that it can be used to detect and identify arbitrary faults in components and sensors in a broad class of switching power converters. The FDI approach is experimentally demonstrated on a nanogrid prototype with a 380-V dc distribution bus. The nanogrid consists of four different switching power converters, including a buck converter, an interleaved boost converter, a single-phase rectifier, and a three-phase inverter. We construct a library of fault signatures for possible component and sensor faults in all four converters. The FDI algorithm successfully achieves fault detection in under 400 $\mu$ s and fault identification in under 10 ms for faults in each converter. The proposed FDI approach enables a flexible and scalable solution for improving fault tolerance and awareness in power electronics systems.


title: Modelling of bike steered by CMG

abstract: In this work authors present the idea of stability of single track vehicles. This is based on the use of Control Moment Gyro (CMG) as an actuator that introduces a stabilizing torque. The basic issue to achieve this goal is to develop the mathematical model of the system: bicycle plus CMG — this problem is the main subject matter of this paper. Additionally, an influence of perturbation of some parameters on system stability has been analysed.


title: Examining the relationship between violent video games, presence, and aggression

abstract: The level of presence, or immersion, a person feels with media influences the effect media has on them. This project examines both the causes and consequences of presence in the context of violent video game play. In a between subjects design, 227 participants were randomly assigned to play either a violent or a non violent video game. Causal modeling techniques revealed two separate paths to presence. First, individual differences predicted levels of presence: men felt more presence while playing the video game, as did those who play video games more frequently. Secondly, those who perceived the game to be more violent felt more presence. Those who felt more presence, felt more resentment, were more verbally aggressive, and that led to increased physically aggressive intentions.


title: Positive psychology progress: Empirical validation of interventions

abstract: Positive psychology has flourished in the last 5 years. The authors review recent developments in the field, including books, meetings, courses, and conferences. They also discuss the newly created classification of character strengths and virtues, a positive complement to the various editions of the Diagnostic and Statistical Manual of Mental Disorders (e. g., American Psychiatric Association, 1994), and present some cross-cultural findings that suggest a surprising ubiquity of strengths and virtues. Finally, the authors focus on psychological interventions that increase individual happiness. In a 6-group, random-assignment, placebo-controlled Internet study, the authors tested 5 purported happiness interventions and 1 plausible control exercise. They found that 3 of the interventions lastingly increased happiness and decreased depressive symptoms. Positive interventions can supplement traditional interventions that relieve suffering and may someday be the practical legacy of positive psychology.


title: The input/output complexity of sorting and related problems

abstract: We provide tight upper and lower bounds, up to a constant factor, for the number of inputs and outputs (I/OS) between internal memory and secondary storage required for five sorting-related problems: sorting, the fast Fourier transform (FFT), permutation networks, permuting, and matrix transposition. The bounds hold both in the worst case and in the average case, and in several situations the constant factors match. Secondary storage is modeled as a magnetic disk capable of transferring P blocks each containing B records in a single time unit; the records in each block must be input from or output to B contiguous locations on the disk. We give two optimal algorithms for the problems, which are variants of merge sorting and distribution sorting. In particular we show for P = 1 that the standard merge sorting algorithm is an optimal external sorting method, up to a constant factor in the number of I/Os. Our sorting algorithms use the same number of I/Os as does the permutation phase of key sorting, except when the internal memory size is extremely small, thus affirming the popular adage that key sorting is not faster. We also give a simpler and more direct derivation of Hong and Kung's lower bound for the FFT for the special case B = P = O(1).


title: Sustainable IT services: Assessing the impact of green computing practices

abstract: Green computing refers to the practice of using computing resources more efficiently while maintaining or increasing overall performance. Sustainable IT services require the integration of green computing practices such as power management, virtualization, improving cooling technology, recycling, electronic waste disposal, and optimization of the IT infrastructure to meet sustainability requirements. Recent studies have shown that costs of power utilized by IT departments can approach 50% of the overall energy costs for an organization. While there is an expectation that green IT should lower costs and the firm's impact on the environment, there has been far less attention directed at understanding the strategic benefits of sustainable IT services in terms of the creation of customer value, business value and societal value. This paper provides a review of the literature on sustainable IT, key areas of focus, and identifies a core set of principles to guide sustainable IT service design.


title: Towards a general purpose architecture for UI generation

abstract: Many software projects spend a significant proportion of their time developing the User Interface (UI), therefore any degree of automation in this area has clear benefits. Such automation is difficult due principally to the diversity of architectures, platforms and development environments. Attempts to automate UI generation to date have contained restrictions which did not accommodate this diversity, leading to a lack of wide industry adoption or standardisation. The authors set out to understand and address these restrictions. We studied the issues of UI generation (especially duplication) in practice, using action research cycles guided by interviews, adoption studies and close collaboration with industry practitioners. In addressing the issues raised in our investigation, we identified five key characteristics any UI generation technique would need before it should expect wide adoption or standardisation. These can be summarised as: inspecting existing, heterogeneous back-end architectures; appreciating different practices in applying inspection results; recognising multiple, and mixtures of, UI widget libraries; supporting multiple, and mixtures of, UI adornments; applying multiple, and mixtures of, UI layouts. Many of these characteristics seem ignored by current approaches. In addition, we discovered an emergent feature of these characteristics that opens the possibility of a previously unattempted goal - namely, retrofitting UI generation to an existing application.


title: Accelerated gradient methods for stochastic optimization and online learning

abstract: Regularized risk minimization often involves non-smooth optimization, either because of the loss function (e.g., hinge loss) or the regularizer (e.g., l1-regularizer). Gradient methods, though highly scalable and easy to implement, are known to converge slowly. In this paper, we develop a novel accelerated gradient method for stochastic optimization while still preserving their computational simplicity and scalability. The proposed algorithm, called SAGE (Stochastic Accelerated GradiEnt), exhibits fast convergence rates on stochastic composite optimization with convex or strongly convex objectives. Experimental results show that SAGE is faster than recent (sub)gradient methods including FOLOS, SMIDAS and SCD. Moreover, SAGE can also be extended for online learning, resulting in a simple algorithm but with the best regret bounds currently known for these problems.


title: FlexSense: a transparent self-sensing deformable surface

abstract: We present FlexSense, a new thin-film, transparent sensing surface based on printed piezoelectric sensors, which can reconstruct complex deformations without the need for any external sensing, such as cameras. FlexSense provides a fully self-contained setup which improves mobility and is not affected from occlusions. Using only a sparse set of sensors, printed on the periphery of the surface substrate, we devise two new algorithms to fully reconstruct the complex deformations of the sheet, using only these sparse sensor measurements. An evaluation shows that both proposed algorithms are capable of reconstructing complex deformations accurately. We demonstrate how FlexSense can be used for a variety of 2.5D interactions, including as a transparent cover for tablets where bending can be performed alongside touch to enable magic lens style effects, layered input, and mode switching, as well as the ability to use our device as a high degree-of-freedom input controller for gaming and beyond.


title: Taint-enhanced policy enforcement: A practical approach to defeat a wide range of attacks

abstract: Policy-based confinement, employed in SELinux and specification-based intrusion detection systems, is a popular approach for defending against exploitation of vulnerabilities in benign software. Conventional access control policies employed in these approaches are effective in detecting privilege escalation attacks. However, they are unable to detect attacks that "hijack" legitimate access privileges granted to a program, e.g., an attack that subverts an FTP server to download the password file. (Note that an FTP server would normally need to access the password file for performing user authentication.) Some of the common attack types reported today, such as SQL injection and cross-site scripting, involve such subversion of legitimate access privileges. In this paper, we present a new approach to strengthen policy enforcement by augmenting security policies with information about the trustworthiness of data used in securitysensitive operations. We evaluated this technique using 9 available exploits involving several popular software packages containing the above types of vulnerabilities. Our technique sucessfully defeated these exploits.


title: A 2.4-GHz monolithic fractional-N frequency synthesizer with robust phase-switching prescaler and loop capacitance multiplier

abstract: The design of a 2.4-GHz fully integrated /spl Sigma//spl Delta/ fractional-N frequency synthesizer in a 0.35-/spl mu/m CMOS process is presented. The design focuses on the prescaler and the loop filter, which are often the speed and the integration bottlenecks of the phase-locked loop (PLL), respectively. A 1.5-V 3-mW inherently glitch-free phase-switching prescaler is proposed. It is based on eight lower frequency 45/spl deg/-spaced phases and a reversed phase-switching sequence. The large integrating capacitor in the loop filter was integrated on chip via a simple capacitance multiplier that saves silicon area, consumes only 0.2 mW, and introduces negligible noise. The synthesizer has a 9.4% frequency tuning range from 2.23 to 2.45 GHz. It dissipates 16 mW and takes an active area of 0.35 mm/sup 2/ excluding the 0.5-mm/sup 2/ digital /spl Sigma//spl Delta/ modulator.


title: Topic similarity networks: Visual analytics for large document sets

abstract: We investigate ways in which to improve the interpretability of LDA topic models by better analyzing and visualizing their outputs. We focus on examining what we refer to as topic similarity networks: graphs in which nodes represent latent topics in text collections and links represent similarity among topics. We describe efficient and effective approaches to both building and labeling such networks. Visualizations of topic models based on these networks are shown to be a powerful means of exploring, characterizing, and summarizing large collections of unstructured text documents. They help to "tease out" non-obvious connections among different sets of documents and provide insights into how topics form larger themes. We demonstrate the efficacy and practicality of these approaches through two case studies: 1) NSF grants for basic research spanning a 14 year period and 2) the entire English portion of Wikipedia.


title: IRON: Functional Encryption using Intel SGX

abstract: Functional encryption (FE) is an extremely powerful cryptographic mechanism that lets an authorized entity compute on encrypted data, and learn the results in the clear. However, all current cryptographic instantiations for general FE are too impractical to be implemented. We construct IRON, a provably secure, and practical FE system using Intel's recent Software Guard Extensions (SGX). We show that IRON can be applied to complex functionalities, and even for simple functions, outperforms the best known cryptographic schemes. We argue security by modeling FE in the context of hardware elements, and prove that IRON satisfies the security model.


title: Upgrading Your Android, Elevating My Malware: Privilege Escalation through Mobile OS Updating

abstract: Android is a fast evolving system, with new updates coming out one after another. These updates often completely overhaul a running system, replacing and adding tens of thousands of files across Android's complex architecture, in the presence of critical user data and applications (apps for short). To avoid accidental damages to such data and existing apps, the upgrade process involves complicated program logic, whose security implications, however, are less known. In this paper, we report the first systematic study on the Android updating mechanism, focusing on its Package Management Service (PMS). Our research brought to light a new type of security-critical vulnerabilities, called Pileup flaws, through which a malicious app can strategically declare a set of privileges and attributes on a low-version operating system (OS) and wait until it is upgraded to escalate its privileges on the new system. Specifically, we found that by exploiting the Pileup vulnerabilities, the app can not only acquire a set of newly added system and signature permissions but also determine their settings (e.g., protection levels), and it can further substitute for new system apps, contaminate their data (e.g., cache, cookies of Android default browser) to steal sensitive user information or change security configurations, and prevent installation of critical system services. We systematically analyzed the source code of PMS using a program verification tool and confirmed the presence of those security flaws on all Android official versions and over 3000 customized versions. Our research also identified hundreds of exploit opportunities the adversary can leverage over thousands of devices across different device manufacturers, carriers and countries. To mitigate this threat without endangering user data and apps during an upgrade, we also developed a new detection service, called SecUP, which deploys a scanner on the user's device to capture the malicious apps designed to exploit Pileup vulnerabilities, based upon the vulnerability-related information automatically collected from newly released Android OS images.


title: Twelve tips for developing an integrated curriculum

abstract: Background: Integration of curriculum is meant to make the teaching/learning activities meaningful; however, the interpretation of ‘integration’ varies in different institutions and among individuals. Many medical schools find it hard to change their existing curriculum or develop a new integrated curriculum mainly because of lack of will, infrastructure and understanding the process of change. Aim: Drawn from experience, these 12 tips relating to staff development, establishing working groups, organizing the teaching/ learning materials under themes and developing some innovative teaching/learning and assessment strategies, are the techniques that a teaching institution can use to change the existing curriculum to an integrated one or develop a new curriculum. Conclusion: Knowing about the different levels of integration and steps for developing an integrated curriculum can make it easier to integrate an existing curriculum or develop a new one.


title: Rational Recurrences

abstract: Despite the tremendous empirical success of neural models in natural language processing, many of them lack the strong intuitions that accompany classical machine learning approaches. Recently, connections have been shown between convolutional neural networks (CNNs) and weighted finite state automata (WFSAs), leading to new interpretations and insights. In this work, we show that some recurrent neural networks also share this connection to WFSAs. We characterize this connection formally, defining rational recurrences to be recurrent hidden state update functions that can be written as the Forward calculation of a finite set of WFSAs. We show that several recent neural models use rational recurrences. Our analysis provides a fresh view of these models and facilitates devising new neural architectures that draw inspiration from WFSAs. We present one such model, which performs better than two recent baselines on language modeling and text classification. Our results demonstrate that transferring intuitions from classical models like WFSAs can be an effective approach to designing and understanding neural models.


title: EULAR recommendations for cardiovascular disease risk management in patients with rheumatoid arthritis and other forms of inflammatory joint disorders: 2015/2016 update

abstract: Patients with rheumatoid arthritis (RA) and other inflammatory joint disorders (IJD) have increased cardiovascular disease (CVD) risk compared with the general population. In 2009, the European League Against Rheumatism (EULAR) taskforce recommended screening, identification of CVD risk factors and CVD risk management largely based on expert opinion. In view of substantial new evidence, an update was conducted with the aim of producing CVD risk management recommendations for patients with IJD that now incorporates an increasing evidence base. A multidisciplinary steering committee (representing 13 European countries) comprised 26 members including patient representatives, rheumatologists, cardiologists, internists, epidemiologists, a health professional and fellows. Systematic literature searches were performed and evidence was categorised according to standard guidelines. The evidence was discussed and summarised by the experts in the course of a consensus finding and voting process. Three overarching principles were defined. First, there is a higher risk for CVD in patients with RA, and this may also apply to ankylosing spondylitis and psoriatic arthritis. Second, the rheumatologist is responsible for CVD risk management in patients with IJD. Third, the use of non-steroidal anti-inflammatory drugs and corticosteroids should be in accordance with treatment-specific recommendations from EULAR and Assessment of Spondyloarthritis International Society. Ten recommendations were defined, of which one is new and six were changed compared with the 2009 recommendations. Each designated an appropriate evidence support level. The present update extends on the evidence that CVD risk in the whole spectrum of IJD is increased. This underscores the need for CVD risk management in these patients. These recommendations are defined to provide assistance in CVD risk management in IJD, based on expert opinion and scientific evidence.


title: Modified Peptide YY Molecule Attenuates the Activity of NPY/AgRP Neurons and Reduces Food Intake in Male Mice

abstract: To study the effects of an analog of the gut-produced hormone peptide YY (PYY3-36), which has increased selectivity for the Y2 receptor; specifically, to record its effects on food intake and on hypothalamic neuropeptide Y/agouti-related peptide (NPY/AgRP) neuron activity. NNC0165-1273, a modified form of the peptide hormone PYY3-36 with potent selectivity at Y2 receptor (>5000-fold over Y1, 1250-fold over Y4, and 650-fold over Y5 receptor), was tested in vivo and in vitro in mouse models. NNC0165-1273 has fivefold lower relative affinity for Y2 compared with PYY3-36, but >250-, 192-, and 400-fold higher selectivity, respectively, for the Y1, Y4, and Y5 receptors. NNC0165-1273 produced a reduction in nighttime feeding at a dose at which PYY3-36 loses efficacy. The normal behavioral satiety sequence observed suggests that NNC0165-1273 is not nauseating and, instead, reduces food intake by producing early satiety. Additionally, NNC0165-1273 blocked ghrelin-induced cFos expression in NPY/AgRP neurons. In vitro electrophysiological recordings showed that, opposite to ghrelin, NNC0165-1273 hyperpolarized NPY/AgRP neurons and reduced action potential frequency. Administration of NNC0165-1273 via subcutaneous osmotic minipump caused a dose-dependent decrease in body weight and fat mass in an obese mouse model. Finally, NNC0165-1273 attenuated the feeding response when NPY/AgRP neurons were activated using ghrelin or more selectively with designer receptors. NNC0165-1273 is nonnauseating and stimulates a satiety response through, at least in part, a direct action on hypothalamic NPY/AgRP neurons. Modification of PYY3-36 to produce compounds with increased affinity to Y2 receptors may be useful as antiobesity therapies in humans.


title: Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities

abstract: Learning styles which refer to students' preferred ways to learn can play an important role in adaptive e-learning systems. With the knowledge of different styles, the system can offer valuable advice and instructions to students and teachers to optimise students' learning process. Moreover, e-leaning system which allows computerised and statistical algorithms opens the opportunity to overcome drawbacks of the traditional detection method that uses mainly questionnaire. These appealing reasons have led to a growing number of researches looking into the integration of learning styles and adaptive learning system. This paper, by reviewing 51 studies, delves deeply into different parts of the integration process. It captures a variety of aspects from learning styles theories selection in e-learning environment, online learning styles predictors, automatic learning styles classification to numerous learning styles applications. The results offer insights into different developments, achievements and open problems in the field. Based on these findings, the paper also provides discussion, recommendations and guidelines for future researches. Review on how learning styles were integrated into adaptive e-learning systems.Felder-Silverman's is found to be the most popular theory that was applied.Explore online learning styles predictors and automatic classification methods.Examine different applications of learning styles in adaptive learning system.Recommendation and future research opportunities are proposed.


title: Integrated Nutrient Management on Soybean in a Coastal Charland of Bangladesh

abstract: A field experiment was conducted at Char Jangalia under MLT site, Laxmipur, a coastal district of southern Bangladesh, during Rabi season of 2015-16 to evaluate the effect of integrated nutrient management on growth and yield performance of soybean (var. Glycine max L.). Six treatment combinations, viz. T 1 = Control, T 2 = Farmers practice (25-15 Kg NP ha -1 , T 3 = 100% recommended dose (30-25-55 Kg NP ha -1 , K), T 4 = 50 % recommended dose + 1.5 t ha -1 vermi compost, T 5 = 50 % recommended dose + 5 t ha -1 compost and T 6 =50 % recommended dose + 1.2 kg ha -1 bio fertilizer were tested. The experiment was laid out in randomized complete block design with 3 replications. There were significant differences among the different treatment combinations in terms of yield and yield contributing characters. Integrated nutrient management with application of 50% of the recommended doses of urea, TSP, MoP, and biofertilizer (Bradyrhizobium) at the rate of 1.2 kg ha -1 significantly increased most of the parameters, such as the plant height, number of nodule per plant, pods per plant, seeds per pod and seed yield. The highest seed yield (2.93 t ha -1 ) was recorded from T 6 while the lowest seed yield (1.83 t ha -1 ) was obtained from T 1 . The highest gross return (Tk.117200 ha -1 ) and gross margin (Tk. 71840 ha -1 ) was obtained from T 6 , whereas the lowest gross return (Tk. 70400 ha -1 ) and gross margin (Tk.27880 ha -1 ) was obtained from T 1 . The overall results indicated that the application of integrated nutrient management of a combination of bio fertilizer with 50% N-P-K (15- 12.5-27.5 Kg ha -1 ) of the recommended dose produced the maximum nodulation and seed yield of soybean in the southern coastal char land. Bangladesh Agron. J. 2017, 20(1): 77-83


title: Cellular and System Biology of Memory: Timing, Molecules, and Beyond

abstract: The storage of information in the mammalian nervous systems is dependent on a delicate balance between change and stability of neuronal networks. The induction and maintenance of processes that lead to changes in synaptic strength to a multistep process which can lead to long-lasting changes, which starts and ends with a highly choreographed and perfectly timed dance of molecules in different cell types of the central nervous system. This is accompanied by synchronization of specific networks, resulting in the generation of characteristic "macroscopic" rhythmic electrical fields, whose characteristic frequencies correspond to certain activity and information-processing states of the brain. Molecular events and macroscopic fields influence each other reciprocally. We review here cellular processes of synaptic plasticity, particularly functional and structural changes, and focus on timing events that are important for the initial memory acquisition, as well as mechanisms of short- and long-term memory storage. Then, we cover the importance of epigenetic events on the long-time range. Furthermore, we consider how brain rhythms at the network level participate in processes of information storage and by what means they participating in it. Finally, we examine memory consolidation at the system level during processes of sleep.


title: Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks

abstract: Real-time algorithms for automatically recognizing surgical phases are needed to develop systems that can provide assistance to surgeons, enable better management of operating room (OR) resources and consequently improve safety within the OR. State-of-the-art surgical phase recognition algorithms using laparoscopic videos are based on fully supervised training. This limits their potential for widespread application, since creation of manual annotations is an expensive process considering the numerous types of existing surgeries and the vast amount of laparoscopic videos available. In this work, we propose a new self-supervised pre-training approach based on the prediction of remaining surgery duration (RSD) from laparoscopic videos. The RSD prediction task is used to pre-train a convolutional neural network (CNN) and long short-term memory (LSTM) network in an end-to-end manner. Our proposed approach utilizes all available data and reduces the reliance on annotated data, thereby facilitating the scaling up of surgical phase recognition algorithms to different kinds of surgeries. Additionally, we present EndoN2N, an end-to-end trained CNN-LSTM model for surgical phase recognition and evaluate the performance of our approach on a dataset of 120 Cholecystectomy laparoscopic videos (Cholec120). This work also presents the first systematic study of self-supervised pre-training approaches to understand the amount of annotations required for surgical phase recognition. Interestingly, the proposed RSD pre-training approach leads to performance improvement even when all the training data is manually annotated and outperforms the single pre-training approach for surgical phase recognition presently published in the literature. It is also observed that end-to-end training of CNN-LSTM networks boosts surgical phase recognition performance.


title: Aerial 3D Building Detection and Modeling From Airborne LiDAR Point Clouds

abstract: A fast, completely automated method to create 3D watertight building models from airborne LiDAR point clouds is presented. The proposed method analyzes the scene content and produces multi-layer rooftops with complex boundaries and vertical walls that connect rooftops to the ground. A graph cuts based method is used to segment vegetative areas from the rest of scene content. The ground terrain and building rooftop patches are then extracted utilizing our technique, the hierarchical Euclidean clustering. Our method adopts a “divide-and-conquer” strategy. Once potential points on rooftops are segmented from terrain and vegetative areas, the whole scene is divided into individual pendent processing units which represent potential building footprints. For each individual building region, significant features on the rooftop are further detected using a specifically designed region growing algorithm with smoothness constraint. Boundaries for all of these features are refined in order to produce strict description. After this refinement, mesh models could be generated using an existing robust dual contouring method.


title: Potential Cyberattacks on Automated Vehicles

abstract: Vehicle automation has been one of the fundamental applications within the field of intelligent transportation systems (ITS) since the start of ITS research in the mid-1980s. For most of this time, it has been generally viewed as a futuristic concept that is not close to being ready for deployment. However, recent development of “self-driving” cars and the announcement by car manufacturers of their deployment by 2020 show that this is becoming a reality. The ITS industry has already been focusing much of its attention on the concepts of “connected vehicles” (United States) or “cooperative ITS” (Europe). These concepts are based on communication of data among vehicles (V2V) and/or between vehicles and the infrastructure (V2I/I2V) to provide the information needed to implement ITS applications. The separate threads of automated vehicles and cooperative ITS have not yet been thoroughly woven together, but this will be a necessary step in the near future because the cooperative exchange of data will provide vital inputs to improve the performance and safety of the automation systems. Thus, it is important to start thinking about the cybersecurity implications of cooperative automated vehicle systems. In this paper, we investigate the potential cyberattacks specific to automated vehicles, with their special needs and vulnerabilities. We analyze the threats on autonomous automated vehicles and cooperative automated vehicles. This analysis shows the need for considerably more redundancy than many have been expecting. We also raise awareness to generate discussion about these threats at this early stage in the development of vehicle automation systems.


title: Rank-Constrained Solutions to Linear Matrix Equations Using PowerFactorization

abstract: Algorithms to construct/recover low-rank matrices satisfying a set of linear equality constraints have important applications in many signal processing contexts. Recently, theoretical guarantees for minimum-rank matrix recovery have been proven for nuclear norm minimization (NNM), which can be solved using standard convex optimization approaches. While nuclear norm minimization is effective, it can be computationally demanding. In this work, we explore the use of the PowerFactorization (PF) algorithm as a tool for rank-constrained matrix recovery. Empirical results indicate that incremented-rank PF is significantly more successful than NNM at recovering low-rank matrices, in addition to being faster.


title: Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

abstract: Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop-based algorithms such as Q-learning and policy gradients on challenging deep reinforcement learning (RL) problems. However, ES can be considered a gradient-based algorithm because it performs stochastic gradient descent via an operation similar to a finite-difference approximation of the gradient. That raises the question of whether non-gradient-based evolutionary algorithms can work at DNN scales. Here we demonstrate they can: we evolve the weights of a DNN with a simple, gradient-free, population-based genetic algorithm (GA) and it performs well on hard deep RL problems, including Atari and humanoid locomotion. The Deep GA successfully evolves networks with over four million free parameters, the largest neural networks ever evolved with a traditional evolutionary algorithm. These results (1) expand our sense of the scale at which GAs can operate, (2) suggest intriguingly that in some cases following the gradient is not the best choice for optimizing performance, and (3) make immediately available the multitude of techniques that have been developed in the neuroevolution community to improve performance on RL problems. To demonstrate the latter, we show that combining DNNs with novelty search, which was designed to encourage exploration on tasks with deceptive or sparse reward functions, can solve a high-dimensional problem on which reward-maximizing algorithms (e.g. DQN, A3C, ES, and the GA) fail. Additionally, the Deep GA parallelizes better than ES, A3C, and DQN, and enables a state-of-the-art compact encoding technique that can represent million-parameter DNNs in thousands of bytes.


title: A Restaurant Recommender System Based on User Preference and Location in Mobile Environment

abstract: Recommender system is an effective way to help users to obtain the personalized and useful information. However, due to complexity and dynamic, the traditional recommender system cannot work well in mobile environment. In this paper, we propose a restaurant recommender system in mobile environment. This recommender system adopts a user preference model by using the features of user's visited restaurants, and also utilizes the location information of user and restaurants to dynamically generate the recommendation results. Baidu map cloud service is used to implement the proposed recommender system. The result of a case study shows that the proposed restaurant recommender system can effectively utilize user's preference and the location information to recommend the personalized and suitable restaurants for different users.


Neural Representation Learning in Linguistic Structured Prediction


title: Autonomous Driving among Many Pedestrians: Models and Algorithms.

abstract: Driving among a dense crowd of pedestrians is a major challenge for autonomous vehicles. This paper presents a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is a pedestrian motion prediction model that accounts for both a pedestrian's global navigation intention and local interactions with the vehicle and other pedestrians. Unfortunately, the autonomous vehicle does not know the pedestrian's intention a priori and requires a planning algorithm that hedges against the uncertainty in pedestrian intentions. Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in near real time. Experiments show that it enables a robot vehicle to drive safely, efficiently, and smoothly among a crowd with a density of nearly one person per square meter.


title: Exploring the Placement and Design of Word-Scale Visualizations

abstract: We present an exploration and a design space that characterize the usage and placement of word-scale visualizations within text documents. Word-scale visualizations are a more general version of sparklines-small, word-sized data graphics that allow meta-information to be visually presented in-line with document text. In accordance with Edward Tufte's definition, sparklines are traditionally placed directly before or after words in the text. We describe alternative placements that permit a wider range of word-scale graphics and more flexible integration with text layouts. These alternative placements include positioning visualizations between lines, within additional vertical and horizontal space in the document, and as interactive overlays on top of the text. Each strategy changes the dimensions of the space available to display the visualizations, as well as the degree to which the text must be adjusted or reflowed to accommodate them. We provide an illustrated design space of placement options for word-scale visualizations and identify six important variables that control the placement of the graphics and the level of disruption of the source text. We also contribute a quantitative analysis that highlights the effect of different placements on readability and text disruption. Finally, we use this analysis to propose guidelines to support the design and placement of word-scale visualizations.


title: Columbia-IBM news video story segmentation in trecvid 2004

abstract: In this technical report, we give an overview of our technical developments in the story segmentation task in TRECVID 2004. Among them, we propose an information-theoretic framework, visual cue cluster construction (VC), to automatically discover adequate mid-level features. The problem is posed as mutual information maximization, through which optimal cue clusters are discovered to preserve the highest information about the semantic labels. We extend the Information Bottleneck framework to high-dimensional continuous features and further propose a projection method to map each video into probabilistic memberships over all the cue clusters. The biggest advantage of the proposed approach is to remove the dependence on the manual process in choosing the mid-level features and the huge labor cost involved in annotating the training corpus for training the detector of each mid-level feature. When tested in TRECVID 2004 news video story segmentation, the proposed approach achieves promising performance gain over representations derived from conventional clustering techniques and even the mid-level features selected manually; meanwhile, it achieved one of the top performances, F1=0.65, close to the highest performance, F1=0.69, by other groups. We also experiment with other promising visual features and continue investigating effective prosody features. The introduction of post-processing also provides practical improvements. Furthermore, the fusion from other modalities, such as speech prosody features and ASR-based segmentation scores are significant and have been confirmed again in this experiment.


title: A Bidirectional Bridge Modular Switched-Capacitor-Based Power Electronics Transformer

abstract: This paper proposes a new bridge modular switched-capacitor-based ac–ac converter, which is intended to replace conventional transformers in commercial and residential applications, especially for microgrids. The main advantages of the proposed converter lie in high efficiency and power factor, bidirectional operation, and capacitor voltage self-balancing ability. Another distinct merit is its symmetric construction, with which the voltage ripple of charge/pump capacitors can be cancelled out, and the output voltage will be smooth with significant ripple reduction. It is also noteworthy that the symmetric construction ensures that the two symmetric parts can work independently under a simplified control strategy. In addition, the modular configuration contributes to convenient voltage extension. Compared with the reported flying-capacitor switched-capacitor ac–ac converter, the proposed converter requires fewer switches and capacitors per unit of voltage conversion, thus suffering lower voltage stress. To demonstrate the performance of the converter, a prototype with 600-W 220-Vrms high-side voltage, 55-Vrms low-side voltage, and a switching frequency of 100 kHz was designed and fabricated. The relevant experimental results are reported herein. The maximum and rated power efficiency obtained were 95.9% and 94.4%, respectively.


title: Health-related rumour detection on Twitter

abstract: In the last years social networks have emerged as a critical mean for information spreading. In spite of all the positive consequences this phenomenon brings, unverified and instrumentally relevant information statements in circulation, named as rumours, are becoming a potential threat to the society. Recently, there have been several studies on topic-independent rumour detection on Twitter. In this paper we present a novel rumour detection system which focuses on a specific topic, that is health-related rumours on Twitter. To this aim, we constructed a new subset of features including influence potential and network characteristics features. We tested our approach on a real dataset observing promising results, as it is able to correctly detect about 89% of rumours, with acceptable levels of precision.


title: Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals

abstract: Steganalysis of the quantization index modulation (QIM) steganography in a low-bit-rate encoded speech stream is conducted in this research. According to the speech generation theory and the phoneme distribution properties in language, we first point out that the correlation characteristics of split vector quantization (VQ) codewords of linear predictive coding filter coefficients are changed after the QIM steganography. Based on this observation, we construct a model called the Quantization codeword correlation network (QCCN) based on split VQ codeword from adjacent speech frames. Furthermore, the QCCN model is pruned to yield a stronger correlation network. After quantifying the correlation characteristics of vertices in the pruned correlation network, we obtain feature vectors that are sensitive to steganalysis. Finally, we build a high-performance detector using the support vector machine (SVM) classifier. It is shown by experimental results that the proposed QCCN steganalysis method can effectively detect the QIM steganography in encoded speech stream when it is applied to low-bit-rate speech codec such as G.723.1 and G.729.


title: Medieval Islamic Architecture, Quasicrystals, and Penrose and Girih Tiles: Questions from the Classroom

abstract: Tiling Theory studies how one might cover the plane with various shapes. Medieval Islamic artisans developed intricate geometric tilings to decorate their mosques, mausoleums, and shrines. Some of these patterns, called girih tilings, first appeared in the 12 th Century AD. Recent investigations show these medieval tilings contain symmetries similar to those found in aperiodic Penrose tilings first investigated in the West in the 1970’s. These intriguing discoveries may suggest that the mathematical understanding of these artisans was much deeper than originally thought. Connections like these, made across the centuries, provide a wonderful opportunity for students to discover the beauty of Islamic architecture in a mathematical and historical context. This paper describes several geometric constructions for Islamic tilings for use in the classroom along with projects utilizing girih tiles as construction templates. Open questions, observations, and conjectures raised in seminars across the United Arab Emirates are described including what the medieval artisans may have known as well as how girih tiles might have been used as tools in the actual construction of intricate patterns.


title: On Security Analysis of Proof-of-Elapsed-Time (PoET)

abstract: As more applications are built on top of blockchain and public ledger, different approaches are developed to improve the performance of blockchain construction. Recently Intel proposed a new concept of proof-of-elapsed-time (PoET), which leverages trusted computing to enforce random waiting times for block construction. However, trusted computing component may not be perfect and 100% reliable. It is not clear, to what extent, blockchain systems based on PoET can tolerate failures of trusted computing component. The current design of PoET lacks rigorous security analysis and a theoretical foundation for assessing its strength against such attacks. To fulfill this gap, we develop a theoretical framework for evaluating a PoET based blockchain system, and show that the current design is vulnerable in the sense that adversary can jeopardize the blockchain system by only compromising \(\varTheta (\log \log n/\log n)\) fraction of the participating nodes, which is very small when n is relatively large. Based on our theoretical analysis, we also propose methods to mitigate these vulnerabilities.


title: Diagnosis Using the Formal Theory of a Signal-Processing System

abstract: Many signal-processing systems can be viewed as transforming their input signals into a representation of the real-world scenario from which the signals originated. Such systems usually have parameters whose settings are selected on the basis of the class of expected input scenarios. Finding the appropriate parameter settings for a class of input scenarios usually involves testing the system against typical and/or important input scenarios from that class. Whenever the system output does not match the input scenario, the parameter settings responsible for the fault are identified. The system user can then adjust the system parameters to ensure correct system behavior for such scenarios. The diagnostic process of identifying the parameters responsible for system faults is generally difficult because the signal-processing system carries out a complicated mathematical transformation involving a multistage algorithm that generates an enormous amount of intermediate data. A new approach to the diagnosis of such systems is developed. The approach is based on the availability of an abstract and possibly qualitative description of the input scenario and the use of an alternative system model derived from the underlying mathematical theory that explicitly represents the phenomena responsible for any incorrect processing. This approach to diagnosis models a system as a combination of processes that transform the user-specified abstract description of the input scenario into the system output. Whenever the correct answer is obtained at the system output, each process reduces to an identity transformation at the level of abstraction of the system output.