From Manual to Semi-Automatic Semantic Annotation: About Ontology-Based Text Annotation Tools

Semantic Annotation is a basic technology for intelligent content and is bene cial in a wide range of content-oriented intelligent applications, esp. in the area of the Semantic Web. In this paper we present our work in ontology-based semantic annotation, which is embedded in a scenario of a knowledge portal application. Starting with seemingly good and bad manual semantic annotation, we describe our experiences made within the KAinitiative. The experiences gave us the starting point for developing an ergonomic and knowledge base-supported annotation tool. Furthermore, the annotation tool described are currently extended with mechanisms for semi-automatic information-extraction based annotation. Supporting the evolving nature of semantic content we additionally describe our idea of evolving ontologies supporting semantic annotation. This paper has been presented at the COLING-2000 Workshop on Semantic Annotation and Intelligent Content, Centre Universitaire, Luxembourg, 5.-6. August, 2000.


Towards Text Knowledge Engineering

We introduce a methodology for automating the maintenance of domain-specific taxonomies based on natural language text understanding. A given ontology is incrementally updated as new concepts are acquired from real-world texts. The acquisition process is centered around the linguistic and conceptual “quality” of various forms of evidence underlying the generation and refinement of concept hypotheses. On the basis of the quality of evidence, concept hypotheses are ranked according to credibility and the most credible ones are selected for assimilation into the domain knowledge base.


Introduction to WordNet: An On-line Lexical Database

Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list. Unfortunately, there is no obvious alternative, no other simple way for lexicographers to keep track of what has been done or for readers to find the word they are looking for. But a frequent objection to this solution is that finding things on an alphabetical list can be tedious and time-consuming. Many people who would like to refer to a dictionary decide not to bother with it because finding the information would interrupt their work and break their train of thought.


Exploring Students' Computational Thinking Skills in Modeling and Simulation Projects: a Pilot Study

Computational Thinking (CT) is gaining a lot of attention in education. We explored how to discern the occurrences of CT in the projects of 12th grade high school students in the computer science (CS) course. Within the projects, they constructed models and ran simulations of phenomena from other (STEM) disciplines. We examined which CT aspects occurred in students' activities and how to assess students' CT accomplishments. For this purpose we employed a framework based on CT characterizations by Wing [14, 15], CSTA [4] and Comer et al. [3]. We analyzed students' project documentation, survey results and interviews with individual students. The findings indicate that this framework is suitable for detection of occurrences of CT aspects in students' data. Moreover, our preliminary results suggest that the framework is useful in assessment of the quality of the students' CT performance.


Influence and passivity in social media

The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We demonstrate that high popularity does not necessarily imply high influence and vice-versa.


Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

A major challenge in brain tumor treatment planning and quantit ative evaluation is determination of the tumor extent. The noninva s ve magnetic resonance imaging (MRI) technique has emerged as a front-line diag nostic tool for brain tumors without ionizing radiation. Manual segmentation of brain t umor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator’s experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, w e propose a fully automatic method for brain tumor segmentation, which is develo ped using U-Net based deep convolutional networks. Our method was evalu ated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cro svalidation has shown that our method can obtain promising segmentation effi-


COMPENDIUM : a text summarisation tool for generating summaries of multiple purposes , domains , and genres

In this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, singleand multi-document summaries can be produced; as the output, the summaries can be extractive or abstractiveoriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.


A low noise readout circuit for integrated electrochemical biosensor arrays

This paper presents a low noise electrochemical interface circuit that is tuned to the needs of protein-based biosensor arrays and compatible with the formation of fully integrated biochemical microsystems. The circuit includes an integrated potentiostat and highly sensitive amperometric readout amplifier. It achieves good noise performance while supporting biosensor output currents from 10 pA to 10 /spl mu/A to suit a wide range of sensitivities and electrode areas. Implemented in 0.5 /spl mu/m CMOS process, it is compatible with a post-CMOS process to realize an integrated biosensor array.


A meta-analytic review of gender differences in perceptions of sexual harassment.

Research on gender differences in perceptions of sexual harassment informs an ongoing legal debate regarding the use of a reasonable person standard instead of a reasonable woman standard to evaluate sexual harassment claims. The authors report a meta-analysis of 62 studies of gender differences in harassment perceptions. An earlier quantitative review combined all types of social-sexual behaviors for a single meta-analysis; the purpose of this study was to investigate whether the magnitude of the female-male difference varies by type of behavior. An overall standardized mean difference of 0.30 was found, suggesting that women perceive a broader range of social-sexual behaviors as harassing. However, the meta-analysis also found that the female-male difference was larger for behaviors that involve hostile work environment harassment, derogatory attitudes toward women, dating pressure, or physical sexual contact than sexual propositions or sexual coercion.


Deep Learning and Data Labeling for Medical Applications

This study addresses the recognition problem of the HEp-2 cell using indirect immunofluorescent (IIF) image analysis, which can facilitate the diagnosis of many autoimmune diseases by finding antibodies in the patient serum. Recently, a lot of automatic HEp-2 cell classification strategies including both shallow and deep methods have been developed, wherein the deep Convolutional Neural Networks (CNNs) have been proven to achieve impressive performance. However, the deep CNNs in general requires a fixed size of image as the input. In order to conquer the limitation of the fixed size problem, a spatial pyramid pooling (SPP) strategy has been proposed in general object recognition and detection. The SPP-net usually exploit max pooling strategies for aggregating all activated status of a specific neuron in a predefined spatial region by only taking the maximum activation, which achieved superior performance compared with mean pooling strategy in the traditional state-of-the-art coding methods such as sparse coding, linear locality-constrained coding and so on. However, the max pooling strategy in SPP-net only retains the strongest activated pattern, and would completely ignore the frequency: an important signature for identifying different types of images, of the activated patterns. Therefore, this study explores a generalized spatial pooling strategy, called K-support spatial pooling, in deep CNNs by integrating not only the maximum activated magnitude but also the response magnitude of the relatively activated patterns of a specific neuron together. This proposed K-support spatial pooling strategy in deep CNNs combines the popularly applied mean and max pooling methods, and then avoid awfully emphasizing of the maximum activation but preferring a group of activations in a supported region. The deep CNNs with the proposed K-support spatial pooling is applied for HEp-2 cell classification, and achieve promising performance compared with the state-of-the-art approaches.


Participatory Visualization with Wordle

We discuss the design and usage of ldquoWordle,rdquo a Web-based tool for visualizing text. Wordle creates tag-cloud-like displays that give careful attention to typography, color, and composition. We describe the algorithms used to balance various aesthetic criteria and create the distinctive Wordle layouts. We then present the results of a study of Wordle usage, based both on spontaneous behaviour observed in the wild, and on a large-scale survey of Wordle users. The results suggest that Wordles have become a kind of medium of expression, and that a ldquoparticipatory culturerdquo has arisen around them.


IncludeOS: A Minimal, Resource Efficient Unikernel for Cloud Services

The emergence of cloud computing as a ubiquitous platform for elastically scaling services has generated need and opportunity for new types of operating systems. A service that needs to be both elastic and resource efficient needs A) highly specialized components, and B) to run with minimal resource overhead. Classical general purpose operating systems designed for extensive hardware support are by design far from meeting these requirements. In this paper we present IncludeOS, a single tasking library operating system for cloud services, written from scratch in C++. Key features include: extremely small disk-and memory footprint, efficient asynchronous I/O, OS-library where only what your service needs gets included, and only one device driver by default (virtio). As a test case a bootable disk image consisting of a simple DNS server with OS included is shown to require only 158 kb of disk space and to require 5-20% less CPU-time, depending on hardware, compared to the same binary running on Linux.


FPGA-Based Sensorless PMSM Speed Control Using Reduced-Order Extended Kalman Filters

This paper presents the design and implementation of a field-programmable gate array (FPGA)-based architecture for the speed control of sensorless permanent-magnet synchronous motor (PMSM) drives. For the reduction of computation resources, as well as accuracy improvement in the rotor position estimation, a parallel reduced-order extended Kalman filter (EKF) is proposed in this work. Compared with an EKF, the system order is reduced and the iteration process is greatly simplified, resulting in significant savings of resource utility, while maintaining high estimation performance. The whole control system includes a current-control-and-coordinate-transformation unit, a proportional-integral (PI) speed controller, and other accessory modules, all implemented in a single FPGA chip. A hardware description language is adopted to describe advantageous features of the proposed control system. Moreover, the finite-state-machine method is applied with the purpose to reduce logic elements used in the FPGA chip. The validity of the approach is verified through simulation based on the Modelsim/Simulink cosimulation method. Finally, experimental results are obtained on an FPGA platform with an inverter-fed PMSM to show the feasibility and effectiveness of the proposed system-on-programmable-chip for PMSM drives.


Learning to Remember Rare Events

Despite recent advances, memory-augmented deep neural networks are still limited when it comes to life-long and one-shot learning, especially in remembering rare events. We present a large-scale life-long memory module for use in deep learning. The module exploits fast nearest-neighbor algorithms for efficiency and thus scales to large memory sizes. Except for the nearest-neighbor query, the module is fully differentiable and trained end-to-end with no extra supervision. It operates in a life-long manner, i.e., without the need to reset it during training. Our memory module can be easily added to any part of a supervised neural network. To show its versatility we add it to a number of networks, from simple convolutional ones tested on image classification to deep sequence-to-sequence and recurrent-convolutional models. In all cases, the enhanced network gains the ability to remember and do life-long one-shot learning. Our module remembers training examples shown many thousands of steps in the past and it can successfully generalize from them. We set new state-of-the-art for one-shot learning on the Omniglot dataset and demonstrate, for the first time, life-long one-shot learning in recurrent neural networks on a large-scale machine translation task.


A 1.2 V Low-Noise-Amplifier with Double Feedback for High Gain and Low Noise Figure

Faculdade de Ciências e Tecnologia Departamento de Engenharia Eletrotécnica e de Computadores Mestrado Integrado em Engenharia Eletrotécnica e de Computadores por David Jorge Tiago Amoêdo In this thesis we present a balun low noise amplifier (LNA) in which the gain is boosted using a double feedback structure. The circuit is based in a Balun LNA with noise and distortion cancellation. The LNA is based in two basic stages: common-gate (CG) and common-source (CS). We propose to replace the resistors by active loads, which have two inputs that will be used to provide the feedback (in the CG and CS stages). This proposed methodology will boost the gain and reduce the NF (Noise Figure). Simulation results, with a 130 nm CMOS technology, show that the gain is 19.65 dB and the NF is less than 2.17 dB. The total power dissipation is only 5 mW (since no extra blocks are required), leading to an FOM (Figure of Merit) of 3.13 mW from a nominal 1.2 supply.


AdapNet: Adaptive semantic segmentation in adverse environmental conditions

Robust scene understanding of outdoor environments using passive optical sensors is a onerous and essential task for autonomous navigation. The problem is heavily characterized by changing environmental conditions throughout the day and across seasons. Robots should be equipped with models that are impervious to these factors in order to be operable and more importantly to ensure safety in the real-world. In this paper, we propose a novel semantic segmentation architecture and the convoluted mixture of deep experts (CMoDE) fusion technique that enables a multi-stream deep neural network to learn features from complementary modalities and spectra, each of which are specialized in a subset of the input space. Our model adaptively weighs class-specific features of expert networks based on the scene condition and further learns fused representations to yield robust segmentation. We present results from experimentation on three publicly available datasets that contain diverse conditions including rain, summer, winter, dusk, fall, night and sunset, and show that our approach exceeds the state-of-the-art. In addition, we evaluate the performance of autonomously traversing several kilometres of a forested environment using only the segmentation for perception.


Statistical compression-based models for text classification

Text classification is the task of assigning predefined categories to text documents. It is a common machine learning problem. Statistical text classification that makes use of machine learning methods to learn classification rules are particularly known to be successful in this regard. In this research project we are trying to re-invent the text classification problem with a sound methodology based on statistical data compression technique-the Minimum Message Length (MML) principle. To model the data sequence we have used the Probabilistic Finite State Automata (PFSAs). We propose two approaches for text classification using the MML-PFSAs. We have tested both the approaches with the Enron spam dataset and the results of our empirical evaluation has been recorded in terms of the well known classification measures i.e. recall, precision, accuracy and error. The results indicate good classification accuracy that can be compared with the state of art classifiers.


A SEM-neural network approach for predicting antecedents of m-commerce acceptance

Higher penetration of powerful mobile devices – especially smartphones – and high-speed mobile internet access are leading to better offer and higher levels of usage of these devices in commercial activities, especially among young generations. The purpose of this paper is to determine the key factors that influence consumers’ adoption of mobile commerce. The extended model incorporates basic TAM predictors, such as perceived usefulness and perceived ease of use, but also several external variables, such as trust, mobility, customization and customer involvement. Data was collected from 224 m-commerce consumers. First, structural equation modeling (SEM) was used to determine which variables had signif-


Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease.

Recent evidence suggests that some brain areas act as hubs interconnecting distinct, functionally specialized systems. These nexuses are intriguing because of their potential role in integration and also because they may augment metabolic cascades relevant to brain disease. To identify regions of high connectivity in the human cerebral cortex, we applied a computationally efficient approach to map the degree of intrinsic functional connectivity across the brain. Analysis of two separate functional magnetic resonance imaging datasets (each n = 24) demonstrated hubs throughout heteromodal areas of association cortex. Prominent hubs were located within posterior cingulate, lateral temporal, lateral parietal, and medial/lateral prefrontal cortices. Network analysis revealed that many, but not all, hubs were located within regions previously implicated as components of the default network. A third dataset (n = 12) demonstrated that the locations of hubs were present across passive and active task states, suggesting that they reflect a stable property of cortical network architecture. To obtain an accurate reference map, data were combined across 127 participants to yield a consensus estimate of cortical hubs. Using this consensus estimate, we explored whether the topography of hubs could explain the pattern of vulnerability in Alzheimer's disease (AD) because some models suggest that regions of high activity and metabolism accelerate pathology. Positron emission tomography amyloid imaging in AD (n = 10) compared with older controls (n = 29) showed high amyloid-beta deposition in the locations of cortical hubs consistent with the possibility that hubs, while acting as critical way stations for information processing, may also augment the underlying pathological cascade in AD.


Low-power, folded cascode near rail-to-rail OTA for moderate frequency signal processing

This paper presents a gate-driven, folded-cascode operational transconductance amplifier (FCOTA), operating in strong inversion region. The input core utilizes two complementary PMOS and NMOS input pairs to ensure rail-to-rail input common mode range (ICMR). The cascoded load structure at output port performs the job of current summer and provides enhanced gain due to high output impedance for FCOTA. The proposed OTA has been employed to implement universal biquadratic low-pass, high-pass, band-pass and notch transfer functions, simultaneously available at different nodes of the filter. The proposed FCOTA used dual supply voltage of ± 0.9V and dissipates around 93.6 μW power and provides 80.24 dB open loop gain and gain bandwidth (GBW) of 6.03 MHz. The Cadence VIRTUOSO environment using UMC 0.18 μm CMOS process technology has been used to simulate the proposed circuit.


Cyber-Crimes and their Impacts : A Review 1

In the current era of online processing, maximum of the information is online and prone to cyber threats. There are a huge number of cyber threats and their behavior is difficult to early understanding hence difficult to restrict in the early phases of the cyber attacks. Cyber attacks may have some motivation behind it or may be processed unknowingly. The attacks those are processed knowingly can be considered as the cyber crime and they have serious impacts over the society in the form of economical disrupt, psychological disorder, threat to National defense etc. Restriction of cyber crimes is dependent on proper analysis of their behavior and understanding of their impacts over various levels of society. Therefore, the current manuscript provides the understanding of cyber crimes and their impacts over society with the future trends of cyber crimes.


Evolving Personalized Content for Super Mario Bros Using Grammatical Evolution

Adapting game content to a particular player’s needs and expertise constitutes an important aspect in game design. Most research in this direction has focused on adapting game difficulty to keep the player engaged in the game. Dynamic difficulty adjustment, however, focuses on one aspect of the gameplay experience by adjusting the content to increase or decrease perceived challenge. In this paper, we introduce a method for automatic level generation for the platform game Super Mario Bros using grammatical evolution. The grammatical evolution-based level generator is used to generate player-adapted content by employing an adaptation mechanism as a fitness function in grammatical evolution to optimize the player experience of three emotional states: engagement, frustration and challenge. The fitness functions used are models of player experience constructed in our previous work from crowd-sourced gameplay data collected from over 1500 game sessions.


Efficient matrix completion for seismic data reconstruction

Despite recent developments in improved acquisition, seismic data often remains undersampled along source and receiver coordinates, resulting in incomplete data for key applications such as migration and multiple prediction. We interpret the missing-trace interpolation problem in the context of matrix completion and outline three practical principles for using low-rank optimization techniques to recover seismic data. Specifically, we strive for recovery scenarios wherein the original signal is low rank and the subsampling scheme increases the singular values of the matrix. We employ an optimization program that restores this low rank structure to recover the full volume. Omitting one or more of these principles can lead to poor interpolation results, as we show experimentally. In light of this theory, we compensate for the high-rank behavior of data in the source-receiver domain by employing the midpoint-offset transformation for 2D data and a source-receiver permutation for 3D data to reduce the overall singular values. Simultaneously, in order to work with computationally feasible algorithms for large scale data, we use a factorization-based approach to matrix completion, which significantly speeds up the computations compared to repeated singular value decompositions without reducing the recovery quality. In the context of our theory and experiments, we also show that windowing the data too aggressively can have adverse effects on the recovery quality. To overcome this problem, we carry out our interpolations for each frequency independently while working with the entire frequency slice. The result is a computationally efficient, theoretically motivated framework for interpolating missing-trace data. Our tests on realistic twoand three-dimensional seismic data sets show that our method compares favorably, both in terms of computational speed and recovery quality, to existing curvelet-based and tensor-based techniques. Geophysics This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ©Mitsubishi Electric Research Laboratories, Inc., 2015 201 Broadway, Cambridge, Massachusetts 02139


A Method for Obtaining Digital Signatures and Public-Key Cryptosystems

An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences: (1) Couriers or other secure means are not needed to transmit keys, since a message can be enciphered using an encryption key publicly revealed by the intented recipient. Only he can decipher the message, since only he knows the corresponding decryption key. (2) A message can be “signed” using a privately held decryption key. Anyone can verify this signature using the corresponding publicly revealed encryption key. Signatures cannot be forged, and a signer cannot later deny the validity of his signature. This has obvious applications in “electronic mail” and “electronic funds transfer” systems. A message is encrypted by representing it as a number M, raising M to a publicly specified power e, and then taking the remainder when the result is divided by the publicly specified product, n, of two large secret primer numbers p and q. Decryption is similar; only a different, secret, power d is used, where e * d ≡ 1(mod (p - 1) * (q - 1)). The security of the system rests in part on the difficulty of factoring the published divisor, n.


New directions in cryptography

Two kinds of contemporary developments in crypcommunications over an insecure channel order to use cryptogtography are examined. Widening applications of teleprocessraphy to insure privacy, however, it currently necessary for the ing have given rise to a need for new types of cryptographic communicating parties to share a key which is known to no systems, which minimize the need for secure key distribution one else. This is done by sending the key in advance over some channels and supply the equivalent of a written signature. This secure channel such a private courier or registered mail. A paper suggests ways to solve these currently open problems. private conversation between two people with no prior acquainIt also discusses how the theories of communication and computance is a common occurrence in business, however, and it is tation are beginning to provide the tools to solve cryptographic unrealistic to expect initial business contacts to be postponed problems of long standing. long enough for keys to be transmitted by some physical means. The cost and delay imposed by this key distribution problem is a major barrier to the transfer of business communications


An improved algorithm for computing logarithms over GF(p) and its cryptographic significance (Corresp.)


Constraint Propagation Algorithms for Temporal Reasoning

This paper considers computational aspects of several temporal representation languages. It investigates an interval-based representation, and a point-based one. Computing the consequences of temporal assertions is shown to be computational/y intractable in the interval-based representation, but not in the poinfbased one. However, a fragment of the interval language can be expressed using the point language and benefits from the tractability of the latter.’ The representation of time has been a recurring concern of Artificial Intelligence researchers. Many representation schemes have been proposed for temporal reasoning; of these, one of the most attractive is James Allen’s algebra of temporal intervals [Allen 831. This representation scheme is particularly appealing for its simplicity and for its ease of implementation with constraint propagation algorithms. Reasoners based on this algebra have been put to use in several ways. For example, the planning system of Allen and Koomen [1983] relies heavily on the temporal algebra to perform reasoning about the ordering of actions. Elegant approaches such as this one may be compromised, however, by computational characteristics of the interval algebra. This paper concerns itself with these computational aspects of Allen’s algebra, and of a simpler algebra of time points. Our perspective here is primarily computation-theoretic. We approach the problem of temporal representation by asking questions of complexity and tractability. In this light, this paper examines Allen’s interval algebra, and the simpler algebra of time points. The bulk of the paper establishes some formal results about the temporal algebras. In brief these results are: l Determining consistency of statements in the interval algebra is NP-hard, as is determining all consequences of these statements. Allen’s polynomial-time constraint propagation algorithm is sound but not complete for these tasks. l In contrast, constraint propagation is sound and complete for computing consistency and consequences of assertions in the time point algebra. It operates in O(n3) time and O(n2) space. l A restricted form of the interval algebra can be formulated in terms of the time point algebra. Constraint propagation is sound and complete for this fragment. Throughout the paper, we consider how these formal results affect practical Artificial Intelligence programs. ‘This research was supported in part by the Defense Advanced Research Agency, under contracts NOOOl4-85-C-0079 and N-0001 4-77-C-0378. Projects The Interval Algebra Allen’s interval algebra has been described in detail in [Allen 831. In brief, the elements of the algebra are relations that may exist between intervals of time. Because the algebra allows for indefiniteness in temporal relations, it admits many possible relations between intervals (213 in fact). But all of these relations can be expressed as vectors of definite simple relations, of which there are only thirteen, 2 The thirteen simple relations, whose definitions appear in Figure 1, precisely characterize the relative starting and ending points of two temporal intervals. If the relation between two intervals is completely defined, then it can be exactly described with a simple relation. Alternatively, vectors of simple relations introduce indefiniteness in the description of how two temporal intervals relate. Vectors are interpreted as the disjunction of their constituent simple relations. A BEFORE B B AFTER A G--v* A MEETS B B MET-BY A A ,B /


Natural Language Aggregate Query over RDF Data

Natural language question/answering over RDF data has received widespread attention. Although there have been several studies that have dealt with a small number of aggregate queries, they have many restrictions (i.e., interactive information, controlled question or query template). Thus far, there has been no natural language querying mechanism that can process general aggregate queries over RDF data. Therefore, we propose a framework called NLAQ (Natural Language Aggregate Query). First, we propose a novel algorithm to automatically understand a user’s query intention, which mainly contains semantic relations and aggregations. Second, to build a better bridge between the query intention and RDF data, we propose an extended paraphrase dictionary ED to obtain more candidate mappings for semantic relations, and we introduce a predicate-type adjacent set PT to filter out inappropriate candidate mapping combinations in semantic relations and basic graph patterns. Third, we design a suitable translation plan for each aggregate category and effectively distinguish whether an aggregate item is numeric or not, which will greatly affect the aggregate result. Finally, we conduct extensive experiments over real datasets (QALD benchmark and DBpedia), and the experimental results demonstrate that our solution is effective.


A new method on designing and simulating CNTFET_based ternary gates and arithmetic circuits

This paper presents a new design of three-valued logic gates on the basis of carbon nanotube transistors. Next, the proposed circuit is compared with the existing models of circuits. The simulation results, using HSPICE indicate that the proposed model outperform the existing models in terms of power and delay. Moreover, using load-transistor results in 48.23% reductions in terms of power-delay-product (PDP) compared with the best model ever observed. Furthermore, the implementation of ternary arithmetic circuits such as half adder and multiplier using load-transistor has been investigated. An added common term between Sum and Carry output in half-adder (HA) and Carry and Product output in lower multiplier (MUL) has been used in arithmetic circuits. Considering the fact that a better proposal with higher performance has been utilized for the implementation of three-valued gates, our model results in the reduction of 9.95% and 23.87% PDP in HA and MUL respectively compared with the best models observed.


A cascade filter for pulse wave baseline drift elimination

Analyzing the pulse wave is an important method to diagnosis the status of human health. Generally the directly acquired signal contains varying degrees of baseline drift which is usually caused by breathing and physical movement, it mostly influences the accuracy of signal analysis and must be eliminated. This article presented a cascade filter framework to remove the baseline drift of pulse wave. The first stage zero phase filter (ZPF) roughly reduced the trend of the baseline drift. The second stage of the removed method considered the signal features. Through the pulse waveform reference points' accurate location and cubic spline estimation, we completely eliminated the rest drift. Compared with the other methods, e.g. wavelet-based filtering, morphological filtering, empirical mode decomposition (EMD) filtering, the experimental result showed that the proposed method had not only better effect, but also small calculation cost, and the scheme was more suitable for actual application.


Text-Based Deep Learning for Artificial Jazz Improvisation

Deep learning has exploded in popularity in the past several years. Picking up from recent projects using recurrent neural networks to generate text, we attempted to use the same principles to generate jazz-like improvisations. We provide an RNN using torch-rnn, trained on freely available MIDI files of jazz transcriptions, which can generate music that captures rhythms, and to a lesser extend harmony and melody. Listener feedback shows that our generated music is easily recognizable as “jazz”, though lacking macro-level structure as can be expected for a basic RNN generating entirely sequential patterns. We also provide a new ASCII-based file format capable of storing music in a compact but not totally opaque way. Our format allows for close to a 7-times size decrease from existing MIDI-to-CSV converters. We show the musical principles behind the information we chose to include and leave out in converting MIDI to our new format.


Design of an experimental setup for evaluation of inductive transcutaneous energy transfer systems

Work objective — is to develop an experimental setup for evaluation characteristics of inductive transcutaneous energy transfer systems, such as comparative studies of various topological solutions; studies of systems' thermal safety; influence of geometry of a coils couple on the parameters of energy transfer. The system consists of planar spiral coils, device for positioning, receiving and transmitting circuit boards, frequency generator, and National Instruments USB-6361 DAQ. Mathematical apparatus has been developed that makes it possible to obtain self-inductance and the internal equivalent resistance of the coils from the measured voltage drop in the transmitting circuit, as well as the dependence of the mutual inductance on displacements. LabVIEW virtual instrument for automation of measurements is designed. The values of the self-inductances of planar spiral coils and the practical dependences of the mutual inductance of spiral coils on displacements without and with using automation tools were experimentally found. The results corresponds theoretical calculations.


Antenna Selection for MIMO Nonorthogonal Multiple Access Systems

This paper considers the antenna selection (AS) problem for a multiple-input multiple-output nonorthogonal multiple access system. In particular, we develop new computationally efficient AS algorithms for two commonly used scenarios: NOMA with fixed power allocation (F-NOMA) and NOMA with cognitive radio-inspired power allocation (CR-NOMA). For the F-NOMA scenario, a new max–max–max AS (A<inline-formula><tex-math notation="LaTeX">$^3$</tex-math> </inline-formula>-AS) scheme is first proposed to maximize the system sum-rate. This is achieved by selecting one antenna at the base station (BS) and corresponding best receive antenna at each user that maximizes the channel gain of the resulting <italic>strong</italic> user. To improve the user fairness, a new max–min–max AS (AIA-AS) scheme is subsequently developed, in which we jointly select one transmit antenna at BS and corresponding best receive antennas at users to maximize the channel gain of the resulting <italic>weak</italic> user. For the CR-NOMA scenario, we propose another new AS algorithm, termed maximum-channel-gain-based antenna selection (MCG-AS), to maximize the achievable rate of the secondary user, under the condition that the primary user's quality-of-service requirement is satisfied. The asymptotic closed-form expressions of the average sum-rate for A<inline-formula> <tex-math notation="LaTeX">$^3$</tex-math></inline-formula>-AS and AIA-AS and that of the average rate of the secondary user for MCG-AS are derived. Numerical results demonstrate that the AIA-AS provides better user fairness, whereas the A<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula>-AS achieves a near-optimal sum-rate in F-NOMA systems. For the CR-NOMA scenario, MCG-AS achieves a near-optimal performance in a wide signal-to-noise-ratio regime. Furthermore, all the proposed AS algorithms yield a significant computational complexity reduction, compared to exhaustive search-based counterparts.


Session-based Recommendations with Recurrent Neural Networks

Session-based recommendation Permanent cold start: where personalized recommendations fail • User identification: Many sites (e.g. classifieds, video services) don’t require users to log in. Although some form of identification is possible, it is not reliable. • Intent/theme: Sessions usually have a goal or a specific theme. Different sessions of the same user center around different concepts. The entire user history may not help much in identifying the user’s current needs. • Never/rarely returning users: High percentage of the users of webshops come from search engines in search for some products and rarely return. Workaround in practice • Item-to-item recommendations: Recommend similar or frequently co-occurring items. We explore item-to-session recommendations. By modeling the whole session, more accurate recommendations can be provided. We propose an RNN-based approach to model the session and provide session-based recommendations. domonkos.tikk@gravityrd.com @domonkostikk balazs.hidasi@gravityrd.com @balazshidasi Balázs Hidasi


Model-predictive target defense by team of unmanned surface vehicles operating in uncertain environments

In this paper, we present a heuristic planning approach for guarding a valuable asset by a team of autonomous unmanned surface vehicles (USVs) operating in a continuous state-action space. The team's objective is to maximize the amount of time it takes an intruder boat to reach the asset. The team must cooperatively deal with uncertainty about which boats are actual intruders, employ active blocking to slow down intruders' movement towards the asset, and intelligently distribute themselves around the target to optimize future guarding opportunities. Our planner incorporates a market-based algorithm for allocating tasks to individual USVs by forward-simulating the mission and assigning estimated utilities to candidate task-allocation plans. The planner can be automatically adapted to a specific mission by optimizing the behaviors used to fulfil individual tasks. We present detailed simulation results that demonstrate the effectiveness of our approach.


The Effects of Personal Susceptibility and Social Support on Internet Addiction: an Application of Adler’s Theory of Individual Psychology

The aim of the present study was to examine the addictive effects of personal susceptibility and social support from family, friends, and significant other on Internet addiction. In this study, 207 medical students completed the UCLA Loneliness Scale, the Academic Expectations Stress Inventory, the Multidimensional Scale of Perceived Social Support, and the Young’s Diagnostic Questionnaire for Internet addiction. Participants were recruited via simple random sampling technique. Personal susceptibility variables such as loneliness, academic stress due to other expectations, and academic stress due to self-expectations were significantly positively correlated to, and all domains of social support were significantly negatively correlated to, Internet addiction. After adjusting for demographic variables, social support emerged as a significant predictor of Internet addiction in the hierarchical regression analysis. In support of Adler’s individual psychology theory, the present findings suggest that social support from family is a valuable adjunct to prevention and intervention programs aimed at alleviating Internet addiction in medical students.


Connectivity Learning in Multi-Branch Networks

The CIFAR-10 dataset consists of color images of size 32x32. The training set contains 50,000 images, the testing set 10,000 images. Each image in CIFAR-10 is categorized into one of 10 possible classes. In Table 3, we report the performance of different models trained on CIFAR-10. From these results we can observe that our models using learned connectivity achieve consistently better performance over the equivalent models trained with the fixed connectivity (Xie et al., 2017).


Processing Analytical Queries over Encrypted Data

MONOMI is a system for securely executing analytical workloads over sensitive data on an untrusted database server. MONOMI works by encrypting the entire database and running queries over the encrypted data. MONOMI introduces split client/server query execution, which can execute arbitrarily complex queries over encrypted data, as well as several techniques that improve performance for such workloads, including per-row precomputation, space-efficient encryption, grouped homomorphic addition, and pre-filtering. Since these optimizations are good for some queries but not others, MONOMI introduces a designer for choosing an efficient physical design at the server for a given workload, and a planner to choose an efficient execution plan for a given query at runtime. A prototype of MONOMI running on top of Postgres can execute most of the queries from the TPC-H benchmark with a median overhead of only 1.24× (ranging from 1.03× to 2.33×) compared to an un-encrypted Postgres database where a compromised server would reveal all data.


CryptoKnight: Generating and Modelling Compiled Cryptographic Primitives

Cryptovirological augmentations present an immediate, incomparable threat. Over the last decade, the substantial proliferation of crypto-ransomware has had widespread consequences for consumers and organisations alike. Established preventive measures perform well, however, the problem has not ceased. Reverse engineering potentially malicious software is a cumbersome task due to platform eccentricities and obfuscated transmutation mechanisms, hence requiring smarter, more efficient detection strategies. The following manuscript presents a novel approach for the classification of cryptographic primitives in compiled binary executables using deep learning. The model blueprint, a Dynamic Convolutional Neural Network (DCNN), is fittingly configured to learn from variable-length control flow diagnostics output from a dynamic trace. To rival the size and variability of equivalent datasets, and to adequately train our model without risking adverse exposure, a methodology for the procedural generation of synthetic cryptographic binaries is defined, using core primitives from OpenSSL with multivariate obfuscation, to draw a vastly scalable distribution. The library, CryptoKnight, rendered an algorithmic pool of AES, RC4, Blowfish, MD5 and RSA to synthesise combinable variants which automatically fed into its core model. Converging at 96% accuracy, CryptoKnight was successfully able to classify the sample pool with minimal loss and correctly identified the algorithm in a real-world crypto-ransomware application.


Autoencoder-based holographic image restoration

We propose a holographic image restoration method using an autoencoder, which is an artificial neural network. Because holographic reconstructed images are often contaminated by direct light, conjugate light, and speckle noise, the discrimination of reconstructed images may be difficult. In this paper, we demonstrate the restoration of reconstructed images from holograms that record page data in holographic memory and quick response codes by using the proposed method.


ICE: Item Concept Embedding via Textual Information

This paper proposes an item concept embedding (ICE) framework to model item concepts via textual information. Specifically, in the proposed framework there are two stages: graph construction and embedding learning. In the first stage, we propose a generalized network construction method to build a network involving heterogeneous nodes and a mixture of both homogeneous and heterogeneous relations. The second stage leverages the concept of neighborhood proximity to learn the embeddings of both items and words. With the proposed carefully designed ICE networks, the resulting embedding facilitates both homogeneous and heterogeneous retrieval, including item-to-item and word-to-item retrieval. Moreover, as a distributed embedding approach, the proposed ICE approach not only generates related retrieval results but also delivers more diverse results than traditional keyword-matching-based approaches. As our experiments on two real-world datasets show, ICE encodes useful textual information and thus outperforms traditional methods in various item classification and retrieval tasks.


Attributing physical and biological impacts to anthropogenic climate change

Significant changes in physical and biological systems are occurring on all continents and in most oceans, with a concentration of available data in Europe and North America. Most of these changes are in the direction expected with warming temperature. Here we show that these changes in natural systems since at least 1970 are occurring in regions of observed temperature increases, and that these temperature increases at continental scales cannot be explained by natural climate variations alone. Given the conclusions from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report that most of the observed increase in global average temperatures since the mid-twentieth century is very likely to be due to the observed increase in anthropogenic greenhouse gas concentrations, and furthermore that it is likely that there has been significant anthropogenic warming over the past 50 years averaged over each continent except Antarctica, we conclude that anthropogenic climate change is having a significant impact on physical and biological systems globally and in some continents.


Life Cycle of Smart Contracts in Blockchain Ecosystems

This paper discusses the life cycle of decentralized smart contracts, i.e. digital and executable representations of rights and obligations in a multi-party environment. The life cycle relies on blockchain technology, i.e. a distributed digital ledger, to ensure proper implementation and integrity of the smart contracts. The life cycle consists of four subsequent phases: Creation, freezing, execution, and finalization. For each phase actors and technological services are identified and explained in detail. With the life cycle at hand, risks and limitations of smart contracts and the underlying blockchain technology are briefly discussed.


SciPlore Xtract: Extracting Titles from Scientific PDF Documents by Analyzing Style Information (Font Size)

Extracting titles from a PDF’s full text is an important task in information retrieval to identify PDFs. Existing approaches apply complicated and expensive (in terms of calculating power) machine learning algorithms such as Support Vector Machines and Conditional Random Fields. In this paper we present a simple rule based heuristic, which considers style information (font size) to identify a PDF’s title. In a first experiment we show that this heuristic delivers better results (77.9% accuracy) than a support vector machine by CiteSeer (69.4% accuracy) in an ‘academic search engine’ scenario and better run times (8:19 minutes vs. 57:26 minutes).


Modified dual band gysel power divider with isolation bandwidth improvement

This paper presents a new design of a dual band power divider with high power handling capability and isolation bandwidth improvement, which contains six branch line sections, two grounded resistors, one resistor between output ports, an extension line at the input, and a single stub line at the end. Exact closed-form equations are derived for the proposed structure using even-mode and odd-mode analysis. In this design, flexibility in choosing two grounded resistors enables us to determine the frequency ratio range, bandwidth of the circuit, and the level of power handling capability. In addition, line admittance analysis shows that the proposed structure can support the acceptable frequency ratio range. For verification, the proposed structure is simulated, fabricated and measured using microstrip lines. Simulation and measurement results show wide isolation bandwidth, while maintaining high power handling capability over the Wilkinson power divider.


Extended Object and Group Tracking: A Comparison of Random Matrices and Random Hypersurface Models

Intelligent Sensor-Actuator-Systems Laboratory (ISAS), Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany †Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany ‡Data Fusion Algorithms & Software, EADS Deutschland GmbH, Ulm, Germany marcus.baum@kit.edu, {michael.feldmann |wolfgang.koch}@fkie.fraunhofer.de dietrich.fraenken@eads.com, uwe.hanebeck@ieee.org


Evolutionary Algorithms for Constrained Parameter Optimization Problems

Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods.


Hardening soft information sources

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Finding Alternative Translations in a Large Corpus of Movie Subtitle

OpenSubtitles.org provides a large collection of user contributed subtitles in various languages for movies and TV programs. Subtitle translations are valuable resources for cross-lingual studies and machine translation research. A less explored feature of the collection is the inclusion of alternative translations, which can be very useful for training paraphrase systems or collecting multi-reference test suites for machine translation. However, differences in translation may also be due to misspellings, incomplete or corrupt data files, or wrongly aligned subtitles. This paper reports our efforts in recognising and classifying alternative subtitle translations with language independent techniques. We use time-based alignment with lexical re-synchronisation techniques and BLEU score filters and sort alternative translations into categories using edit distance metrics and heuristic rules. Our approach produces large numbers of sentence-aligned translation alternatives for over 50 languages provided via the OPUS corpus collection.


Language as a Latent Variable: Discrete Generative Models for Sentence Compression

In this work we explore deep generative models of text in which the latent representation of a document is itself drawn from a discrete language model distribution. We formulate a variational auto-encoder for inference in this model and apply it to the task of compressing sentences. In this application the generative model first draws a latent summary sentence from a background language model, and then subsequently draws the observed sentence conditioned on this latent summary. In our empirical evaluation we show that generative formulations of both abstractive and extractive compression yield state-of-the-art results when trained on a large amount of supervised data. Further, we explore semi-supervised compression scenarios where we show that it is possible to achieve performance competitive with previously proposed supervised models while training on a fraction of the supervised data.


Abstractive Sentence Summarization with Attentive Recurrent Neural Networks

Abstractive Sentence Summarization generates a shorter version of a given sentence while attempting to preserve its meaning. We introduce a conditional recurrent neural network (RNN) which generates a summary of an input sentence. The conditioning is provided by a novel convolutional attention-based encoder which ensures that the decoder focuses on the appropriate input words at each step of generation. Our model relies only on learned features and is easy to train in an end-to-end fashion on large data sets. Our experiments show that the model significantly outperforms the recently proposed state-of-the-art method on the Gigaword corpus while performing competitively on the DUC-2004 shared task.ive Sentence Summarization generates a shorter version of a given sentence while attempting to preserve its meaning. We introduce a conditional recurrent neural network (RNN) which generates a summary of an input sentence. The conditioning is provided by a novel convolutional attention-based encoder which ensures that the decoder focuses on the appropriate input words at each step of generation. Our model relies only on learned features and is easy to train in an end-to-end fashion on large data sets. Our experiments show that the model significantly outperforms the recently proposed state-of-the-art method on the Gigaword corpus while performing competitively on the DUC-2004 shared task.


Selective Encoding for Abstractive Sentence Summarization

We propose a selective encoding model to extend the sequence-to-sequence framework for abstractive sentence summarization. It consists of a sentence encoder, a selective gate network, and an attention equipped decoder. The sentence encoder and decoder are built with recurrent neural networks. The selective gate network constructs a second level sentence representation by controlling the information flow from encoder to decoder. The second level representation is tailored for sentence summarization task, which leads to better performance. We evaluate our model on the English Gigaword, DUC 2004 and MSR abstractive sentence summarization datasets. The experimental results show that the proposed selective encoding model outperforms the state-ofthe-art baseline models.


Simulating crowd movement in agent-based model of large-scale flood

Crowd movement during natural disasters and major accidents can affect the success of evacuation procedure. Thus to develop an effective evacuation plan, a wide-range of scenarios causing different routes and patterns of crowd movement should be considered. Recent researches in agent-based simulation have achieved techniques to simulate crowd movement in emergency scenarios at city scale. However simulating crowd movement at a macroscopic level for disasters which might affect several cities, like large-scale flood, is still a challenge. This paper addresses this issue and makes three contributions. First, the development of an agent-based layered model to simulate large-scale flood using GIS is demonstrated. Second, the simulation of crowd agents' movement on available roads is presented. Third, the preliminary experiments running on private Cloud server is reported. The experiments cover case studies illustrated the movement of crowd agents around Thailand while several parts of the country were inundated. The 2D animation depict the movement of crowd; and the simulation results show the status of the agents and the amount of individuals which required shelters. To simulate one day events, the simulator took 4-9 hours execution time depending on the severity of floods and available facilities.


Secure routing for internet of things: A survey

The Internet of Things (IoT) could be described as the pervasive and global network which aids and provides a system for the monitoring and control of the physical world through the collection, processing and analysis of generated data by IoT sensor devices. It is projected that by 2020 the number of connected devices is estimated to grow exponentially to 50 billion. The main drivers for this growth are our everyday devices such as cars, refrigerators, fans, lights, mobile phones and other operational technologies including the manufacturing infrastructures which are now becoming connected systems across the world. It is apparent that security will pose a fundamental enabling factor for the successful deployment and use of most IoT applications and in particular secure routing among IoT sensor nodes thus, mechanisms need to be designed to provide secure routing communications for devices enabled by the IoT technology. This survey analyzes existing routing protocols and mechanisms to secure routing communications in IoT, as well as the open research issues. We further analyze how existing approaches ensure secure routing in IoT, their weaknesses, threats to secure routing in IoT and the open challenges and strategies for future research work for a better secure IoT routing.


Practical Identity-Based Encryption Without Random Oracles

We present an Identity Based Encryption (IBE) system that is fully secure in the standard model and has several advantages over previous such systems – namely, computational efficiency, shorter public parameters, and a “tight” security reduction, albeit to a stronger assumption that depends on the number of private key generation queries made by the adversary. Our assumption is a variant of Boneh et al.’s decisional Bilinear Diffie-Hellman Exponent assumption, which has been used to construct efficient hierarchical IBE and broadcast encryption systems. The construction is remarkably simple. It also provides recipient anonymity automatically, providing a second (and more efficient) solution to the problem of achieving anonymous IBE without random oracles. Finally, our proof of CCA2 security, which has more in common with the security proof for the Cramer-Shoup encryption scheme than with security proofs for other IBE systems, may be of independent interest.


Empirical Evaluation of User Experience in two Adaptive Mobile Application Prototypes

Today’s applications such as ubiquitous systems are more and more aware of user’s habits and the context of use. The features of products and the context of use will affect the human’s experiences and preferences about the use of device. Thus, user experience in user-product interaction has been regarded as an important research topic in the mobile application design area. The purpose of this paper is to clarify how user experience can be evaluated in adaptive mobile applications. The user experience evaluations were performed through interviews and observation while test users were using PDA-based adaptive mobile application prototypes. As a result, this paper presents the analysis of the test methods for further user experience evaluations. CR Categories: J.m [Computer Applications]: Miscellaneous; Experimentation; Human Factors


Changes in adult attachment styles in American college students over time: a meta-analysis.

The current article examines changes over time in a commonly used measure of adult attachment style. A cross-temporal meta-analysis was conducted on 94 samples of American college students (total N = 25,243, between 1988 and 2011) who chose the most representative description of four possible attachment styles (Secure, Dismissing, Preoccupied, and Fearful) on the Relationship Questionnaire. The percentage of students with Secure attachment styles has decreased in recent years (1988: 48.98%; 2011: 41.62%), whereas the percentage of students with Insecure attachment styles (sum of Dismissing, Preoccupied, Fearful) has increased in recent years (1988: 51.02%; 2011: 58.38%). The percentage of students with Dismissing attachment styles has increased over time (1988: 11.93%; 2011: 18.62%), even after controlling for age, gender, race, and publication status. Positive views of others have declined across the same time period. We discuss possible implications and explanations for these changes.


Zero moment point-measurements from a human walker wearing robot feet as shoes

The anthropomorphic biped robot Bip is equipped with sensors for measuring the ground/feet forces in order to localize the center of pressure (CoP) and zero moment point (ZMP). This paper focuses on experimental results regarding the evolution of the ground contact forces, obtained from a human walker wearing the robot feet as shoes. First, one determines the influence of heavy and rigid metallic shoes on the gait of the subject, during experiments carried out on flat ground. Second, the evolution of the contact forces is studied while walking on parallel planes with different elevations (stairs), then one addresses the case where the feet are supported by two nonparallel planes (uneven terrain). The corresponding analysis is founded on the concepts of virtual supporting surface and pseudo-CoP-ZMP introduced in a companion paper, discussing the theoretical aspects of CoP and ZMP (Sardain and Bessonnet, 2004). Beyond the academic contribution, the data analyzed in this paper could constitute an incitement to design truly anthropomorphic feet, for Bip as well as for other biped robots, because all the current robot feet are functionally rather poor.


Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms

Find the secret to improve the quality of life by reading this evolutionary algorithms in theory and practice evolution strategies evolutionary programming genetic algorithms. This is a kind of book that you need now. Besides, it can be your favorite book to read after having this book. Do you ask why? Well, this is a book that has different characteristic with others. You may not need to know who the author is, how well-known the work is. As wise word, never judge the words from who speaks, but make the words as your good value to your life.


Quality Metrics in optical modulation analysis: EVM and its relation to Q-factor, OSNR, and BER

The quality of optical signals is a very important parameter in optical communications. Several metrics are in common use, like optical signal-to-noise power ratio (OSNR), Q-factor, error vector magnitude (EVM) and bit error ratio (BER). A measured raw BER is not necessarily useful to predict the final BER after soft-decision forward error correction (FEC), if the statistics of the noise leading to errors is unknown. In this respect the EVM is superior, as it allows an estimation of the error statistics. We compare various metrics analytically, by simulation, and through experiments. We employ six quadrature amplitude modulation (QAM) formats at symbol rates of 20 GBd and 25 GBd. The signals were generated by a software-defined transmitter. We conclude that for optical channels with additive Gaussian noise the EVM metric is a reliable quality measure. For nondata-aided QAM reception, BER in the range 10-6...10-2 can be reliably estimated from measured EVM.


Deep Marching Cubes: Learning Explicit Surface Representations

Existing learning based solutions to 3D surface prediction cannot be trained end-to-end as they operate on intermediate representations (e.g., TSDF) from which 3D surface meshes must be extracted in a post-processing step (e.g., via the marching cubes algorithm). In this paper, we investigate the problem of end-to-end 3D surface prediction. We first demonstrate that the marching cubes algorithm is not differentiable and propose an alternative differentiable formulation which we insert as a final layer into a 3D convolutional neural network. We further propose a set of loss functions which allow for training our model with sparse point supervision. Our experiments demonstrate that the model allows for predicting sub-voxel accurate 3D shapes of arbitrary topology. Additionally, it learns to complete shapes and to separate an object's inside from its outside even in the presence of sparse and incomplete ground truth. We investigate the benefits of our approach on the task of inferring shapes from 3D point clouds. Our model is flexible and can be combined with a variety of shape encoder and shape inference techniques.


Causal Generative Domain Adaptation Networks

An essential problem in domain adaptation is to understand and make use of distribution changes across domains. For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains. By explicitly modeling the changes, one can even generate data in new domains using the generating process with new values for the latent variables in G-DAN. In practice, the process to generate all features together may involve high-dimensional latent variables, requiring dealing with distributions in high dimensions and making it difficult to learn domain changes from few source domains. Interestingly, by further making use of the causal representation of joint distributions, we then decompose the joint distribution into separate modules, each of which involves different low-dimensional latent variables and can be learned separately, leading to a Causal G-DAN (CG-DAN). This improves both statistical and computational efficiency of the learning procedure. Finally, by matching the feature distribution in the target domain, we can recover the target-domain joint distribution and derive the learning machine for the target domain. We demonstrate the efficacy of both G-DAN and CG-DAN in domain generation and cross-domain prediction on both synthetic and real data experiments.


Migrating code with statistical machine translation

In the era of mobile computing, developers often need to migrate code written for one platform in a programming language to another language for a different platform, e.g., from Java for Android to C# for Windows Phone. The migration process is often performed manually or semi-automatically, in which developers are required to manually define translation rules and API mappings. This paper presents semSMT, an automatic tool to migrate code written in Java to C#. semSMT utilizes statistical machine translation to automatically infer translation rules from existing migrated code, thus, requires no manual defining of rules. The video demonstration on semSMT can be found on YouTube at http://www.youtube.com/watch?v=aRSnl5-7vNo.


A Faster and More Realistic Flush+Reload Attack on AES

Cloud’s unrivaled cost effectiveness and on the fly operation versatility is attractive to enterprise and personal users. However, the cloud inherits a dangerous behavior from virtualization systems that poses a serious security risk: resource sharing. This work exploits a shared resource optimization technique called memory deduplication to mount a powerful known-ciphertext only cache side-channel attack on a popular OpenSSL implementation of AES. In contrast to the other cross-VM cache attacks, our attack does not require synchronization with the target server and is fully asynchronous, working in a more realistic scenario with much weaker assumption. Also, our attack succeeds in just 15 seconds working across cores in the cross-VM setting. Our results show that there is strong information leakage through cache in virtualized systems and the memory deduplication should be approached with caution.


Time-Varying Graphs and Social Network Analysis: Temporal Indicators and Metrics

Most instruments formalisms, concepts, and metrics for social networks analysis fail to capture their dynamics. Typical systems exhibit different scales of dynamics, ranging from the fine-grain dynamics of interactions (which recently led researchers to consider temporal versions of distance, connectivity, and related indicators), to the evolution of network properties over longer periods of time. This paper proposes a general approach to study that evolution for both atemporal and temporal indicators, based respectively on sequences of static graphs and sequences of time-varying graphs that cover successive time-windows. All the concepts and indicators, some of which are new, are expressed using a time-varying graph formalism recently proposed in [10].


Speech Synthesis for Mixed-Language Navigation Instructions

Text-to-Speech (TTS) systems that can read navigation instructions are one of the most widely used speech interfaces today. Text in the navigation domain may contain named entities such as location names that are not in the language that the TTS database is recorded in. Moreover, named entities can be compound words where individual lexical items belong to different languages. These named entities may be transliterated into the script that the TTS system is trained on. This may result in incorrect pronunciation rules being used for such words. We describe experiments to extend our previous work in generating code-mixed speech to synthesize navigation instructions, with a mixed-lingual TTS system. We conduct subjective listening tests with two sets of users, one being students who are native speakers of an Indian language and very proficient in English, and the other being drivers with low English literacy, but familiarity with location names. We find that in both sets of users, there is a significant preference for our proposed system over a baseline system that synthesizes instructions in English.


Deep-learning: A potential method for tuberculosis detection using chest radiography

Tuberculosis (TB) is a major health threat in the developing countries. Many patients die every year due to lack of treatment and error in diagnosis. Developing a computer-aided diagnosis (CAD) system for TB detection can help in early diagnosis and containing the disease. Most of the current CAD systems use handcrafted features, however, lately there is a shift towards deep-learning-based automatic feature extractors. In this paper, we present a potential method for tuberculosis detection using deep-learning which classifies CXR images into two categories, that is, normal and abnormal. We have used CNN architecture with 7 convolutional layers and 3 fully connected layers. The performance of three different optimizers has been compared. Out of these, Adam optimizer with an overall accuracy of 94.73% and validation accuracy of 82.09% performed best amongst them. All the results are obtained using Montgomery and Shenzhen datasets which are available in public domain.


MARKETING AND THE INTERNET : A RESEARCH REVIEW

This paper reviews the research to-date on how the Internet is impacting marketing. It focuses mainly on academic research published in US journals, but includes selected material from other sources. It covers internet adoption and usage; online purchasing behavior; internet advertising; internet economics and pricing; channels and intermediaries and online marketing strategy. The final section gives overall conclusions and briefly lists some opportunities for future research. Contrary to earlier hype, the Internet does not change the fundamental principles of marketing. Nor has its impact to-date – e.g. on consumer behavior, advertising, pricing, channels, intermediaries, strategy and globalization – been anything like as dramatic as predicted. Nevertheless, it has already had some effect on all of these areas. For instance, it is emerging as a flexible, fast-growing advertising medium and as a significant direct channel in many markets (PCs, books, music, travel, and even cars). It has led to some reduction in average prices and in price dispersion. It is bringing into the mainstream previously exotic business models such as auctions, electronic markets, and brand communities. Most of its successes have come from established businesses with ‘bricks and clicks’ strategies which use the Internet to supplement existing activities but rarely to generate much direct revenue We expect its impact to increase steadily over the next ten years, as the technology improves, its availability and usage continue to grow, and businesses learn better how to use it within their wider marketing strategy. Earlier expectations about the Internet were so overblown that the reality was bound to disappoint. But by any normal standards, it is a fast-growing young medium with wide-ranging effects across the whole marketing spectrum.


Social-Aware Movie Recommendation via Multimodal Network Learning

With the rapid development of Internet movie industry social-aware movie recommendation systems (SMRs) have become a popular online web service that provide relevant movie recommendations to users. In this effort many existing movie recommendation approaches learn a user ranking model from user feedback with respect to the movie's content. Unfortunately this approach suffers from the sparsity problem inherent in SMR data. In the present work we address the sparsity problem by learning a multimodal network representation for ranking movie recommendations. We develop a heterogeneous SMR network for movie recommendation that exploits the textual description and movie-poster image of each movie as well as user ratings and social relationships. With this multimodal data we then present a heterogeneous information network learning framework called SMR-multimodal network representation learning (MNRL) for movie recommendation. To learn a ranking metric from the heterogeneous information network we also developed a multimodal neural network model. We evaluated this model on a large-scale dataset from a real world SMR Web site and we find that SMR-MNRL achieves better performance than other state-of-the-art solutions to the problem.


A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms

Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes a hyper-heuristic evolutionary algorithm for automatically generating decision-tree induction algorithms, named HEAD-DT. We perform extensive experiments in 20 public data sets to assess the performance of HEAD-DT, and we compare it to traditional decision-tree algorithms such as C4.5 and CART. Results show that HEAD-DT can generate algorithms that significantly outperform C4.5 and CART regarding predictive accuracy and F-Measure.


On the tradeoff between privacy and utility in data publishing

In data publishing, anonymization techniques such as generalization and bucketization have been designed to provide privacy protection. In the meanwhile, they reduce the utility of the data. It is important to consider the tradeoff between privacy and utility. In a paper that appeared in KDD 2008, Brickell and Shmatikov proposed an evaluation methodology by comparing privacy gain with utility gain resulted from anonymizing the data, and concluded that "even modest privacy gains require almost complete destruction of the data-mining utility". This conclusion seems to undermine existing work on data anonymization. In this paper, we analyze the fundamental characteristics of privacy and utility, and show that it is inappropriate to directly compare privacy with utility. We then observe that the privacy-utility tradeoff in data publishing is similar to the risk-return tradeoff in financial investment, and propose an integrated framework for considering privacy-utility tradeoff, borrowing concepts from the Modern Portfolio Theory for financial investment. Finally, we evaluate our methodology on the Adult dataset from the UCI machine learning repository. Our results clarify several common misconceptions about data utility and provide data publishers useful guidelines on choosing the right tradeoff between privacy and utility.


Wideband Tapered-Slot Antenna with Corrugated Edges for GPR Applications

A novel Wideband tapered-slot antenna for GPR applications is proposed. A conventional approach to reduce unwanted interactions between the antenna and the ground surface requires extensive microwave absorbers treatment. For the same purpose we propose to use corrugations along the antenna edges. The corrugations improve directivity and wideband matching of the antenna with free space retaining also good radiation efficiency. Further improvement of the antenna performance has been achieved by patching the corrugated areas of the antenna with the resistance card strips. The elimination of the foam-type absorbers makes the antenna more robust and easier in fabrication.


Relational Learning of Pattern-Match Rules for Information Extraction

Information extraction is a form of shallow text processing which locates a specified set of relevant items in natural language documents. Such systems can be useful, but require domain-specific knowledge and rules, and are time-consuming and difficult to build by hand, making infomation extraction a good testbed for the application of machine learning techniques to natural language processing. This paper presents a system, RAPIER, that takes pairs of documents and filled templates and induces pattern-match rules that directly extract fillers for the slots in the template. The learning algorithm incorporates techniques from several inductive logic programming systems and learns unbounded patterns that include constraints on the words and part-of-speech tags surrounding the filler. Encouraging results are presented on learning to extract information from computer job postings from the newsgroup misc. jobs. offered.


Concept-Based Knowledge Discovery in Texts Extracted from the Web

This paper presents an approach for knowledge discovery in texts extracted from the Web. Instead of analyzing words or attribute values, the approach is based on concepts, which are extracted from texts to be used as characteristics in the mining process. Statistical techniques are applied on concepts in order to find interesting patterns in concept distributions or associations. In this way, users can perform discovery in a high level, since concepts describe real world events, objects, thoughts, etc. For identifying concepts in texts, a categorization algorithm is used associated to a previous classification task for concept definitions. Two experiments are presented: one for political analysis and other for competitive intelligence. At the end, the approach is discussed, examining its problems and advantages in the Web context.


Fast Effective Rule Induction


A Survey of Data Mining and Knowledge Discovery Software Tools

Knowledge discovery in databases is a rapidly growing field, whose development is driven by strong research interests as well as urgent practical, social, and economical needs. While the last few years knowledge discovery tools have been used mainly in research environments, sophisticated software products are now rapidly emerging. In this paper, we provide an overview of common knowledge discovery tasks and approaches to solve these tasks. We propose a feature classification scheme that can be used to study knowledge and data mining software. This scheme is based on the software's general characteristics, database connectivity, and data mining characteristics. We then apply our feature classification scheme to investigate 43 software products, which are either research prototypes or commercially available. Finally, we specify features that we consider important for knowledge discovery software to possess in order to accommodate its users effectively, as well as issues that are either not addressed or insufficiently solved yet.


Prosthetic mesh used for inguinal and ventral hernia repair: normal appearance and complications in ultrasound and CT.

The use of prosthetic mesh has now become accepted practice in the treatment of patients with both inguinal and ventral hernias. This pictorial review illustrates the various radiological appearances of these meshes and also demonstrates the post-operative complications associated with their implantation.


Design and Roadmap Methodology for Integrated Power Modules in High Switching Frequency Synchronous Buck Voltage Regulators

The proposed methodology encompasses four study phases that address the following key design aspects of integrated power modules (IPM) for voltage regulators: Fundamental switching understanding, determination of compact/accurate loss expressions and derivation of design rules, guidelines and figure of merits for converter optimisation. The applied methods are based on several modelling approaches suitable for the different study phases. The simplified loss model proves to be effective to breakdown the power losses of a state of the art IPM. Analysis of an optimised design leads to the identification of critical circuit and device parameters that may well condition the layout of future technological roadmap targets.


Analysing B2B electronic procurement benefits: information systems perspective

Abstract Purpose – This paper aims to present electronic procurement benefits identified in four case companies from the information technology (IT), hi-tech sector. Design/methodology/approach – Multi-case study design was applied. The benefits reported in the companies were analysed and classified according to taxonomies from the information systems discipline. Finally, a new benefits classification was proposed. The framework was developed based on information systems literature. Findings – The research confirmed difficulties with benefits evaluation, as, apart from operational benefits, non-financial, intangible benefits at strategic level were also identified. Traditional evaluation methods are unable to capture all benefits categories, especially at strategic level. New taxonomy was created, which allows evaluation of the complex e-procurement impact. In the proposed taxonomy, e-procurement benefits are classified according to their level (operational, tactical, strategic), area of impact, applying scorecard dimensions (customer, process, financial, learning and growth). In addition the benefits characteristic is captured (tangible, intangible, financial and non-financial). Research limitations/implications – Research is based on four case studies only. Findings are specific to case companies and the environment in which they operate. The framework should be tested further in different contexts. Practical implications – The new taxonomy allows evaluation of the complex e-procurement impact, demonstrating that benefits achieved do not concern merely the financial impact. The framework can be applied to preparing new systems implementation as well as to evaluating existing systems. Originality/value – The paper applies information systems frameworks to the electronic procurement field, which allows one to look at e-procurement systems considering its complex impact. The framework can also be used to evaluate different systems, not simply e-procurement.


Riemannian Geometry and Geometric Analysis (5. ed.)


Improving Comprehension of Measurements Using Concrete Re-expression Strategies

It can be difficult to understand physical measurements (e.g., 28 lb, 600 gallons) that appear in news stories, data reports, and other documents. We develop tools that automatically re-express unfamiliar measurements using the measurements of familiar objects. Our work makes three contributions: (1) we identify effectiveness criteria for objects used in concrete measurement re-expressions; (2) we operationalize these criteria in a scalable method for mining a large dataset of concrete familiar objects with their physical dimensions from Amazon and Wikipedia; and (3) we develop automated concrete re-expression tools that implement three common re-expression strategies (adding familiar context, reunitization and proportional analogy) as energy minimization algorithms. Crowdsourced evaluations of our tools indicate that people find news articles with re-expressions more helpful and re- expressions help them to better estimate new measurements.