Computational Thinking … and Doing

A t our most recent CiSE meeting, one of the board members approached me during a short break and asked: " Why do you do this? you seem to have better things to do. " seeking clarification , i came to understand that he Was Wondering Why a computer scientist such as myself would want to be on the editorial board of a publication that isn't necessarily focused on the " tradition " of computer science. then and there, i realized that a computer sci-entist's role is something worth explaining and understanding. much has been made recently of computational thinking, a term coined by Jeanette Wing at the us national science foundation. 1 While i have my doubts about the term's longevity, 2 it struck me as an excellent starting point for explaining what a computer scientist is and does, especially in the CiSE context. computer scientists are an increasingly important part of interdisciplinary—or multidisciplinary, a term i prefer—problem solving. We help others understand the merits of bringing the right methods (algorithms), architecture/design (software engineering), and programming tools (languages and systems) and techniques (visualization) to science and engineering applications. so, as i see it, computer scientists bring a panoply of options to the table that enhance multi disciplinary research. if you will, we're about computational doing—and doing it right. as the former chez thiruvathukal in scientific programming , i like to view computer science's various ideas as the masala found in my (and possibly your) favorite curries. this brings me back to why i'm here. i've always had an eye to applying computer science to other disciplines, scientific and otherwise. in the past few months, i've begun working on two new (funded) projects—one addresses healthcare (modeling health issues in social networks using agent-based simulation) and the other addresses digital humanities (developing collaborative tools for tex-tual studies). in both cases, computer science plays a critical role in that both projects have expected software and experimental outcomes. i don't always write about my other disciplinary interests in CiSE, but given my emphasis on programming and emerging technologies , it's comforting to know that i've joined a group of people committed to exploring the intersections with and showing respect for other disciplines. for the sake of scholarship and meaningful discourse, we need more places like CiSE, and we must keep the content fresh and exciting—even during these challenging times when all publications face …


On structuring probabilistic dependences in stochastic language modelling


Translational Recommender Networks

Representing relationships as translations in vector space lives at the heart of many neural embedding models such as word embeddings and knowledge graph embeddings. In this work, we study the connections of this translational principle with collaborative ltering algorithms. We propose Translational Recommender Networks (TransRec), a new aentive neural architecture that utilizes the translational principle to model the relationships between user and item pairs. Our model employs a neural aention mechanism over a Latent Relational Aentive Memory (LRAM) module to learn the latent relations between user-item pairs that best explains the interaction. By exploiting adaptive user-item specic translations in vector space, our model also alleviates the geometric inexibility problem of other metric learning algorithms while enabling greater modeling capability and ne-grained ing of users and items in vector space. e proposed architecture not only demonstrates the state-of-the-art performance across multiple recommendation benchmarks but also boasts of improved interpretability. alitative studies over the LRAM module shows evidence that our proposed model is able to infer and encode explicit sentiment, temporal and aribute information despite being only trained on implicit feedback. As such, this ascertains the ability of TransRec to uncover hidden relational structure within implicit datasets.


How STEM Education Improves Student Learning

The intent of this article is to examine current taxonomies used to design and deliver Science, Technology, Engineering, and Mathematics (i.e., STEM) curriculum in an effort to attract a greater variety of students to the STEM field of study in the K-12 public school environment. I analyzed specific aspects of STEM-based programs to compare STEM education components to traditional college-preparatory methods of instruction, including looking for environmental practices that may attract female and first-generation college attendees toward developing a positive attitude toward attaining a STEM education. I also made connections between current instructional trends and their impact on student mastery.


Health Information Management System for Elderly Health Sector: A Qualitative Study in Iran.

BACKGROUND
There are increasing change and development of information in healthcare systems. Given the increase in aging population, managers are in need of true and timely information when making decision.

OBJECTIVES
The aim of this study was to investigate the current status of the health information management system for the elderly health sector in Iran.

MATERIALS AND METHODS
This qualitative study was conducted in two steps. In the first step, required documents for administrative managers were collected using the data gathering form and observed and reviewed by the researcher. In the second step, using an interview guide, the required information was gathered through interviewing experts and faculty members. The convenience, purposeful and snowball sampling methods were applied to select interviewees and the sampling continued until reaching the data saturation point. Finally, notes and interviews were transcribed and content analysis was used to analyze them.

RESULTS
The results of the study showed that there was a health information management system for the elderly health sector in Iran. However, in all primary health care centers the documentation of data was done manually; the data flow was not automated; and the analysis and reporting of data are also manually. Eventually, decision makers are provided with delayed information.

CONCLUSIONS
It is suggested that the steward of health in Iran, the ministry of health, develops an appropriate infrastructure and finally puts a high priority on the implementation of the health information management system for elderly health sector in Iran.


A 0.18μm CMOS fully integrated RFDAC and VGA for WCDMA transmitters

A digital IF to RF converter architecture with an RF VGA targeted for WCDMA transmitters is presented. The RFDAC consists of a 1.5-bits current steering DAC with an embedded semi-digital FIR filter and a mixer with an LC load. The VGA controls the output power in 10 dB steps. The prototype is fabricated in 0.18 mum CMOS technology and achieves 0 dBm output power at 2 GHz and 33 dB image rejection. The measured rms EVM and ACPR show the system fulfills the standard specifications for the WCDMA. The chip draws 123.9 mA from 1.8 V supply. The die area is 0.8 times 1.6 mm2.


Personalized Multitask Learning for Predicting Tomorrow ’ s Mood , Stress , and Health

While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional machine learning (ML) methods frequently yield low performance in this domain. We posit that this is because a one-size-fits-all machine learning model is inherently ill-suited to predicting outcomes like mood and stress, which vary greatly due to individual differences. Therefore, we employ Multitask Learning (MTL) techniques to train personalized ML models which are customized to the needs of each individual, but still leverage data from across the population. Three formulations of MTL are compared: i) MTL deep neural networks, which share several hidden layers but have final layers unique to each task; ii) Multi-task Multi-Kernel learning, which feeds information across tasks through kernel weights on feature types; and iii) a Hierarchical Bayesian model in which tasks share a common Dirichlet Process prior. We offer the code for this work in open source. These techniques are investigated in the context of predicting future mood, stress, and health using data collected from surveys, wearable sensors, smartphone logs, and the weather. Empirical results demonstrate that using MTL to account for individual differences provides large performance improvements over traditional machine learning methods and provides personalized, actionable insights.


Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model

We introduce an extensible and modifiable knowledge representation model to represent cancer disease characteristics in a comparable and consistent fashion. We describe a system, MedTAS/P which automatically instantiates the knowledge representation model from free-text pathology reports. MedTAS/P is based on an open-source framework and its components use natural language processing principles, machine learning and rules to discover and populate elements of the model. To validate the model and measure the accuracy of MedTAS/P, we developed a gold-standard corpus of manually annotated colon cancer pathology reports. MedTAS/P achieves F1-scores of 0.97-1.0 for instantiating classes in the knowledge representation model such as histologies or anatomical sites, and F1-scores of 0.82-0.93 for primary tumors or lymph nodes, which require the extractions of relations. An F1-score of 0.65 is reported for metastatic tumors, a lower score predominantly due to a very small number of instances in the training and test sets.


A taxonomy and rating system to measure situation awareness in resuscitation teams

Team SA involves a common perspective between two or more individuals regarding current environmental events, their meaning, and projected future status. Team SA has been theorized to be important for resuscitation team effectiveness. Accordingly, multidimensional frameworks of observable behaviors relevant to resuscitation teams are needed to understand more deeply the nature of team SA, its implications for team effectiveness, and whether it can be trained. A seven-dimension team resuscitation SA framework was developed following a literature review and consensus process using a modified Delphi approach with a group of content experts. We applied a pre-post design within a day-long team training program involving four video-recorded simulated resuscitation events and 42 teams across Canada. The first and fourth events represented "pre" and "post" training events, respectively. Teams were scored on SA five times within each 15-minute event. Distractions were introduced to investigate whether SA scores would be affected. The current study provides initial construct validity evidence for a new measure of SA and explicates SA's role in resuscitation teams.


Compact Aspect Embedding for Diversified Query Expansions

Diversified query expansion (DQE) based approaches aim to select a set of expansion terms with less redundancy among them while covering as many query aspects as possible. Recently they have experimentally demonstrate their effectiveness for the task of search result diversification. One challenge faced by existing DQE approaches is to ensure the aspect coverage. In this paper, we propose a novel method for DQE, called compact aspect embedding, which exploits trace norm regularization to learn a low rank vector space for the query, with each eigenvector of the learnt vector space representing an aspect, and the absolute value of its corresponding eigenvalue representing the association strength of that aspect to the query. Meanwhile, each expansion term is mapped into the vector space as well. Based on this novel representation of the query aspects and expansion terms, we design a greedy selection strategy to choose a set of expansion terms to explicitly cover all possible aspects of the query. We test our method on several TREC diversification data sets, and show that our method significantly outperforms the state-of-the-art search result diversification approaches.


LATIONS FOR KNOWLEDGE BASE COMPLETION

In this work we present a novel approach for the utilization of observed relations between entity pairs in the task of triple argument prediction. The approach is based on representing observations in a shared, continuous vector space of structured relations and text. Results on a recent benchmark dataset demonstrate that the new model is superior to existing sparse feature models. In combination with state-of-the-art models, we achieve substantial improvements when observed relations are available.


What's in an Embedding? Analyzing Word Embeddings through Multilingual Evaluation

In the last two years, there has been a surge of word embedding algorithms and research on them. However, evaluation has mostly been carried out on a narrow set of tasks, mainly word similarity/relatedness and word relation similarity and on a single language, namely English. We propose an approach to evaluate embeddings on a variety of languages that also yields insights into the structure of the embedding space by investigating how well word embeddings cluster along different syntactic features. We show that all embedding approaches behave similarly in this task, with dependency-based embeddings performing best. This effect is even more pronounced when generating low dimensional embed-


A Practical Security Architecture for In-Vehicle CAN-FD

The controller area network with flexible data rate (CAN-FD) is attracting attention as the next generation of in-vehicle network technology. However, security issues have not been completely taken into account when designing CAN-FD, although every bit of information transmitted could be critical to driver safety. If we fail to solve the security vulnerabilities of CAN-FD, we cannot expect Vehicle-Information and Communications Technology (Vehicle-ICT) convergence to continue to develop. Fortunately, secure in-vehicle CAN-FD communication environments can be constructed using the larger data payload of CAN-FD. In this paper, we propose a security architecture for in-vehicle CAN-FD as a countermeasure (designed in accordance with CAN-FD specifications). We considered the characteristics of the International Organization for Standardization (ISO) 26262 Automotive Safety Integrity Level and the in-vehicle subnetwork to design a practical security architecture. We also evaluated the feasibility of the proposed security architecture using three kinds of microcontroller unit and the CANoe software. Our evaluation findings may be used as an indicator of the performance level of electronic control units for manufacturing next-generation vehicles.


Design, measurement and equivalent circuit synthesis of high power HF transformer for three-phase composite dual active bridge topology

High voltage high frequency (HF) transformer provides the isolation between high and low dc link voltages in dual active bridge (DAB) converters. Such DAB converters are finding wide applications as an intermediate DC-DC converter in transformerless intelligent power substation (TIPS), which is proposed as an alternative for conventional distribution-transformer connecting 13.8 kV and 480 V grids. The design of HF transformer used in DAB stage of such application is very challenging considering the required isolation, size and cost. In this paper, the specification generation, design, characterization, test and measurement results on a 10kHz HF transformer are presented, highlighting the above challenges.


Analysis of the weighted Chinese air transportation multilayer network

Air transportation system is a crucial infrastructure of modern world. In this paper, we analyze the weighted Chinese air transportation network with the framework of multilayer network, in which each layer is defined by a commercial airline (company) and the weight of links is set respectively by the number of flights, the number of seats and the geographical distance between pairs of airports. It is shown that the size of the airlines can be characterized approximatively by the number of air routes they operated. The numeric results also indicate that the CCA (air China) is of a considerable higher value of maximal degree and betweenness than other top airlines. Our work will be helpful to better understand and model the Chinese air transportation network.


Domain Transfer SVM for video concept detection

Cross-domain learning methods have shown promising results by leveraging labeled patterns from auxiliary domains to learn a robust classifier for target domain, which has a limited number of labeled samples. To cope with the tremendous change of feature distribution between different domains in video concept detection, we propose a new cross-domain kernel learning method. Our method, referred to as Domain Transfer SVM (DTSVM), simultaneously learns a kernel function and a robust SVM classifier by minimizing both the structural risk functional of SVM and the distribution mismatch of labeled and unlabeled samples between the auxiliary and target domains. Comprehensive experiments on the challenging TRECVID corpus demonstrate that DTSVM outperforms existing cross-domain learning and multiple kernel learning methods.


Characterization of a Novel Nine-Transistor SRAM Cell

Data stability of SRAM cells has become an important issue with the scaling of CMOS technology. Memory banks are also important sources of leakage since the majority of transistors are utilized for on-chip caches in today's high performance microprocessors. A new nine-transistor (9T) SRAM cell is proposed in this paper for simultaneously reducing leakage power and enhancing data stability. The proposed 9T SRAM cell completely isolates the data from the bit lines during a read operation. The read static-noise-margin of the proposed circuit is thereby enhanced by 2 X as compared to a conventional six-transistor (6T) SRAM cell. The idle 9T SRAM cells are placed into a super cutoff sleep mode, thereby reducing the leakage power consumption by 22.9% as compared to the standard 6T SRAM cells in a 65-nm CMOS technology. The leakage power reduction and read stability enhancement provided with the new circuit technique are also verified under process parameter variations.


Labelled data collection for anomaly detection in wireless sensor networks

Security of wireless sensor networks (WSN) is an important research area in computer and communications sciences. Anomaly detection is a key challenge in ensuring the security of WSN. Several anomaly detection algorithms have been proposed and validated recently using labeled datasets that are not publicly available. Our group proposed an ellipsoid-based anomaly detection algorithm but demonstrated its performance using synthetic datasets and real Intel Berkeley Research Laboratory and Grand St. Bernard datasets which are not labeled with anomalies. This approach requires manual assignment of the anomalies' positions based on visual estimates for performance evaluation. In this paper, we have implemented a single-hop and multi-hop sensor-data collection network. In both scenarios we generated real labeled data for anomaly detection and identified different types of anomalies. These labeled sensor data and types of anomalies are useful for research, such as machine learning, and this information will be disseminated to the research community.


Performance of LoRa-Based IoT Applications on Campus

To promote smart campus, National Chiao Tung University is deploying several location-based IoT applications based on wireless communications technologies including LoRa and NB-IoT. The applications are indoor/outdoor environment conditions monitoring (such as PM2.5, temperature, and humidity), emergency buttons, and so on. This paper uses PM2.5 measurement application as an example to present the performance of LoRa when it is used on campus. We investigated the LoRa transmission performance from LoRa end-devices to the LoRa gateway. We show how packet losses were affected by the distance between the end-device and the gateway, the transmit power, the payload length, the antenna angle, the time of day, and the weather conditions. The pattern of LoRa packet losses were also measured and we found that in our long-term PM2.5 monitoring application, more than 99\% of LoRa packet losses occurred with three or less consecutive packet losses.


Large-Scale Video Classification with Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes. We study multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggest a multiresolution, foveated architecture as a promising way of speeding up the training. Our best spatio-temporal networks display significant performance improvements compared to strong feature-based baselines (55.3% to 63.9%), but only a surprisingly modest improvement compared to single-frame models (59.3% to 60.9%). We further study the generalization performance of our best model by retraining the top layers on the UCF-101 Action Recognition dataset and observe significant performance improvements compared to the UCF-101 baseline model (63.3% up from 43.9%).


Mechanisms governing empowerment effects: a self-efficacy analysis.

This experiment tested the hypotheses that perceived coping and cognitive control self-efficacy govern the effects of personal empowerment over physical threats. Women participated in a mastery modeling program in which they mastered the physical skills to defend themselves successfully against unarmed sexual assailants. Multifaceted measures of theoretically relevant variables were administered within a staggered intragroup control design to test the immediate and long-term effects of the empowerment program and the mechanisms through which it produced its effects. Mastery modeling enhanced perceived coping and cognitive control efficacy, decreased perceived vulnerability to assault, and reduced the incidence of intrusive negative thinking and anxiety arousal. These changes were accompanied by increased freedom of action and decreased avoidant behavior. Path analyses of causal structures revealed a dual path of regulation of behavior by perceived coping self-efficacy, one mediated through perceived vulnerability and risk discernment and the other through perceived cognitive control self-efficacy and intrusive negative thinking.


Computationally efficient low-pass FIR filter design using Cuckoo Search with adaptive Levy step size

This paper looks into efficient implementation of one dimensional low pass Finite Impulse Response filters using certain commonly used and state-of-the-art optimization techniques. Methods like Parks-McClellan (PM) equiripple design, Quantum-behaved Particle Swarm Optimization (QPSO) and Cuckoo Search Algorithm (CSA) with Levy Flight are employed and overall performance is further improved by hybridization and adaptive step size update. Various performance metrics are analyzed with a focus on increasing convergence speed to reach global optima faster. It is seen that the improved search methods used in this work, i.e., Simulated Annealing based Weighted Mean Best QPSO (SAWQPSO) and Adaptive CSA (ACSA) effect significant reductions in convergence time with ACSA proving to be the faster one. The designed filter is used in the receiver stage of a Frequency Modulated Radio Transmission model using a Quadrature-Phase Shift Keyed (QPSK) Modulator and Demodulator. Its efficiency is validated by obtaining near perfect correlation between the message and recovered signals.


Multiple kernel learning, conic duality, and the SMO algorithm

While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimization of the coefficients of such a combination reduces to a convex optimization problem known as a quadratically-constrained quadratic program (QCQP). Unfortunately, current convex optimization toolboxes can solve this problem only for a small number of kernels and a small number of data points; moreover, the sequential minimal optimization (SMO) techniques that are essential in large-scale implementations of the SVM cannot be applied because the cost function is non-differentiable. We propose a novel dual formulation of the QCQP as a second-order cone programming problem, and show how to exploit the technique of Moreau-Yosida regularization to yield a formulation to which SMO techniques can be applied. We present experimental results that show that our SMO-based algorithm is significantly more efficient than the general-purpose interior point methods available in current optimization toolboxes.


Large Margin Methods for Structured and Interdependent Output Variables

Learning general functional dependencies between arbitra ry input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representa tions, this paper addresses the complementary issue of designing classification algorithms that c an deal with more complex outputs, such as trees, sequences, or sets. More generally, we consider pr oblems involving multiple dependent output variables, structured output spaces, and classifica tion problems with class attributes. In order to accomplish this, we propose to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulat ion. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm that solves the optimization problem i n polynomial time for a large class of problems. The proposed method has important applications i n areas such as computational biology, natural language processing, information retrieval/extr action, and optical character recognition. Experiments from various domains involving different types o f output spaces emphasize the breadth and generality of our approach.


Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space—classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm— using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.


Open-World Knowledge Graph Completion

Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking. However, most KGs are far from complete and are growing at a rapid pace. To address these problems, Knowledge Graph Completion (KGC) has been proposed to improve KGs by filling in its missing connections. Unlike existing methods which hold a closed-world assumption, i.e., where KGs are fixed and new entities cannot be easily added, in the present work we relax this assumption and propose a new open-world KGC task. As a first attempt to solve this task we introduce an openworld KGC model called ConMask. This model learns embeddings of the entity’s name and parts of its text-description to connect unseen entities to the KG. To mitigate the presence of noisy text descriptions, ConMask uses a relationshipdependent content masking to extract relevant snippets and then trains a fully convolutional neural network to fuse the extracted snippets with entities in the KG. Experiments on large data sets, both old and new, show that ConMask performs well in the open-world KGC task and even outperforms existing KGC models on the standard closed-world KGC task. Introduction Knowledge Graphs (KGs) are a special type of information network that represents knowledge using RDF-style triples 〈h, r, t〉, where h represents some head entity and r represents some relationship that connects h to some tail entity t. In this formalism a statement like “Springfield is the capital of Illinois” can be represented as 〈Springfield, capitalOf, Illinois〉. Recently, a variety of KGs, such as DBPedia (Lehmann et al. 2015), and ConceptNet (Speer, Chin, and Havasi 2017), have been curated in the service of fact checking (Shi and Weninger 2016), question answering (Lukovnikov et al. 2017), entity linking (Hachey et al. 2013), and for many other tasks (Nickel et al. 2016). Despite their usefulness and popularity, KGs are often noisy and incomplete. For example, DBPedia, which is generated from Wikipedia’s infoboxes, contains 4.6 million entities, but half of these entities contain less than 5 relationships. Based on this observation, researchers aim to improve the accuracy and reliability of KGs by predicting the existence Copyright c © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. (or probability) of relationships. This task is often called Knowledge Graph Completion (KGC). Continuing the example from above, suppose the relationship capitalOf is missing between Indianapolis and Indiana; the KGC task might predict this missing relationship based on the topological similarity between this part of the KG and the part containing Springfield and Illinois. Progress in vector embeddings originating with word2vec has produced major advancements in the KGC task. Typical embedding-based KGC algorithms like TransE (Bordes et al. 2013) and others learn lowdimensional representations (i.e., embeddings) for entities and relationships using topological features. These models are able to predict the existence of missing relationships thereby “completing” the KG. Existing KGC models implicitly operate under the Closed-World Assumption (Reiter 1978) in which all entities and relationships in the KG cannot be changed – only discovered. We formally define the Closed-word KGC task as follows: Definition 1 Given an incomplete Knowledge Graph G = (E,R,T), where E, R, and T are the entity set, relationship set, and triple set respectively, Closed-World Knowledge Graph Completion completes G by finding a set of missing triples T = {〈h, r, t〉|h ∈ E, r ∈ R, t ∈ E, 〈h, r, t〉 / ∈ T} in the incomplete Knowledge Graph G. Closed-world KGC models heavily rely on the connectivity of the existing KG and are best able to predict relationships between existing, well-connected entities. Unfortunately, because of their strict reliance on the connectivity of the existing KG, closed-world KGC models are unable to predict the relationships of poorly connected or new entities. Therefore, we assess that closed-world KGC is most suitable for fixed or slowly evolving KGs. However, most real-world KGs evolve quickly with new entities and relationships being added by the minute. For example, in the 6 months between DBPedia’s October 2015 release and its April 2016 release 36, 340 new English entities were added – a rate of 200 new entities per day. Recall that DBPedia merely tracks changes to Wikipedia infoboxes, so these updates do not include newly added articles without valid infobox data. Because of the accelerated growth of online information, repeatedly re-training closed-world models every day (or hour) has become impractical. In the present work we borrow the idea of openworld assumption from probabilistic database literature (Ceylan, Darwiche, and Van den Broeck 2016) and relax the closed-world assumption to develop an Open-World Knowledge Graph Completion model capable of predicting relationships involving unseen entities or those entities that have only a few connections. Formally we define the openworld KGC task as follows: Definition 2 Given an incomplete Knowledge Graph G = (E,R,T), where E, R, and T are the entity set, relationship set, and triple set respectively, Open-World Knowledge Graph Completion completes G by finding a set of missing triples T = {〈h, r, t〉|〈h, r, t〉 / ∈ T, h ∈ E, t ∈ E, r ∈ R} in the incomplete Knowledge Graph G where E is an entity superset. In Defn. 2 we relax the constraint on the triple set T so that triples in T can contain entities that are absent from the original entity set E. Closed-world KGC models learn entity and relationship embedding vectors by updating an initially random vector based on the KG’s topology. Therefore, any triple 〈h, r, t〉 ∈ T ′ such that h / ∈ E or t / ∈ E will only ever be represented by its initial random vector because its absence does not permit updates from any inference function. In order to predict the missing connections for unseen entities, it is necessary to develop alternative features to replace the topological features used by closed-world models. Text content is a natural substitute for the missing topological features of disconnected or newly added entities. Indeed, most KGs such as FreeBase (Bollacker et al. 2008), DBPedia (Lehmann et al. 2015), and SemMedDB (Kilicoglu et al. 2012) were either directly extracted from (Lin et al. 2016; Ji and Grishman 2011), or are built in parallel to some underlying textual descriptions. However, open-world KGC differs from the standard information extraction task because 1) Rather than extracting triples from a large text corpus, the goal of open-world KGC is to discover missing relationships; and 2) Rather than a pipeline of independent subtasks like Entity Linking (Francis-Landau, Durrett, and Klein 2016) and Slotfilling (Liu and Lane 2016), etc., open-world KGC is a holistic task that operates as a single model. Although it may seem intuitive to simply include an entity’s description into an existing KGC model, we find that learning useful vector embeddings from unstructured text is much more challenging than learning topology-embeddings as in the closed-world task. First, in closed-world KGC models, each entity will have a unique embedding, which is learned from its directly connected neighbors; whereas open-world KGC models must fuse entity embeddings with the word embeddings of the entity’s description. These word embeddings must be updated by entities sharing the same words regardless of their connectivity status. Second, because of the inclusion of unstructured content, open-world models are likely to include noisy or redundant information. With respect to these challenges, the present work makes the following contributions: 1. We describe an open-world KGC model called ConMask that uses relationship-dependent content masking to reduce noise in the given entity description and uses fully convolutional neural networks (FCN) to fuse related text into a relationship-dependent entity embedding. 2. We release two new Knowledge Graph Completion data sets constructed from DBPedia and Wikipedia for use in closed-world and open-world KGC evaluation. Before introduce the ConMask model, we first present preliminary material by describing relevant KGC models. Then we describe the methodology, data sets, and a robust case study of closed-world and open-world KGC tasks. Finally, we draw conclusions and offer suggestions for future work. Closed-World Knowledge Graph Completion A variety of models have been developed to solve the closedworld KGC task. The most fundamental and widely used model is a translation-based Representation Learning (RL) model called TransE (Bordes et al. 2013). TransE assumes there exists a simple function that can translate the embedding of the head entity to the embedding of some tail entity via some relationship:


Implicit Quantile Networks for Distributional Reinforcement Learning

For the merging function m, the simplest choice would be a simple vector concatenation of ψ(x) and φ(τ). Note however, that the MLP f which takes in the output of m and outputs the action-value quantiles, only has a single hidden layer in the DQN network. Therefore, to force a sufficiently early interaction between the two representations, we also considered a multiplicative function m(ψ, φ) = ψ φ, where denotes the element-wise (Hadamard) product of two vectors, as well as a ‘residual’ function m(ψ, φ) = ψ (1 + φ).


DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image

In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth. Inspired by the indoor depth completion, our network estimates surface normals as the intermediate representation to produce dense depth, and can be trained end-to-end. With a modified encoder-decoder structure, our network effectively fuses the dense color image and the sparse LiDAR depth. To address outdoor specific challenges, our network predicts a confidence mask to handle mixed LiDAR signals near foreground boundaries due to occlusion, and combines estimates from the color image and surface normals with learned attention maps to improve the depth accuracy especially for distant areas. Extensive experiments demonstrate that our model improves upon the state-of-the-art performance on KITTI depth completion benchmark. Ablation study shows the positive impact of each model components to the final performance, and comprehensive analysis shows that our model generalizes well to the input with higher sparsity or from indoor scenes.


Introduction Welcome to this early seminal work , Fostering Resiliency in Kids : Protective Factors in the Family , School and Community

The field of prevention, both research and practice, came a long way in the1980s: from short-term, even one-shot, individual-focused interventions in the school classroom to a growing awareness and beginning implementation of long-term, comprehensive, environmental-focused interventions expanding beyond the school to include the community. Furthermore, in the mid-1980s we finally started to hear preventionists talking about prevention strategies and programs based on research identifying the underlying risk factors for problems such as alcohol and other drug abuse, teen pregnancy, delinquency and gangs, and dropping out (Hawkins, Lishner, and Catalano, 1985). While certainly a giant step in the right direction, the identification of risks does not necessarily provide us with a clear sense of just what strategies we need to implement to reduce the risks. More recently, we are hearing preventionists talk about “protective factors,” about building “resiliency” in youth, about basing our strategies on what research hast old us about the environmental factors that facilitate the development of youth who do not get involved in life-compromising problems (Benard, March 1987). What clearly becomes the challenge for the l990s is the implementation of prevention strategies that strengthen protective factors in our families, schools, an communities. As Gibbs and Bennett (1990) conceptualize the process, we must”turn the situation around by translating negative risk factors into positive action strategies” which are, in essence, protective factors. After a brief overview of the protective factor research phenomenon, this paper will discuss the major protective factors that research has identified as contributing to the development of resiliency in youth and the implications of this for building effective prevention programs.


A Novel AHRS Inertial Sensor-Based Algorithm for Wheelchair Propulsion Performance Analysis

With the increasing rise of professionalism in sport, athletes, teams, and coaches are looking to technology to monitor performance in both games and training in order to find a competitive advantage. The use of inertial sensors has been proposed as a cost effective and adaptable measurement device for monitoring wheelchair kinematics; however, the outcomes are dependent on the reliability of the processing algorithms. Though there are a variety of algorithms that have been proposed to monitor wheelchair propulsion in court sports, they all have limitations. Through experimental testing, we have shown the Attitude and Heading Reference System (AHRS)-based algorithm to be a suitable and reliable candidate algorithm for estimating velocity, distance, and approximating trajectory. The proposed algorithm is computationally inexpensive, agnostic of wheel camber, not sensitive to sensor placement, and can be embedded for real-time implementations. The research is conducted under Griffith University Ethics (GU Ref No: 2016/294).


Tuning of PID controller based on Fruit Fly Optimization Algorithm

The Proportional - Integral - Derivative (PID) controllers are one of the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. PID tuning is the key issue in the design of PID controllers and most of the tuning processes are implemented manually resulting in difficulty and time consuming. To enhance the capabilities of traditional PID parameters tuning techniques, modern heuristics approaches, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are employed recent years. In this paper, a novel tuning method based on Fruit Fly Optimization Algorithm (FOA) is proposed to optimize PID controller parameters. Each fruit fly's position represents a candidate solution for PID parameters. When the fruit fly swarm flies towards one location, it is treated as the evolution of each iterative swarm. After hundreds of iteration, the tuning results - the best PID controller parameters can be obtained. The main advantages of the proposed method include ease of implementation, stable convergence characteristic, large searching range, ease of transformation of such concept into program code and ease of understanding. Simulation results demonstrate that the FOA-Based optimized PID (FOA - PID) controller is with the capability of providing satisfactory closed - loop performance.


Event detection over twitter social media streams

In recent years, microblogs have become an important source for reporting real-world events. A real-world occurrence reported in microblogs is also called a social event. Social events may hold critical materials that describe the situations during a crisis. In real applications, such as crisis management and decision making, monitoring the critical events over social streams will enable watch officers to analyze a whole situation that is a composite event, and make the right decision based on the detailed contexts such as what is happening, where an event is happening, and who are involved. Although there has been significant research effort on detecting a target event in social networks based on a single source, in crisis, we often want to analyze the composite events contributed by different social users. So far, the problem of integrating ambiguous views from different users is not well investigated. To address this issue, we propose a novel framework to detect composite social events over streams, which fully exploits the information of social data over multiple dimensions. Specifically, we first propose a graphical model called location-time constrained topic (LTT) to capture the content, time, and location of social messages. Using LTT, a social message is represented as a probability distribution over a set of topics by inference, and the similarity between two messages is measured by the distance between their distributions. Then, the events are identified by conducting efficient similarity joins over social media streams. To accelerate the similarity join, we also propose a variable dimensional extendible hash over social streams. We have conducted extensive experiments to prove the high effectiveness and efficiency of the proposed approach.


Kullback-Leibler Divergence Constrained Distributionally Robust Optimization

In this paper we study distributionally robust optimization (DRO) problems where the ambiguity set of the probability distribution is defined by the Kullback-Leibler (KL) divergence. We consider DRO problems where the ambiguity is in the objective function, which takes a form of an expectation, and show that the resulted minimax DRO problems can be formulated as a one-layer convex minimization problem. We also consider DRO problems where the ambiguity is in the constraint. We show that ambiguous expectation-constrained programs may be reformulated as a one-layer convex optimization problem that takes the form of the Benstein approximation of Nemirovski and Shapiro (2006). We further consider distributionally robust probabilistic programs. We show that the optimal solution of a probability minimization problem is also optimal for the distributionally robust version of the same problem, and also show that the ambiguous chance-constrained programs (CCPs) may be reformulated as the original CCP with an adjusted confidence level. A number of examples and special cases are also discussed in the paper to show that the reformulated problems may take simple forms that can be solved easily. The main contribution of the paper is to show that the KL divergence constrained DRO problems are often of the same complexity as their original stochastic programming problems and, thus, KL divergence appears a good candidate in modeling distribution ambiguities in mathematical programming.


Measuring the effectiveness of answers in Yahoo! Answers

Purpose – This study investigates the ways in which effectiveness of answers in Yahoo! Answers, one of the largest community question answering sites (CQAs), is related to question types and answerer reputation. Effective answers are defined as those that are detailed, readable, superior in quality, and contributed promptly. Five question types that were studied include factoid, list, definition, complex interactive, and opinion. Answerer reputation refers to the past track record of answerers in the community. Design/Methodology/Approach – The dataset comprises 1,459 answers posted in Yahoo! Answers in response to 464 questions that were distributed across the five question types. The analysis was done using factorial analysis of variance. Findings – The results indicate that factoid, definition and opinion questions were comparable in attracting high quality as well as readable answers. Although reputed answerers generally fared better in offering detailed and high quality answers, novices were found to submit more readable responses. Moreover, novices were more prompt in answering factoid, list and definition questions. Originality/value – By analyzing variations in answer effectiveness with a twin-focus on question types and answerer reputation, this study explores a strand of CQA research that has hitherto received limited attention. The findings offer insights to users and designers of CQAs.


An Inference Model for Semantic Entailment in Natural Language

Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. This is a fundamental problem in natural language understanding that provides a broad framework for studying language variability and has a large number of applications. We present a principled approach to this problem that builds on inducing rerepresentations of text snippets into a hierarchical knowledge representation along with a sound inferential mechanism that makes use of it to prove semantic entailment.


The social role of social media: the case of Chennai rains-2015

Social media has altered the way individuals communicate in present scenario. Individuals feel more connected on Facebook and Twitter with greater communication freedom to chat, share pictures, and videos. Hence, social media is widely employed by various companies to promote their product and services and establish better customer relationships. Owing to the increasing popularity of these social media platforms, their usage is also expanding significantly. Various studies have discussed the importance of social media in the corporate world for effective marketing communication, customer relationships, and firm performance, but no studies have focused on the social role of social media, i.e., in disaster resilience in India. Various academicians and practitioners have advocated the importance and use of social media in disaster resilience. This article focuses on the role that social media can play during the time of natural disasters, with the help of the recent case of Chennai floods in India. This study provides a better understanding about the role social media can play in natural disaster resilience in Indian context.


A New Algorithm for Detecting Text Line in Handwritten Documents

Curvilinear text line detection and segmentation in handwritten documents is a significant challenge for handwriting recognition. Given no prior knowledge of script, we model text line detection as an image segmentation problem by enhancing text line structure using a Gaussian window, and adopting the level set method to evolve text line boundaries. Experiments show that the proposed method achieves high accuracy for detecting text lines in both handwritten and machine printed documents with many scripts.


Modelling and control of a 2-DOF planar parallel manipulator for semiconductor packaging systems

A novel direct-drive planar parallel manipulator for high-speed and high-precision semiconductor packaging systems is presented. High precision kinematics design, significant reduction on moving mass and driving power of the actuators over traditional XY motion stages are the benefits of the proposed manipulator. The mathematical model of the manipulator is obtained using the Newton-Euler method and a practical model-based control design approach is employed to design the PID computed-torque controller. Experimental results demonstrate that the proposed planar parallel manipulator has significant improvements on motion performance in terms of positioning accuracy, settling time and stability when compared with traditional XY stages. This shows that the proposed planar parallel manipulator can provide a superior alternative for replacing traditional XY motion stages in high precision low-payload applications


BuildingRules: a trigger-action based system to manage complex commercial buildings

Modern Building Management Systems (BMSs) provide limited amount of control to its occupants, and typically allow only the facility manager to set the building policies. BuildingRules let occupants to customise their office spaces using trigger-action programming. In order to accomplish this task, BuildingRules automatically detects conflicts among the policies expressed by the users using a SMT based logic. We tested our system with 23 users across 17 days in a virtual office building, and evaluate the effectiveness and scalability of the system.


Social Media, News and Political Information during the US Election: Was Polarizing Content Concentrated in Swing States?

US voters shared large volumes of polarizing political news and information in the form of links to content from Russian, WikiLeaks and junk news sources. Was this low quality political information distributed evenly around the country, or concentrated in swing states and particular parts of the country? In this data memo we apply a tested dictionary of sources about political news and information being shared over Twitter over a ten day period around the 2016 Presidential Election. Using self-reported location information, we place a third of users by state and create a simple index for the distribution of polarizing content around the country. We find that (1) nationally, Twitter users got more misinformation, polarizing and conspiratorial content than professionally produced news. (2) Users in some states, however, shared more polarizing political news and information than users in other states. (3) Average levels of misinformation were higher in swing states than in uncontested states, even when weighted for the relative size of the user population in each state. We conclude with some observations about the impact of strategically disseminated polarizing information on public life. COMPUTATIONAL PROPAGANDA AND THE 2016 US ELECTION Social media plays an important role in the circulation of ideas about public policy and politics. Political actors and governments worldwide are deploying both people and algorithms to shape public life. Bots are pieces of software intended to perform simple, repetitive, and robotic tasks. They can perform legitimate tasks on social media like delivering news and information—real news as well as junk—or undertake malicious activities like spamming, harassment and hate speech. Whatever their uses, bots on social media platforms are able to rapidly deploy messages, replicate themselves, and pass as human users. They are also a pernicious means of spreading junk news over social networks of family and friends. Computational propaganda flourished during the 2016 US Presidential Election. There were numerous examples of misinformation distributed online with the intention of misleading voters or simply earning a profit. Multiple media reports have investigated how “fake news” may have propelled Donald J. Trump to victory. What kinds of political news and information were social media users in the United States sharing in advance of voting day? How much of it was extremist, sensationalist, conspiratorial, masked commentary, fake, or some other form of junk news? Was this misleading information concentrated in the battleground states where the margins of victory for candidates had big consequences for electoral outcomes? SOCIAL MEDIA AND JUNK NEWS Junk news, widely distributed over social media platforms, can in many cases be considered to be a form of computational propaganda. Social media platforms have served significant volumes of fake, sensational, and other forms of junk news at sensitive moments in public life, though most platforms reveal little about how much of this content there is or what its impact on users may be. The World Economic Forum recently identified the rapid spread of misinformation online as among the top 10 perils to society. Prior research has found that social media favors sensationalist content, regardless of whether the content has been fact checked or is from a reliable source. When junk news is backed by automation, either through dissemination algorithms that the platform operators cannot fully explain or through political bots that promote content in a preprogrammed way, political actors have a powerful set of tools for computational propaganda. Both state and non-state political actors deliberately manipulate and amplify non-factual information online. Fake news websites deliberately publish misleading, deceptive or incorrect information purporting to be real news for political, economic or cultural gain. These sites often rely on social media to attract web traffic and drive engagement. Both fake news websites and political bots are crucial tools in digital propaganda attacks—they aim to influence conversations, demobilize opposition and generate false support. SAMPLING AND METHOD Our analysis is based on a dataset of 22,117,221 tweets collected between November 1-11, 2016, that contained hashtags related to politics and the election in the US. Our previous analyses have been based on samples of political conversation, over Twitter that used hashtags that were relevant to the US election as a whole. In this analysis, we selected users who provided some evidence of physical location across the United States in their profiles. Within our initial sample, approximately 7,083,691 tweets, 32 percent of the total


Uptane: Security and Customizability of Software Updates for Vehicles

A widely accepted premise is that complex software frequently contains bugs that can be remotely exploited by attackers. When this software is on an electronic control unit (ECU) in a vehicle, exploitation of these bugs can have life or death consequences. Since software for vehicles is likely to proliferate and grow more complex in time, the number of exploitable vulnerabilities will increase. As a result, manufacturers are keenly aware of the need to quickly and efficiently deploy updates so that software vulnerabilities can be remedied as soon as possible.


Automated classification of security requirements

Requirement engineers are not able to elicit and analyze the security requirements clearly, that are essential for the development of secure and reliable software. Proper identification of security requirements present in the Software Requirement Specification (SRS) document has been a problem being faced by the developers. As a result, they are not able to deliver the software free from threats and vulnerabilities. Thus, in this paper, we intend to mine the descriptions of security requirements present in the SRS document and thereafter develop the classification models. The security-based descriptions are analyzed using text mining techniques and are then classified into four types of security requirements viz. authentication-authorization, access control, cryptography-encryption and data integrity using J48 decision tree method. Corresponding to each type of security requirement, a prediction model has been developed. The effectiveness of the prediction models is evaluated against requirement specifications collected from 15 projects which have been developed by MS students at DePaul University. The result analysis indicated that all the four models have performed very well in predicting their respective type of security requirements.


CitySpectrum: a non-negative tensor factorization approach

People flow at a citywide level is in a mixed state with several basic patterns (e.g. commuting, working, commercial), and it is therefore difficult to extract useful information from such a mixture of patterns directly. In this paper, we proposed a novel tensor factorization approach to modeling city dynamics in a basic life pattern space (CitySpectral Space). To obtain the CitySpectrum, we utilized Non-negative Tensor Factorization (NTF) to decompose a people flow tensor into basic life pattern tensors, described by three bases i.e. the intensity variation among different regions, the time-of-day and the sample days. We apply our approach to a big mobile phone GPS log dataset (containing 1.6 million users) to model the fluctuation in people flow before and after the Great East Japan Earthquake from a CitySpectral perspective. In addition, our framework is extensible to a variety of auxiliary spatial-temporal data. We parametrize a people flow with a spatial distribution of the Points of Interest (POIs) to quantitatively analyze the relationship between human mobility and POI distribution. Based on the parametric people flow, we propose a spectral approach for a site-selection recommendation and people flow simulation in another similar area using POI distribution.


A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning

The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, ABC has an insufficiency regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose an improved ABC method called as CABC where a modified search equation is applied to generate a candidate solution to improve the search ability of ABC. Furthermore, we use the orthogonal experimental design (OED) to form an orthogonal learning (OL) strategy for variant ABCs to discover more useful information from the search experiences. Owing to OED's good character of sampling a small number of well representative combinations for testing, the OL strategy can construct a more promising and efficient candidate solution. In this paper, the OL strategy is applied to three versions of ABC, i.e., the standard ABC, global-best-guided ABC (GABC), and CABC, which yields OABC, OGABC, and OCABC, respectively. The experimental results on a set of 22 benchmark functions demonstrate the effectiveness and efficiency of the modified search equation and the OL strategy. The comparisons with some other ABCs and several state-of-the-art algorithms show that the proposed algorithms significantly improve the performance of ABC. Moreover, OCABC offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all the test functions.


Large-scale cluster management at Google with Borg

Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from many thousands of different applications, across a number of clusters each with up to tens of thousands of machines.
 It achieves high utilization by combining admission control, efficient task-packing, over-commitment, and machine sharing with process-level performance isolation. It supports high-availability applications with runtime features that minimize fault-recovery time, and scheduling policies that reduce the probability of correlated failures. Borg simplifies life for its users by offering a declarative job specification language, name service integration, real-time job monitoring, and tools to analyze and simulate system behavior.
 We present a summary of the Borg system architecture and features, important design decisions, a quantitative analysis of some of its policy decisions, and a qualitative examination of lessons learned from a decade of operational experience with it.


The tail at scale

Software techniques that tolerate latency variability are vital to building responsive large-scale Web services.


Characterizing Machines and Workloads on a Google Cluster

Cloud computing offers high scalability, flexibility and cost-effectiveness to meet emerging computing requirements. Understanding the characteristics of real workloads on a large production cloud cluster benefits not only cloud service providers but also researchers and daily users. This paper studies a large-scale Google cluster usage trace dataset and characterizes how the machines in the cluster are managed and the workloads submitted during a 29-day period behave. We focus on the frequency and pattern of machine maintenance events, job- and task-level workload behavior, and how the overall cluster resources are utilized.


Characterizing Cloud Applications on a Google Data Center

In this paper, we characterize Google applications, based on a one-month Google trace with over 650k jobs running across over 12000 heterogeneous hosts from a Google data center. On one hand, we carefully compute the valuable statistics about task events and resource utilization for Google applications, based on various types of resources (such as CPU, memory) and execution types (e.g., whether they can run batch tasks or not). Resource utilization per application is observed with an extremely typical Pareto principle. On the other hand, we classify applications via a K-means clustering algorithm with optimized number of sets, based on task events and resource usage. The number of applications in the K-means clustering sets follows a Pareto-similar distribution. We believe our work is very interesting and valuable for the further investigation of Cloud environment.


The Autism Diagnostic Observation Schedule, Toddler Module: Standardized Severity Scores.

Standardized calibrated severity scores (CSS) have been created for Autism Diagnostic Observation Schedule, 2nd edition (ADOS-2) Modules 1-4 as a metric of the relative severity of autism-specific behaviors. Total and domain CSS were created for the Toddler Module to facilitate comparison to other modules. Analyses included 388 children with ASD age 12-30 months and were replicated on 435 repeated assessments from 127 children with ASD. Compared to raw scores, associations between total and domain CSS and participant characteristics were reduced in the original sample. Verbal IQ effects on Social Affect-CSS were not reduced in the replication sample. Toddler Module CSS increases comparability of ADOS-2 scores across modules and allows studies of symptom trajectories to extend to earlier ages.


Arx : A DBMS with Semantically Secure Encryption Rishabh

In recent years, encrypted databases have emerged as a promising direction that provides data confidentiality without sacrificing functionality: queries are executed on encrypted data. However, existing practical proposals rely on a set of weak encryption schemes that have been shown to leak sensitive data. In this paper, we propose Arx, a practical and functionally rich database system that encrypts the data only with semantically secure encryption schemes. We show that Arx supports real applications such as ShareLaTeX and a health data cloud provider with a modest performance overhead.


Design and Vlsi Implementation of Anti- Collision Enabled Robot Processor Using Rfid Technology

RFID is a low power wireless emerging technology which has given rise to highly promising applications in real life. It can be employed for robot navigation. In multi-robot environment, when many robots are moving in the same workspace, there is a possibility of their physical collision with themselves as well as with physical objects. In the present work, we have proposed and developed a processor incorporating smart algorithm for avoiding such collisions with the help of RFID technology and implemented it by using VHDL. The design procedure and the simulated results are very useful in designing and implementing a practical RFID system. The RTL schematic view of the processor is achieved by successfully synthesizing the proposed design.


Text Steganography in SMS

One of the services used in mobile phone is the short message service (SMS) which is widely used by the public in all parts of the world especially in Asia and Europe. This service enables people to write and exchange short messages via mobile phone. Due to the limited size of SMS, lack of a proper keyboard on the mobile phone and to improve the speed of typing, new abbreviations have been invented for different words and phrases which has lead to the invention of a new language called SMS-texting. One of the main issues in communication is information security and privacy. There are many methods for secret communication and many researchers are working on steganography. In steganography the data is hidden in a cover media such as picture or text. The present paper offers a new method for secret exchange of information through SMS by using and developing abbreviation text steganography with the use of the invented language of SMS-texting. This project has been implemented by J2ME (Java 2 Micro Edition) programming language and tested on a Nokia N71 mobile phone.


Synchronization and cell search algorithms in 3GPP long term evolution systems (FDD mode)

In this paper initial downlink synchronization (sync) and cell identification algorithms for the 3rd generation partnership project (3GPP) long term evolution (LTE) systems are presented. The frequency division duplex (FDD) mode is used in the downlink radio frame structure. A user equipment digital receiver architecture is proposed. The orthogonality of signals in orthogonal frequency division multiplexing (OFDM) may be lost due to some impairments such as frequency, time, and phase offsets. Therefore, major parts of this research are involved in restoring the orthogonality at the user equipment (UE) by using some techniques to estimate the sync parameters, and to detect cell identity among candidate cells based on sync signals. A Farrow structure interpolator is used to compensate the fractional timing offset. Both inter site synchronous and inter site asynchronous networks are presented. Computer simulations are used to demonstrate the performance of the proposed schemes with multipath Rayleigh fading channel, frequency offsets, timing offsets, and additive white Gaussian noise (AWGN). Results show high probability of cell identification in a very short time 20 ms in both multi cell model scenarios, especially when multiple input multiple output (MIMO) technique, oversampling at the UE, and high order Farrow structure interpolator are used. Key-Words: 3GPP LTE, OFDM, MIMO, Frequency and Time Synchronization, Cell Search


Social norms and human cooperation

The existence of social norms is one of the big unsolved problems in social cognitive science. Although no other concept is invoked more frequently in the social sciences, we still know little about how social norms are formed, the forces determining their content, and the cognitive and emotional requirements that enable a species to establish and enforce social norms. In recent years, there has been substantial progress, however, on how cooperation norms are enforced. Here we review evidence showing that sanctions are decisive for norm enforcement, and that they are largely driven by non-selfish motives. Moreover, the explicit study of sanctioning behavior provides instruments for measuring social norms and has also led to deeper insights into the proximate and ultimate forces behind human cooperation.


Circular Pulley Versus Variable Radius Pulley: Optimal Design Methodologies and Dynamic Characteristics Analysis

Human-centered robotics has received growing interest in low-impedance actuations. In particular, pneumatic artificial muscles (PAMs) provide compliance and high force-to-weight ratio, which allow for safe actuation. However, several performance drawbacks prevent PAMs from being more pervasive. Although many approaches have been proposed to overcome the low control bandwidth of PAMs, some limitations of PAMs, such as restricted workspace and torque capacity, remain to be addressed. This paper analyzes the characteristics and limitations of PAMs-driven joints and subsequently provides an optimization strategy for circular pulleys (CPs) in order to improve joint torque capacity over a large workspace. In addition to CPs, this paper proposes a design methodology to synthesize a pair of variable radius pulleys (VRPs) for further improvement. Simulation and experimental results show that newly synthesized VRPs significantly improve torque capacity in the enlarged workspace without loss of dynamic performance. Finally, the characteristics of CPs and VRPs are discussed in terms of physical human-robot interaction.


Privacy-Preserving Profile Matching for Proximity-Based Mobile Social Networking

Proximity-based mobile social networking (PMSN) refers to the social interaction among physically proximate mobile users. The first step toward effective PMSN is for mobile users to choose whom to interact with. Profile matching refers to two users comparing their personal profiles and is promising for user selection in PMSN. It, however, conflicts with users' growing privacy concerns about disclosing their personal profiles to complete strangers. This paper tackles this open challenge by designing novel fine-grained private matching protocols. Our protocols enable two users to perform profile matching without disclosing any information about their profiles beyond the comparison result. In contrast to existing coarse-grained private matching schemes for PMSN, our protocols allow finer differentiation between PMSN users and can support a wide range of matching metrics at different privacy levels. The performance of our protocols is thoroughly analyzed and evaluated via real smartphone experiments.


A Survey of Machine Learning Techniques for Spam Filtering

Email spam or junk e-mail (unwanted e-mail “usually of a commercial nature sent out in bulk”) is one of the major problems of the today's Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is an important and popular one. Common uses for mail filters include organizing incoming email and removal of spam and computer viruses. A less common use is to inspect outgoing email at some companies to ensure that employees comply with appropriate laws. Users might also employ a mail filter to prioritize messages, and to sort them into folders based on subject matter or other criteria. Mail filters can be installed by the user, either as separate programs, or as part of their email program (email client). In email programs, users can make personal, "manual" filters that then automatically filter mail according to the chosen criteria. In this paper, we present a survey of the performance of five commonly used machine learning methods in spam filtering. Most email programs now also have an automatic spam filtering function.


Splitting Compounds by Semantic Analogy

Compounding is a highly productive word-formation process in some languages that is often problematic for natural language processing applications. In this paper, we investigate whether distributional semantics in the form of word embeddings can enable a deeper, i.e., more knowledge-rich, processing of compounds than the standard string-based methods. We present an unsupervised approach that exploits regularities in the semantic vector space (based on analogies such as “bookshop is to shop as bookshelf is to shelf”) to produce compound analyses of high quality. A subsequent compound splitting algorithm based on these analyses is highly effective, particularly for ambiguous compounds. German to English machine translation experiments show that this semantic analogy-based compound splitter leads to better translations than a commonly used frequency-based method.


Polymorphic Encryption and Pseudonymisation for Personalised Healthcare

Polymorphic encryption and Pseudonymisation, abbreviated as PEP, form a novel approach for the management of sensitive personal data, especially in health care. Traditional encryption is rather rigid: once encrypted, only one key can be used to decrypt the data. This rigidity is becoming an every greater problem in the context of big data analytics, where different parties who wish to investigate part of an encrypted data set all need the one key for decryption. Polymorphic encryption is a new cryptographic technique that solves these problems. Together with the associated technique of polymorphic pseudonymi-sation new security and privacy guarantees can be given which are essential in areas such as (personalised) healthcare, medical data collection via self-measurement apps, and more generally in privacy-friendly identity management and data analytics. The key ideas of polymorphic encryption are: 1. Directly after generation, data can be encrypted in a 'polymorphic' manner and stored at a (cloud) storage facility in such a way that the storage provider cannot get access. Crucially, there is no need to a priori fix who gets to see the data, so that the data can immediately be protected. For instance a PEP-enabled self-measurement device will store all its measurement data in polymorphically encrypted form in a back-end data base. 2. Later on it can be decided who can decrypt the data. This decision will be made on the basis of a policy, in which the data subject should play a key role. The user of the PEP-enabled device can, for instance, decide that doctors X, Y, Z may at some stage decrypt to use the data in their diagnosis, or medical researcher groups A, B, C may use it for their investigations, or third parties U, V, W may use it for additional services, etc. 3. This 'tweaking' of the encrypted data to make it decryptable by a specific party can be done in a blind manner. It will have to be done by a trusted party who knows how to tweak the ciphertext for whom. This PEP technology can provide the necessary security and privacy infrastructure for big data analytics. People can entrust their data in polymorphically encrypted form, and each time decide later to make (parts of) it available (de-cryptable) for specific parties, for specific analysis purposes. In this way users remain in control, and can monitor which of their data is used where by whom for which purposes. The …


Video Captioning by Adversarial LSTM

In this paper, we propose a novel approach to video captioning based on adversarial learning and long short-term memory (LSTM). With this solution concept, we aim at compensating for the deficiencies of LSTM-based video captioning methods that generally show potential to effectively handle temporal nature of video data when generating captions but also typically suffer from exponential error accumulation. Specifically, we adopt a standard generative adversarial network (GAN) architecture, characterized by an interplay of two competing processes: a “generator” that generates textual sentences given the visual content of a video and a “discriminator” that controls the accuracy of the generated sentences. The discriminator acts as an “adversary” toward the generator, and with its controlling mechanism, it helps the generator to become more accurate. For the generator module, we take an existing video captioning concept using LSTM network. For the discriminator, we propose a novel realization specifically tuned for the video captioning problem and taking both the sentences and video features as input. This leads to our proposed LSTM–GAN system architecture, for which we show experimentally to significantly outperform the existing methods on standard public datasets.


Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset

We investigate the problem of readeraware multi-document summarization (RA-MDS) and introduce a new dataset for this problem. To tackle RA-MDS, we extend a variational auto-encodes (VAEs) based MDS framework by jointly considering news documents and reader comments. To conduct evaluation for summarization performance, we prepare a new dataset. We describe the methods for data collection, aspect annotation, and summary writing as well as scrutinizing by experts. Experimental results show that reader comments can improve the summarization performance, which also demonstrates the usefulness of the proposed dataset. The annotated dataset for RA-MDS is available online1.


Bee Colony Optimization (BCO)

Swarm Intelligence is the part of Artificial Intel ligence based on study of actions of individuals in various decentralized sys tems. The Bee Colony Optimization (BCO) metaheuristic has been introduce fairly recently as a new direction in the field of Swarm Intelligence. Artif icial bees represent agents, which collaboratively solve complex combinatorial optimiz ation problem. The chapter presents a classification and analysis of the resul ts achieved using Bee Colony Optimization (BCO) to model complex engineering and management processes. The primary goal of this chapter is to acquaint readers with the basic principles of Bee Colony Optimization, as well as to indicate potenti al BCO applications in engineering and management.


Big data needs approximate computing: technical perspective

以容忍。还有一点让人印象深刻: 对于不同的程序,这些提升和能 耗降低并不需要使用不同的加速 器实现方式——它们通过相同的 近似加速器得到,那些加速器可 用于所有程序中所有类型的可近 似代码区。 除了文中描述的之外,通过让 近似计算引擎在明显低得多的电压 下运行,还能节约更多的能源。业 界增长的真正引擎——登纳德缩放 (Dennard scaling)——允许晶体 管减少尺寸,同时消耗更少的能源, 得到更高的性能。但是登纳德缩放 正趋近极限。设备的尺寸已经缩减 到了原子级别。而且,在不牺牲设 备可靠性的情况下,无法继续显著 降低电压。新技术会带来不错的能 耗性能,但由于需要利用冗余来补 偿它们的不可靠性,这一优势被抵 消了。因此,对于传统的交易系统 而言,未来的技术不大可能大幅提 升其能耗性能。然而,对于那些生 成近似结果的系统(比如作者描述 的系统),需要的冗余可能有所不 同且要简单得多。对于这些系统类 型,低电压运行是可行的。 未 来, 当 我 们 使 用 缩 小 的 CMOS 和后硅技术,利用海量计算 处理世上产生的海量数据时,近似 计算似乎是我们最有可能取得成功 的希望。下面的文章就是为实现此 目标所需开展的工作的一个很好的 例子。


Torus data center network with smart flow control enabled by hybrid optoelectronic routers [Invited]

A torus-topology photonic data center network with smart flow control is presented, where optical packet switching (OPS), optical circuit switching (OCS), and virtual OCS (VOCS) schemes are all enabled on a unified hardware platform via the combination of hybrid optoelectronic routers (HOPR) and a centralized network controller. An upgraded HOPR is being developed with the goal of handling 100 Gbps optical packets with a high energy efficiency of 90 mW/Gbps and low latency of less than 100 ns. The architecture of HOPR and its enabling technologies are reviewed, including a label processor, optical switch, and optoelectronic shared buffer. We also explain the concept and operation mechanisms of both the data and control planes in the hybrid OPS/OCS/VOCS torus network. The performance of these three transmission modes is evaluated by numerical simulations for a network composed of 4096 HOPRs.


ANNarchy: a code generation approach to neural simulations on parallel hardware

Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions.


How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks

Variational autoencoders are a powerful framework for unsupervised learning. However, previous work has been restricted to shallow models with one or two layers of fully factorized stochastic latent variables, limiting the flexibility of the latent representation. We propose three advances in training algorithms of variational autoencoders, for the first time allowing to train deep models of up to five stochastic layers, (1) using a structure similar to the Ladder network as the inference model, (2) warm-up period to support stochastic units staying active in early training, and (3) use of batch normalization. Using these improvements we show state-of-the-art log-likelihood results for generative modeling on several benchmark datasets.


Wide Residual Networks

Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layerdeep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at https: //github.com/szagoruyko/wide-residual-networks.


Deep learning in neural networks: An overview

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.


Video games do affect social outcomes: a meta-analytic review of the effects of violent and prosocial video game play.

Whether video game play affects social behavior is a topic of debate. Many argue that aggression and helping are affected by video game play, whereas this stance is disputed by others. The present research provides a meta-analytical test of the idea that depending on their content, video games do affect social outcomes. Data from 98 independent studies with 36,965 participants revealed that for both violent video games and prosocial video games, there was a significant association with social outcomes. Whereas violent video games increase aggression and aggression-related variables and decrease prosocial outcomes, prosocial video games have the opposite effects. These effects were reliable across experimental, correlational, and longitudinal studies, indicating that video game exposure causally affects social outcomes and that there are both short- and long-term effects.


A Privacy Preservation Model for Facebook-Style Social Network Systems

Recent years have seen unprecedented growth in the popularity of social network systems, with Facebook being an archetypical example. The access control paradigm behind the privacy preservation mechanism of Facebook is distinctly different from such existing access control paradigms as Discretionary Access Control, Role-Based Access Control, Capability Systems, and Trust Management Systems. This work takes a first step in deepening the understanding of this access control paradigm, by proposing an access control model that formalizes and generalizes the privacy preservation mechanism of Facebook. The model can be instantiated into a family of Facebook-style social network systems, each with a recognizably different access control mechanism, so that Facebook is but one instantiation of the model. We also demonstrate that the model can be instantiated to express policies that are not currently supported by Facebook but possess rich and natural social significance. This work thus delineates the design space of privacy preservation mechanisms for Facebook-style social network systems, and lays out a formal framework for policy analysis in these systems.


Adversarial machine learning

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques.


Voxel-Based Morphometry—The Methods

At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.


Efficient Algorithms for Convolutional Sparse Representations

When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but results in a representation that is multi-valued and not optimized with respect to the entire image. An alternative representation structure is provided by a convolutional sparse representation, in which a sparse representation of an entire image is computed by replacing the linear combination of a set of dictionary vectors by the sum of a set of convolutions with dictionary filters. The resulting representation is both single-valued and jointly optimized over the entire image. While this form of a sparse representation has been applied to a variety of problems in signal and image processing and computer vision, the computational expense of the corresponding optimization problems has restricted application to relatively small signals and images. This paper presents new, efficient algorithms that substantially improve on the performance of other recent methods, contributing to the development of this type of representation as a practical tool for a wider range of problems.


Blind Trust Online : Experimental Evidence from Baseball Cards

Before reaping transaction cost savings from the Internet, online sellers must credibly communicate the quality of their goods or their reliability of delivery. We use baseball cards as an example to address (1) whether consumers fully understand the risk of online trading, (2) the extent to which consumers employ seller reputation and professional grading to alleviate the risk they are exposed to, and (3) the impact of online trading on the retail market and supporting industries. An experiment is carried out to obtain actual baseball cards from both online and retail markets whose quality is then professionally graded and compared to the prices paid by online buyers for goods with similar claims. Our findings indicate that some uninformed online buyers are misled by non-credible claims of quality; they pay higher prices but do not receive better quality and in fact are defrauded more often. Although a combination of professional grading and reputation mechanisms appear to have the potential to solve the problem, they have only had limited success so far. Data on structural changes in the baseball card industry suggest the problems stemming from buyer misconception may have far-reaching consequences, extending beyond the immediate losses of defrauded consumers to influencing producer and supply decisions. Our findings also suggest that empirical studies using online price data should be careful of what assumptions they are making of online users and what behaviors of buyers and sellers are likely to drive the price data. Department of Economics, University of Maryland, 3115 G Tydings, College Park, MD 20742. Contact Email: jin@econ.umd.edu. We thank Seth Sanders, John List, John Shea, Dan Vincent, David Lucking-Reiley, Larry Ausubel, Peter Cramton, V. Joseph Hotz, Jimmy Chan, Vincent Crawford, attendants in Johns Hopkins UMD conference and participants of UCLA,UCSD and FTC seminars for constructive advice and suggestions. We are grateful to 8 friends who acted as our agents in purchasing baseball cards in retail markets, and numerous sports card store owners for their time and insights regarding the sports card industry. Excellent research assistance from Randy Alexander Moore and Krzysztof Fizyta is gratefully acknowledged. Any remaining errors are ours.


Tail call elimination on the Java Virtual Machine

A problem that often has to be solved by compilers for functional languages targeting the Java Virtual Machine is the elimination of tail calls. This paper explains how we solved it in our Funnel compiler and presents some experimental results about the impact our technique has on both performance and size of the compiled programs.


Vehicle location finder using Global position system and Global System for Mobile

Each year, the number of stolen vehicle is on the rise. Usually, to prevent theft, a physical type countermeasure is used such as padlock, disk break lock and other more which is a preventive action but it is not enough safe. The objective of this study is to create a controllable system that can display the location of a vehicle using Global position system (GPS) to pin point the location and Global System for Mobile (GSM) as a mean for communicating with the vehicle for ease of finding after a theft attempt. The system is made to test the accuracy of the location that is send to the user when the vehicle is in motion and stationary in the city and suburb. The system is made by combining a micro controller with GPS and GSM, then comparing it with other similar device available in the market like Garmin and a reference website to find the radius of error. The study of proposed device begins by studying IEEE journal about alternative product and the vehicle itself. The hardware and program development is done by research and trial and error as the controller do not interact with both module at the same time, after successfully programming both module, it is combined into a single program with addition of interrupt program. The experiment is done in three set of tests so that the system accuracy can be determine when stationary and in motion on vehicle, output controlling is the test to determine if the controller can be made into anti-theft system. The result of the test concludes that the system can provide standard GPS coordinate when requested via Short Message Service (SMS). The system can also be used to control an actuator.


Time is of the essence: improving recency ranking using Twitter data

Realtime web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However, supporting realtime web search introduces two challenges not encountered in non-realtime web search: quickly crawling relevant content and ranking documents with impoverished link and click information. In this paper, we advocate the use of realtime micro-blogging data for addressing both of these problems. We propose a method to use the micro-blogging data stream to detect fresh URLs. We also use micro-blogging data to compute novel and effective features for ranking fresh URLs. We demonstrate these methods improve effective of the portal web search engine for realtime web search.


Mining asynchronous periodic patterns in time series data

Periodicy detection in time series data is a challenging pro blem of great importance in many applications. Most previous work focused on mining synchronous p eriodic patterns and did not recognize misaligned presence of a pattern due to the intervention of r andom noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that ma y be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parametersmin rep andmax dis are employed to specify the minimum number of repetitions th at is required within each segment of non-disrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, th e longest valid subsequence of a pattern is returned. A two phase algorithm is devised to first generate p o ntial periods by distance-based pruning followed by an iterative procedure to derive and validat e candidate patterns and locate the longest valid subsequence. We also show that this algorithm can not o nly provide linear time complexity with respect to the length of the sequence but also achieve space e ffici ncy.