title: Path sharing and predicate evaluation for high-performance XML filtering

abstract: XML filtering systems aim to provide fast, on-the-fly matching of XML-encoded data to large numbers of query specifications containing constraints on both structure and content. It is now well accepted that approaches using event-based parsing and Finite State Machines (FSMs) can provide the basis for highly scalable structure-oriented XML filtering systems. The XFilter system [Altinel and Franklin 2000] was the first published FSM-based XML filtering approach. XFilter used a separate FSM per path query and a novel indexing mechanism to allow all of the FSMs to be executed simultaneously during the processing of a document. Building on the insights of the XFilter work, we describe a new method, called "YFilter" that combines all of the path queries into a single Nondeterministic Finite Automaton (NFA). YFilter exploits commonality among queries by merging common prefixes of the query paths such that they are processed at most once. The resulting shared processing provides tremendous improvements in structure matching performance but complicates the handling of value-based predicates.In this article, we first describe the XFilter and YFilter approaches and present results of a detailed performance comparison of structure matching for these algorithms as well as a hybrid approach. The results show that the path sharing employed by YFilter can provide order-of-magnitude performance benefits. We then propose two alternative techniques for extending YFilter's shared structure matching with support for value-based predicates, and compare the performance of these two techniques. The results of this latter study demonstrate some key differences between shared XML filtering and traditional database query processing. Finally, we describe how the YFilter approach is extended to handle more complicated queries containing nested path expressions.


title: Eavesdropping on Fine-Grained User Activities Within Smartphone Apps Over Encrypted Network Traffic

abstract: Smartphone apps have changed the way we interact with online services, but highly specialized apps come at a cost to privacy. In this paper we will demonstrate that a passive eavesdropper is capable of identifying finegrained user activities within the wireless network traffic generated by apps. Despite the widespread use of fully encrypted communication, our technique, called NetScope, is based on the intuition that the highly specific implementation of each app leaves a fingerprint on its traffic behavior (e.g., transfer rates, packet exchanges, and data movement). By learning the subtle traffic behavioral differences between activities (e.g., "browsing" versus "chatting" in a dating app), NetScope is able to perform robust inference of users' activities, for both Android and iOS devices, based solely on inspecting IP headers. Our evaluation with 35 widely popular app activities (ranging from social networking and dating to personal health and presidential campaigns) shows that NetScope yields high detection accuracy (78.04% precision and 76.04% recall on average).


NCRYPTION A ND S TEGANOGRAPHY


title: A novel Iris recognition method based on the Contourlet Transform and Biomimetic Pattern Recognition Algorithm

abstract: Iris recognition has been a hot research topic in these years. In this paper, an iris recognition method based on the the Contourlet Transform (CT) and Biomimetic Pattern Recognition (BPR) has been proposed. In proposed method, the Contourlet Transform was used to extract the significant features of the preprocessed iris image, and the Biomimetic Pattern Recognition algorithm was used to construct the high-dimensional covering hypersurface in order to recognize the iris images. Experiments on the CASIA iris image database show that the proposed iris recognition method based on the CT and BPR can achieve promising results compared with other methods.


GUIDE FOR CONSTRUCTING SELF-EFFICACY SCALES


title: Comparison of perioperative outcomes between robotic and laparoscopic partial nephrectomy: a systematic review and meta-analysis.

abstract: CONTEXT ::: Robotic partial nephrectomy (RPN) is rapidly increasing; however, the benefit of RPN over laparoscopic partial nephrectomy (LPN) is controversial. ::: ::: ::: OBJECTIVE ::: To compare perioperative outcomes of RPN and LPN. ::: ::: ::: EVIDENCE ACQUISITION ::: We searched Ovid-Medline, Ovid-Embase, the Cochrane Library, KoreaMed, KMbase, KISS, RISS, and KisTi from their inception through August 2013. Two independent reviewers extracted data using a standardized form. Quality of the selected studies was assessed using the methodological index for nonrandomized studies. ::: ::: ::: EVIDENCE SYNTHESIS ::: A total of 23 studies and 2240 patients were included. All studies were cohort studies with no randomization, and the methodological quality varied. There was no significant difference between the two groups regarding complications of Clavien-Dindo classification grades 1-2 (p=0.62), Clavien-Dindo classification grades 3-5 (p=0.78), change of serum creatinine (p=0.65), operative time (p=0.35), estimated blood loss (p=0.76), and positive margins (p=0.75). The RPN group had a significantly lower rate of conversion to open surgery (p=0.02) and conversion to radical surgery (p=0.0006), shorter warm ischemia time (WIT; p=0.005), smaller change of estimated glomerular filtration rate (eGFR; p=0.03), and shorter length of stay (LOS; p=0.004). ::: ::: ::: CONCLUSIONS ::: This meta-analysis shows that RPN is associated with more favorable results than LPN in conversion rate to open or radical surgery, WIT, change of eGFR, and shorter LOS. To establish the safety and effectiveness outcomes of robotic surgery, well-designed randomized clinical studies with long-term follow-up are needed. ::: ::: ::: PATIENT SUMMARY ::: Robotic partial nephrectomy (PN) is more favorable than laparoscopic PN in terms of lower conversion rate to radical nephrectomy, a favorable renal function indexed estimated glomerular filtration rate, shorter length of hospital stay, and shorter warm ischemia time.


title: Building Bridges across Social Platforms: Answering Twitter Questions with Yahoo! Answers

abstract: This paper investigates techniques for answering microblog questions by searching in a large community question answering website. Some question transformations are considered, some proprieties of the answering platform are examined, how to select among the various available configurations in a learning-to-rank framework is studied.


title: Tensor displays: compressive light field synthesis using multilayer displays with directional backlighting

abstract: We introduce tensor displays: a family of compressive light field displays comprising all architectures employing a stack of time-multiplexed, light-attenuating layers illuminated by uniform or directional backlighting (i.e., any low-resolution light field emitter). We show that the light field emitted by an N-layer, M-frame tensor display can be represented by an Nth-order, rank-M tensor. Using this representation we introduce a unified optimization framework, based on nonnegative tensor factorization (NTF), encompassing all tensor display architectures. This framework is the first to allow joint multilayer, multiframe light field decompositions, significantly reducing artifacts observed with prior multilayer-only and multiframe-only decompositions; it is also the first optimization method for designs combining multiple layers with directional backlighting. We verify the benefits and limitations of tensor displays by constructing a prototype using modified LCD panels and a custom integral imaging backlight. Our efficient, GPU-based NTF implementation enables interactive applications. Through simulations and experiments we show that tensor displays reveal practical architectures with greater depths of field, wider fields of view, and thinner form factors, compared to prior automultiscopic displays.


title: Access control and the Resource Description Framework: A survey

abstract: In recent years we have seen significant advances in the technology used to both publish and consume structured data using the existing web infrastructure, commonly referred to as the Linked Data Web. However, in order to support the next generation of e-business applications on top of Linked Data suitable forms of access control need to be put in place. This paper provides an overview of the various access control models, standards and policy languages, and the different access control enforcement strategies for the Resource Description Framework (the data model underpinning the Linked Data Web). A set of access control requirements that can be used to categorise existing access control strategies is proposed and a number of challenges that still need to be overcome are identified.


title: Continuous interacting ant colony algorithm based on dense heterarchy

abstract: Ant colony algorithms are a class of metaheuristics which are inspired from the behavior of real ants. The original idea consisted in simulating the stigmergic communication, therefore these algorithms are considered as a form of adaptive memory programming. A new formalization is proposed for the design of ant colony algorithms, introducing the biological notions of heterarchy and communication channels. We are interested in the way ant colonies handle the information. According to these issues, a heterarchical algorithm called "Continuous Interacting Ant Colony" (CIAC) is designed for the optimization of multiminima continuous functions. CIAC uses two communication channels showing the properties of trail and direct communications. CIAC presents interesting emergent properties as it was shown through some analytical test functions.


title: VLAT: Development of a Visualization Literacy Assessment Test

abstract: The Information Visualization community has begun to pay attention to visualization literacy; however, researchers still lack instruments for measuring the visualization literacy of users. In order to address this gap, we systematically developed a visualization literacy assessment test (VLAT), especially for non-expert users in data visualization, by following the established procedure of test development in Psychological and Educational Measurement: (1) Test Blueprint Construction, (2) Test Item Generation, (3) Content Validity Evaluation, (4) Test Tryout and Item Analysis, (5) Test Item Selection, and (6) Reliability Evaluation. The VLAT consists of 12 data visualizations and 53 multiple-choice test items that cover eight data visualization tasks. The test items in the VLAT were evaluated with respect to their essentialness by five domain experts in Information Visualization and Visual Analytics (average content validity ratio = 0.66). The VLAT was also tried out on a sample of 191 test takers and showed high reliability (reliability coefficient omega = 0.76). In addition, we demonstrated the relationship between users' visualization literacy and aptitude for learning an unfamiliar visualization and showed that they had a fairly high positive relationship (correlation coefficient = 0.64). Finally, we discuss evidence for the validity of the VLAT and potential research areas that are related to the instrument.


title: Modulation and coding for ATSC 3.0

abstract: In this paper we summarize and expound upon the choices made for the BICM (Bit-Interleaved and Coded Modulation) part of the next-generation terrestrial broadcast standard known as ATSC 3.0. The structure of the ATSC 3.0 BICM consists of a forward error correcting (FEC) code, bit interleaver and constellation mapper. In order to achieve high efficiency over a wide range of reception conditions and Carrier-to-Noise (C/N) ratio values, several notable new elements have been standardized. First, twenty four original Low-Density Parity Check (LDPC) codes have been designed, with coding rates from 2/15 (0.13) up to 13/15 (0.87) for two code sizes: 16200 bits and 64800 bits. Two different LDPC structures have been adopted; one structure more suited to medium and high coding rates and another structure suited to very low coding rates. Second, in addition to QPSK, Non-Uniform Constellations (NUCs) have been chosen for constellation sizes from 16QAM to 4096QAM to bridge the gap to the Shannon theoretical limit. Two different types of NUCs have been proposed: one-dimensional NUCs (1D-NUC) for 1024- and 4096-point constellations, and two-dimensional NUCs (2D-NUCs) for 16-, 64- and 256-point constellations. 2D-NUCs achieve a better performance than 1D-NUCs but with a higher complexity since they cannot be separated into two independent I/Q components. Each NUC has been optimized for the particular code and coding rate, although for the shorter code length the constellations have been limited up to 256QAM. Finally, a bit interleaver, optimized for each NUC/coding rate combination, has been designed to maximize the performance. This paper describes the coding, modulation and bit interleaving modules of the BICM block of ATSC 3.0, and compares its performance with other DTT standards such as ATSC A/53 and DVB-T2.


title: TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering

abstract: Trajectory classification, i.e., model construction for predicting the class labels of moving objects based on their trajectories and other features, has many important, real-world applications. A number of methods have been reported in the literature, but due to using the shapes of whole trajectories for classification, they have limited classification capability when discriminative features appear at parts of trajectories or are not relevant to the shapes of trajectories. These situations are often observed in long trajectories spreading over large geographic areas. ::: ::: Since an essential task for effective classification is generating discriminative features, a feature generation framework TraClass for trajectory data is proposed in this paper, which generates a hierarchy of features by partitioning trajectories and exploring two types of clustering: (1) region-based and (2) trajectory-based. The former captures the higher-level region-based features without using movement patterns, whereas the latter captures the lower-level trajectory-based features using movement patterns. The proposed framework overcomes the limitations of the previous studies because trajectory partitioning makes discriminative parts of trajectories identifiable, and the two types of clustering collaborate to find features of both regions and sub-trajectories. Experimental results demonstrate that TraClass generates high-quality features and achieves high classification accuracy from real trajectory data.


title: Energy-Efficient Cooperative Spectrum Sensing: A Survey

abstract: The article analyzes the problem of energy efficient techniques in cooperative spectrum sensing (CSS). Although it was proven that single-device sensing is not sufficient for reliable sensing, cooperative spectrum sensing was proposed, burdened, however, with great overhead. Thus, work on the topic of energy efficient cooperative schemes gained more interest, which resulted in a number of energy efficient cooperative algorithm proposals. In this work, we try to classify the possible directions in energy efficient CSS and present a limited set of works introducing new ideas to an energy efficient CSS algorithm.


title: An SELinux-based intent manager for Android

abstract: The support for Mandatory Access Control offered by SELinux has become a significant component of the security design of the Android operating system, offering robust protection and the ability to support system-level policies enforced by all the elements of the system. A well-known security-sensitive aspect of Android that currently SELinux does not cover is the abuse of intents, which represent the Android approach to inter-process communication. We propose SEIntentFirewall, an SELinux intent manager that provides fine-grained access control over Intent objects, permitting to cover within MAC policies the use of intents.


title: Identifying domain-dependent influential microblog users: a post-feature based approach

abstract: Users of a social network like to follow the posts published by influential users. Such posts usually are delivered quickly and thus will produce a strong influence on public opinions. In this paper, we focus on the problem of identifying domain-dependent influential users(or topic experts). Some of traditional approaches are based on the post contents of users users to identify influential users, which may be biased by spammers who try to make posts related to some topics through a simple copy and paste. Others make use of user authentication information given by a service platform or user self description (introduction or label) in finding influential users. However, what users have published is not necessarily related to what they have registed and described. In addition, if there is no comments from other users, its less objective to assess a users post quality. To improve effectiveness of recognizing influential users in a topic of microblogs, we propose a post-feature based approach which is supplementary to post-content based approaches. Our experimental results show that the post-feature based approach produces relatively higher precision than that of the content based approach.


title: Hardware accelerator for real-time image resizing

abstract: An accurate, hardware efficient and fast image rescaling unit is a crucial part of any real-time image processing system. Although there are a number of image scaling algorithms existing in the literature but Bicubic and Bilinear interpolation algorithms are most widely used. In the recent years, numerous algorithms have been proposed that aim to bridge the gap between these two standard algorithms, by attempting to provide high image quality of the former while maintaining the computational simplicity of the latter. This paper proposes a novel image resizing algorithm which uses a four-part piecewise linear function that closely mimics the behavior of the bicubic kernel, and thus provides high quality resized images comparable to Bicubic interpolation while having a much lower computational cost. An optimized architecture is proposed to implement this algorithm which features a high re-use of hardware units for coefficient generation so that the hardware cost is comparable to that of bilinear interpolation. The architecture and algorithm have been designed in tandem so as to meet the real-time requirements of applications such as automated video surveillance, requiring minimal hardware use without compromising on image quality.


title: Intersubject differences in false nonmatch rates for a fingerprint-based authentication system

abstract: The intersubject dependencies of false nonmatch rates were investigated for a minutiae-based biometric authentication process using single enrollment and verification measurements. A large number of genuine comparison scores were subjected to statistical inference tests that indicated that the number of false nonmatches depends on the subject and finger under test. This result was also observed if subjects associated with failures to enroll were excluded from the test set. The majority of the population (about 90%) showed a false nonmatch rate that was considerably smaller than the average false nonmatch rate of the complete population. The remaining 10% could be characterized as "goats" due to their relatively high probability for a false nonmatch. The image quality reported by the template extraction module only weakly correlated with the genuine comparison scores. When multiple verification attempts were investigated, only a limited benefit was observed for "goats," since the conditional probability for a false nonmatch given earlier nonsuccessful attempts increased with the number of attempts. These observations suggest that (1) there is a need for improved identification of "goats" during enrollment (e.g., using dedicated signal-driven analysis and classification methods and/or the use of multiple enrollment images) and (2) there should be alternative means for identity verification in the biometric system under test in case of two subsequent false nonmatches.


HISTOLOGICAL GRADING AND PROGNOSIS IN BREAST CANCER A STUDY OF 1409 CASES OF WHICH 359 HAVE BEEN FOLLOWED FOR 15 YEARS


title: Honeycyber: Automated signature generation for zero-day polymorphic worms

abstract: Signature-based intrusion detection systems (IDSs) can be evaded by polymorphic worms which vary their payloads in every infection attempt. In this paper, we propose Honeycyber, a system for automated signature generation for zero-day polymorphic worms. We have designed a novel double-Honeynet system, which is able to automatically detect new worms and isolate the attack traffic from innocuous traffic. We introduce unlimited Honeynet outbound connections, which allow us to capture different payloads in every infection of the same worm. The system is able to generate signatures to match most polymorphic worm instances with low false positives and low false negatives.


title: Content-based approach for Vietnamese spam SMS filtering

abstract: Short Message Service (SMS) spam is a serious problem in Vietnam because of the availability of very cheap pre-paid SMS packages. There are some systems to detect and filter spam messages for English, most of which use machine learning techniques to analyze the content of messages and classify them. For Vietnamese, there is some research on spam email filtering but none focused on SMS. In this work, we propose the first system for filtering Vietnamese spam SMS. We first propose an appropriate preprocessing method since existing tools for Vietnamese preprocessing cannot give good accuracy on our dataset. We then experiment with vector representations and classifiers to find the best model for this problem. Our system achieves an accuracy of 94% when labelling spam messages while the misclassification rate of legitimate messages is relatively small, about only 0.4%. This is an encouraging result compared to that of English and can be served as a strong baseline for future development of Vietnamese SMS spam prevention systems.


title: A low-power high-speed charge-steering ADC-based equalizer for serial links

abstract: This paper presents a 20-GSps low-power ADC-based equalizer for high speed serial links receiver. Digital receivers are recently adopted to overcome the challenges of power, delay and mismatches facing circuits in the analog domain besides utilizing benefits of the digital domain of scaling, adaptation algorithms, calibration and noise immunity. The ADC-based equalizer is designed and simulated in a 65-nm CMOS technology and dissipates 15.5 mW in the ADC and 0.45 mW in the discrete-time linear equalizer from 1-V supply. Low power consumption is achieved by using interleaving in ADC architecture, utilizing charge-steering concept, sharing single reference ladder across the four interleaved branches of ADC, and using a novel proposed design for the comparator itself in the Flash ADC besides using the novel Discrete Time Linear Equalizer-DTLE-circuit.


title: IIT-TUDA: System for Sentiment Analysis in Indian Languages Using Lexical Acquisition

abstract: Social networking platforms such as Facebook and Twitter have become a very popular communication tools among online users to share and express opinions and sentiment about the surrounding world. The availability of such opinionated text content has drawn much attention in the field of Natural Language Processing. Compared to other languages, such as English, little work has been done for Indian languages in this domain. In this paper, we present our contribution in classifying sentiment polarity for Indian tweets as a part of the shared task on Sentiment Analysis in Indian Languages SAIL 2015. With the support of a distributional thesaurus DTs and sentence level co-occurrences, we expand existing Indian sentiment lexicons to reach a higher coverage on sentiment words. Our system achieves an accuracy of 43.20i¾?% and 49.68i¾?% for the constrained submission, and an accuracy of 42.0i¾?% and 46.25i¾?% for the unconstrained setup for Bengali and Hindi, respectively. This puts our system in the first position for Bengali and in the third position for Hindi, amongst six participating teams.


title: The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification

abstract: We present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. BCM brings the intuitive power of CBR to a Bayesian generative framework. The BCM learns prototypes, the "quintessential" observations that best represent clusters in a dataset, by performing joint inference on cluster labels, prototypes and important features. Simultaneously, BCM pursues sparsity by learning subspaces, the sets of features that play important roles in the characterization of the prototypes. The prototype and subspace representation provides quantitative benefits in interpretability while preserving classification accuracy. Human subject experiments verify statistically significant improvements to participants' understanding when using explanations produced by BCM, compared to those given by prior art.


The Narrative Explorer


title: Automatic speech emotion recognition using modulation spectral features

abstract: In this study, modulation spectral features (MSFs) are proposed for the automatic recognition of human affective information from speech. The features are extracted from an auditory-inspired long-term spectro-temporal representation. Obtained using an auditory filterbank and a modulation filterbank for speech analysis, the representation captures both acoustic frequency and temporal modulation frequency components, thereby conveying information that is important for human speech perception but missing from conventional short-term spectral features. On an experiment assessing classification of discrete emotion categories, the MSFs show promising performance in comparison with features that are based on mel-frequency cepstral coefficients and perceptual linear prediction coefficients, two commonly used short-term spectral representations. The MSFs further render a substantial improvement in recognition performance when used to augment prosodic features, which have been extensively used for emotion recognition. Using both types of features, an overall recognition rate of 91.6% is obtained for classifying seven emotion categories. Moreover, in an experiment assessing recognition of continuous emotions, the proposed features in combination with prosodic features attain estimation performance comparable to human evaluation.


title: Predicting your next OLAP query based on recent analytical sessions

abstract: In Business Intelligence systems, users interact with data warehouses by formulating OLAP queries aimed at exploring multidimensional data cubes. Being able to predict the most likely next queries would provide a way to recommend interesting queries to users on the one hand, and could improve the efficiency of OLAP sessions on the other. In particular, query recommendation would proactively guide users in data exploration and improve the quality of their interactive experience. In this paper, we propose a framework to predict the most likely next query and recommend this to the user. Our framework relies on a probabilistic user behavior model built by analyzing previous OLAP sessions and exploiting a query similarity metric. To gain insight in the recommendation precision and on what parameters it depends, we evaluate our approach using different quality assessments.


title: A review of security frameworks for Internet of Things

abstract: By 2020, it's estimated that the amount of connected devices is predicted to grow exponentially to fifty billion. internet of things has gained intensive attention, the deployment of sensors, actuators are increasing at a speedy pace round the world. there's tremendous scope for a lot of efficient living through a rise of smart services, however this coincides with a rise in security and privacy issues. there's a desire to perform a scientific review of information security governance frameworks within the internet of things (IoT) Objective: The aim of this paper to evaluate systematic review of information security management frameworks that are related to the internet of things (IoT). it'll additionally discuss totally different information security frameworks that IoT models and deployments across different verticals. These frameworks are classified in line with the realm of the framework, the security executives and senior management of any enterprise that plans to start out mistreatment good services must outline a transparent governance strategy regarding the safety of their assets, this method review can facilitate them to create an improved decision for their investment for secure IoT deployments. Method — a set of standard criteria has been established to investigate which security framework are the most effective work among these classified security structures in notably for net of Things (IoT). the primary step to judge security framework by victimisation commonplace criteria methodology is to identify resources, the safety framework for IoT is chosen to be assessed in line with CCS. The second step is to develop a group of Security Targets (ST). The ST is that the set of criteria to use for the target of analysis (TOE). The third step is data extraction; fourth step data synthesis; and final step is to write-up study as a report. Conclusion — after reviewing four information security risk frameworks, this study makes some suggestions associated with information security risk governance in internet of Things (IoT). The organizations that have determined to maneuver to smart devices ought to outline the advantages and risks and preparation processes to manage security risk. the knowledge security risk policies ought to befits an organization's IT policies and standards to guard the confidentiality, integrity and handiness of knowledge security. The study observes a number of the most processes that are required to manage security risks. Moreover, the paper additionally drew attention on some suggestions which will assist corporations that square measure related to the knowledge security framework in internet of things (IoT).


title: SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision

abstract: In this paper, we propose a classifier for predicting message-level sentiments of English micro-blog messages from Twitter. Our method builds upon the convolutional sentence embedding approach proposed by (Severyn and Moschitti, 2015a; Severyn and Moschitti, 2015b). We leverage large amounts of data with distant supervision to train an ensemble of 2-layer convolutional neural networks whose predictions are combined using a random forest classifier. Our approach was evaluated on the datasets of the SemEval-2016 competition (Task 4) outperforming all other approaches for the Message Polarity Classification task.


Chapter 2 Broadband Vibration Energy Harvesting Techniques


title: Nonvolatile multibit SRAM, bit level caching, and multi-context computing for IoT

abstract: We propose a new memory hierarchy for energy conscious internet of things (IoT) integrated circuits. In this scheme a new type of SRAM cell — hybrid of SRAM/DRAM or SRAM/NVM memory — that is capable of storing multiple bits are used to bring caching to the bit level, near processor cores. This new scheme is expected to have energy efficiency advantage over traditional memory hierarchy for multi-context computation, especially suitable for many IoT applications.


title: Spam Email Classification using an Adaptive Ontology

abstract: Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of spam emails during the past few years. As spammers always try to find a way to evade existing filters, new filters need to be developed to catch spam. Ontologies allow for machine-understandable semantics of data. It is important to share information with each other for more effective spam filtering. Thus, it is necessary to build ontology and a framework for efficient email filtering. Using ontology that is specially designed to filter spam, bunch of unsolicited bulk email could be filtered out on the system. Similar to other filters, the ontology evolves with the user requests. Hence the ontology would be customized for the user. This paper proposes to find an efficient spam email filtering method using adaptive ontology


title: CORE: Context-Aware Open Relation Extraction with Factorization Machines

abstract: We propose CORE, a novel matrix factorization model that leverages contextual information for open relation extraction. Our model is based on factorization machines and integrates facts from various sources, such as knowledge bases or open information extractors, as well as the context in which these facts have been observed. We argue that integrating contextual information—such as metadata about extraction sources, lexical context, or type information—significantly improves prediction performance. Open information extractors, for example, may produce extractions that are unspecific or ambiguous when taken out of context. Our experimental study on a large real-world dataset indicates that CORE has significantly better prediction performance than state-ofthe-art approaches when contextual information is available.


title: A Stable Mode-Transition Technique for a Digitally Controlled Non-Inverting Buck–Boost DC–DC Converter

abstract: The propagation delay of a comparator and dead time causes the duty-discontinuity region near the boundary of the step-down and step-up regions in a non-inverting buck-boost (NIBB) converter. The duty- discontinuity region leads to an unstable output voltage and an unpredictable output voltage ripple, which might cause the entire power system to shut down. In this pa- per, a mode-transition technique called duty-lock control is proposed for a digitally controlled NIBB converter. It locks the duty cycle and eliminates the error between the output voltage and the reference signal by using a proposed fixed reference scheme that ensures the stability of the digital controller and output voltage. The experimental results that were applied to a field-programmable gate array-based plat- form revealed that the output voltage of the NIBB converter is stable throughout the entire transition region, without any efficiency tradeoffs. The input voltage of the converter that was provided by a Li-ion battery was 2.7-4.2 V, and the output voltage was 1.0-3.6 V, which is suitable for radio-frequency power amplifiers. The switching frequency was 500 kHz, and the maximum load current was 450 mA.


title: Hooked up to the international artistic community: external linkages, absorptive capacity and exporting by small creative firms

abstract: The contemporary economy is marked both by the growth in international trade and the increasing importance of the creative industries. However, these activities are dominated by big firms and big cities, spurring a large body of research into creative cities in the last decade. Going against this trend, this paper examines how small creative firms in small cities can successfully export their products. Drawing on survey data from 464 small firms in the creative industries in the county of Rogaland, Norway, it examines how cooperation with partners and active strategies for identifying and absorbing knowledge from these partners affect the firms' ability to sell their products abroad. Firms with extensive collegial linkages, especially those that cooperate with partners in the international artistic community, are particularly successful at exporting. However, cooperation within the national artistic community may have a negative effect on exporting. Furthermore, having strategies for absorbing knowledge f...


title: Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification

abstract: Person re-identification has been usually solved as either the matching of single-image representation (SIR) or the classification of cross-image representation (CIR). In this work, we exploit the connection between these two categories of methods, and propose a joint learning frame-work to unify SIR and CIR using convolutional neural network (CNN). Specifically, our deep architecture contains one shared sub-network together with two sub-networks that extract the SIRs of given images and the CIRs of given image pairs, respectively. The SIR sub-network is required to be computed once for each image (in both the probe and gallery sets), and the depth of the CIR sub-network is required to be minimal to reduce computational burden. Therefore, the two types of representation can be jointly optimized for pursuing better matching accuracy with moderate computational cost. Furthermore, the representations learned with pairwise comparison and triplet comparison objectives can be combined to improve matching performance. Experiments on the CUHK03, CUHK01 and VIPeR datasets show that the proposed method can achieve favorable accuracy while compared with state-of-the-arts.


title: Towards the bio-personalization of music recommendation systems: A single-sensor EEG biomarker of subjective music preference

abstract: Mobile neuroimaging leads to a whole new era of brain-oriented consumer applications.A single-sensor EEG biomarker to assess subjective music preference is introduced.It can facilitate the bio-personalization of modern music recommendation systems. Recent advances in biosensors technology and mobile electroencephalographic (EEG) interfaces have opened new application fields for cognitive monitoring. A computable biomarker for the assessment of spontaneous aesthetic brain responses during music listening is introduced here. It derives from well-established measures of cross-frequency coupling (CFC) and quantifies the music-induced alterations in the dynamic relationships between brain rhythms. During a stage of exploratory analysis, and using the signals from a suitably designed experiment, we established the biomarker, which acts on brain activations recorded over the left prefrontal cortex and focuses on the functional coupling between high-β and low-γ oscillations. Based on data from an additional experimental paradigm, we validated the introduced biomarker and showed its relevance for expressing the subjective aesthetic appreciation of a piece of music. Our approach resulted in an affordable tool that can promote human-machine interaction and, by serving as a personalized music annotation strategy, can be potentially integrated into modern flexible music recommendation systems.


title: Improving Gender Classification of Blog Authors

abstract: The problem of automatically classifying the gender of a blog author has important applications in many commercial domains. Existing systems mainly use features such as words, word classes, and POS (part-of-speech) n-grams, for classification learning. In this paper, we propose two new techniques to improve the current result. The first technique introduces a new class of features which are variable length POS sequence patterns mined from the training data using a sequence pattern mining algorithm. The second technique is a new feature selection method which is based on an ensemble of several feature selection criteria and approaches. Empirical evaluation using a real-life blog data set shows that these two techniques improve the classification accuracy of the current state-of-the-art methods significantly.


title: LDA-AdaBoost.MH: Accelerated AdaBoost.MH based on latent Dirichlet allocation for text categorization

abstract: AdaBoost.MH is a boosting algorithm that is considered to be one of the most accurate algorithms for multilabel classification. It works by iteratively building a committee of weak hypotheses of decision stumps. To build the weak hypotheses, in each iteration, AdaBoost.MH obtains the whole extracted features and examines them one by one to check their ability to characterize the appropriate category. Using Bag-Of-Words for text representation dramatically increases the computational time of AdaBoost.MH learning, especially for large-scale datasets. In this paper we demonstrate how to improve the efficiency and effectiveness of AdaBoost.MH using latent topics, rather than words. A well-known probabilistic topic modelling method, Latent Dirichlet Allocation, is used to estimate the latent topics in the corpus as features for AdaBoost.MH. To evaluate LDA-AdaBoost.MH, the following four datasets have been used: Reuters-21578-ModApte, WebKB, 20-Newsgroups and a collection of Arabic news. The experimental results confirmed that representing the texts as a small number of latent topics, rather than a large number of words, significantly decreased the computational time of AdaBoost.MH learning and improved its performance for text categorization.


title: On Orthogonal and Superimposed Pilot Schemes in Massive MIMO NOMA Systems

abstract: This paper is concerned with pilot transmission schemes in a large antenna system with non-orthogonal multiple-access (NOMA). We investigate two pilot structures—orthogonal pilot (OP) and superimposed pilot (SP). In OP, pilots occupy dedicated time (or frequency) slots, while in SP, pilots are superimposed with data. We study an iterative data-aided channel estimation (IDACE) receiver, where partially decoded data are used to refine channel estimation. We analyze the achievable rates for systems with IDACE receivers for both OP and SP. We show that the optimal portion of pilot power tends to zero for SP with Gaussian signaling. This result is consistent with existing findings obtained via the replica method in statistical physics. The latter involves multiple codes, which is convenient for theoretical analysis but difficult to implement. As a comparison, IDACE is potentially implementable in practice. We demonstrate that, with code optimization, SP can outperform OP in a high mobility environment with a large number of users. We provide numerical examples to verify our analysis.


title: Estimating individual treatment effect: generalization bounds and algorithms

abstract: There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine. We give a new theoretical analysis and family of algorithms for predicting individual treatment effect (ITE) from observational data, under the assumption known as strong ignorability. The algorithms learn a "balanced" representation such that the induced treated and control distributions look similar. We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation. We use Integral Probability Metrics to measure distances between distributions, deriving explicit bounds for the Wasserstein and Maximum Mean Discrepancy (MMD) distances. Experiments on real and simulated data show the new algorithms match or outperform the state-of-the-art.


title: Q-Learning in Continuous State and Action Spaces

abstract: Q-learning can be used to learn a control policy that maximises a scalar reward through interaction with the environment. Q- learning is commonly applied to problems with discrete states and actions. We describe a method suitable for control tasks which require continuous actions, in response to continuous states. The system consists of a neureil network coupled with a novel interpolator. Simulation results are presented for a non-holonomic control task. Advantage Learning, a variation of Q-learning, is shown enhance learning speed and reliability for this task.


title: A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models

abstract: We present a novel approach to learn binary classifiers when only positive and unlabeled instances are available (PU learning). This problem is routinely cast as a supervised task with label noise in the negative set. We use an ensemble of SVM models trained on bootstrap resamples of the training data for increased robustness against label noise. The approach can be considered in a bagging framework which provides an intuitive explanation for its mechanics in a semi-supervised setting. We compared our method to state-of-the-art approaches in simulations using multiple public benchmark data sets. The included benchmark comprises three settings with increasing label noise: (i) fully supervised, (ii) PU learning and (iii) PU learning with false positives. Our approach shows a marginal improvement over existing methods in the second setting and a significant improvement in the third.


title: A Low Cost Smart Pollution Measurement System Using REST API and ESP32

abstract: The ongoing trends in technology enable the communication between devices through wired or wireless medium. The Internet of Things (IoT) and Machine to Machine (M2M) communication are such trends in technology which make the networked devices to communicate and performs the action without human interventions. Moreover, one problem is debilitating to air quality and its measurement in Indian cities. The major contributors to air pollution are the smog, sulfur dioxide, nitrogen oxides, CO, PM10, PM2.5. These gases are found as the most important in terms of human health Problems. It can cause lung cancer, asthma, breathing problems. In this proposed system, MQ-9 is used for measuring CO, LPG, CH4 value, DHT22 is used for measuring temperature in degree celsius and humidity in percentage, dust sensor is used for measuring dust particles MQ-135 is used for measuring benzene, ammonia, sulfur dioxide in the Environment. This measured data is put on to the server by using REST protocol (REpresentational State Transfer protocol)and ESP32. All sensors readings are displayed on the webpage and mobile application.


title: Macro process of knowledge management for continuous innovation

abstract: Purpose – The purpose of this research is to explore the mechanisms of knowledge management (KM) for innovation and provide an approach for enterprises to leverage KM activities into continuous innovation.Design/methodology/approach – By reviewing the literature from multidisciplinary fields, the concepts of knowledge, KM and innovation are investigated. The physical, human and technological perspectives of KM are distinguished with the identification of two core activities for innovation: knowledge creation and knowledge usage. Then an essential requirement for continuous innovation – an internalization phase – is defined. The systems thinking and human‐centered perspectives are adopted for providing a comprehensive understanding about the mechanisms of KM for innovation.Findings – A networking process of continuous innovation based on KM is proposed by incorporating the phase of internalization. Three sources of organizational knowledge assets in innovation are identified. Then, based on the two core ac...


title: Visual simultaneous localization and mapping: a survey

abstract: Visual SLAM (simultaneous localization and mapping) refers to the problem of using images, as the only source of external information, in order to establish the position of a robot, a vehicle, or a moving camera in an environment, and at the same time, construct a representation of the explored zone. SLAM is an essential task for the autonomy of a robot. Nowadays, the problem of SLAM is considered solved when range sensors such as lasers or sonar are used to built 2D maps of small static environments. However SLAM for dynamic, complex and large scale environments, using vision as the sole external sensor, is an active area of research. The computer vision techniques employed in visual SLAM, such as detection, description and matching of salient features, image recognition and retrieval, among others, are still susceptible of improvement. The objective of this article is to provide new researchers in the field of visual SLAM a brief and comprehensible review of the state-of-the-art.


title: Accelerating simulation of agent-based models on heterogeneous architectures

abstract: The wide usage of GPGPU programming models and compiler techniques enables the optimization of data-parallel programs on commodity GPUs. However, mapping GPGPU applications running on discrete parts to emerging integrated heterogeneous architectures such as the AMD Fusion APU and Intel Sandy/Ivy bridge with the CPU and the GPU on the same die has not been well studied. Classic time-step simulation applications represented by agent-based models have the intrinsic parallel structure that is a good fit for GPGPU architectures. However, when mapping these applications directly to the integrated GPUs, the performance may degrade due to less computation units and lower clock speed. This paper proposes an optimization to the GPGPU implementation of the agent-based model and illustrates it in the traffic simulation example. The optimization adapts the algorithm by moving part of the workload to the CPU to leverage the integrated architecture and the on-chip memory bus which is faster than the PCIe bus that connects the discrete GPU and the host. The experiments on discrete AMD Radeon GPU and AMD Fusion APU demonstrate that the optimization can achieve 1.08--2.71x performance speedup on the integrated architecture over the discrete platform.


title: Vehicle Stability Enhancement through Hierarchical Control for a Four-Wheel-Independently-Actuated Electric Vehicle

abstract: In this paper, an optimal control strategy for a four-wheel-independently-actuated electric vehicle (FWIA EV) is proposed to improve vehicle dynamics stability and handling performance. The proposed scheme has a hierarchical structure composed of an upper and a lower controller. The desired longitudinal and lateral forces and yaw moment are determined based on the sliding-mode control (SMC) scheme in the upper controller, which takes the longitudinal and lateral velocity and the yaw rate as control variables. In the lower controller, an optimization algorithm is adopted to allocate the driving/braking torques to each in-wheel motor. A cost function with adjustable weight coefficients is specially designed by taking the motor power capability and the tire workload into consideration. The simulation and hardware-in-loop experimental results show that the proposed control strategy exhibits superior performance in comparison to commonly-used rule-based control strategies, and has the capability of online implementation.


title: Every Goodbye Ain't Gone: An Anthology of Innovative Poetry by African Americans

abstract: Just prior to the Second World War, and even more explosively in the 1950s and 1960s, a far-reaching revolution in aesthetics and prosody by black poets ensued, some working independently and others in organized groups. Little of this new work was reflected in the anthologies and syllabi of college English courses of the period. Even during the 1970s, when African American literature began to receive substantial critical attention, the work of many experimental black poets continued to be neglected. "Every Goodbye Ain't Gone" presents the groundbreaking work of many of these poets who carried on the innovative legacies of Melvin Tolson, Gwendolyn Brooks, and Robert Hayden. Whereas poetry by key figures such as Amirt Baraka, Tolson, Jayne Cortez, Clarence Major, and June Jordan is represented, this anthology also elevates into view the work of less studied poets such as Russell Atkins, Jodi Braxton, David Henderson, Bob Kaufman, Stephen Jonas, and Elouise Loftin. Many of the poems collected in the volume are currently unavailable and some will appear in print here for the first time. Coeditors Aldon Lynn Nielsen and Lauri Ramey provide a critical introduction that situates the poems historically and highlights the ways such poetry has been obscured from view by recent critical and academic practices. The result is a record of experimentation, instigation, and innovation that links contemporary African American poetry to its black modernist roots and extends the terms of modern poetics into the future.


title: Do cheerfulness, exhilaration, and humor production moderate pain tolerance? A FACS study

abstract: Prior studies have shown that watching a funny film leads to an increase in pain tolerance. The present study aimed at separating three factors considered potentially essential (mood, behavior, and cognition related to humor) and examined whether they are responsible for this effect. Furthermore, the study examined whether trait cheerfulness and trait seriousness, as measured by the State-Trait-Cheerfulness-Inventory (STCI; Ruch et al. 1996), moderate changes in pain tolerance. Fifty-sixty female subjects were assigned randomly to three groups, each having a different task to pursue while watching a funny film: (1) get into a cheerful mood without smiling or laughing ("Cheerfulness"); (2) smile and laugh extensively ("Exhilaration"); and (3) produce a humorous commentary to the film ("Humor production"). Pain tolerance was measured using the cold pressor test before, immediately after, and twenty minutes after the film. Results indicated that pain tolerance increased for participants from before to after watching the funny film and remained high for the twenty minutes. This effect was moderated by facial but not verbal indicators of enjoyment of humor. Participants low in trait seriousness had an overall higher pain tolerance. Subjects with a high score in trait cheerfulness showed an increase in pain tolerance after producing humor while watching the film whereas subjects low in trait cheerfulness showed a similar increase after smiling and laughter during the film.


title: Cost Effectiveness of Falls and Injury Prevention Strategies for Older Adults Living in Residential Aged Care Facilities

abstract: OBJECTIVE ::: To evaluate the cost effectiveness of interventions designed to prevent falls and fall-related injuries among older people living in residential aged care facilities (RACFs) from an Australian health care perspective. ::: ::: ::: METHODS ::: A decision analytic Markov model was developed that stratified individuals according to their risk of falling and accounted for the risk of injury following a fall. The effectiveness of the interventions was derived from two Cochrane reviews of randomized controlled trials for falls/fall-related injury prevention in RACFs. Interventions were considered effective if they reduced the risk of falling or reduced the risk of injury following a fall. The interventions that were modelled included vitamin D supplementation, annual medication review, multifactorial intervention (a combination of risk assessment, medication review, vision assessment and exercise) and hip protectors. The cost effectiveness was calculated as the incremental cost relative to the incremental benefit, in which the benefit was estimated using quality-adjusted life-years (QALYs). Uncertainty was explored using univariate and probabilistic sensitivity analysis. ::: ::: ::: RESULTS ::: Vitamin D supplementation and medication review both dominated 'no intervention', as these interventions were both more effective and cost saving (because of healthcare costs avoided). Hip protectors are dominated (less effective and more costly) by vitamin D and medication review. The incremental cost-effectiveness ratio (ICER) for medication review relative to vitamin D supplementation is AU$2442 per QALY gained, and the ICER for multifactorial intervention relative to medication review is AU$1,112,500 per QALY gained. The model is most sensitive to the fear of falling and the cost of the interventions. ::: ::: ::: CONCLUSION ::: The model suggests that vitamin D supplementation and medication review are cost-effective interventions that reduce falls, provide health benefits and reduce health care costs in older adults living in RACFs.


title: Social networking on smartphones: When mobile phones become addictive

abstract: As the penetration of mobile phones in societies increases, there is a large growth in the use of mobile phones especially among the youth. This trend is followed by the fast growth in use of online social networking services (SNS). Extensive use of technology can lead to addiction. This study finds that the use of SNS mobile applications is a significant predictor of mobile addiction. The result also shows that the use of SNS mobile applications is affected by both SNS network size and SNS intensity of the user. This study has implications for academia as well as governmental and non-for-profit organizations regarding the effect of mobile phones on individual's and public health.


title: A Single-Layer Frequency-Selective Surface for Ultrawideband Electromagnetic Shielding

abstract: An efficient approach to achieve the shielding effectiveness (SE) by using a frequency-selective surface (FSS) is presented. This FSS, which consists of cross dipoles and rings printed on the opposite sides of a single-layer FR-4 substrate, exhibits a wide, 7.5-GHz stopband to provide simultaneous shielding in both X- and Ka-bands. Experimental results confirm SE of the prototype over an ultra-wide band with more than 20-dB measured attenuation. The design is compact and suitable to provide shielding against the radiation interference caused by license-free and other radio systems.


title: Potential Applications For Total Internal Reflection Fibre Optic Sensors

abstract: A simple displacement fibre optic sensor is proposed which is based upon the principle of frustrated total internal reflection. The simple designs considered overcome the problems inherent with the small distances involved with frustrated total internal reflection.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Animation and the Role of Map Design in Scientific Visualization

abstract: Scientists visualize data for a range of purposes, from exploring unfamiliar data sets to communicating insights revealed by visual analyses. As the supply of numerical environmental data has increased, so has the need for effective visual methods, especially for exploratory data analysis. Map animation is particularly attractive to earth system scientists who typically study large spatio-temporal data sets. In addition to the "visual variables" of static maps, animated maps are composed of three basic design elements or "dynamic variables"–scene duration, rate of change between scenes, and scene order. The dynamic variables can be used to emphasize the location of a phenomenon, emphasize its attributes, or visualize change in its spatial, temporal, and attribute dimensions. In combination with static maps, graphs, diagrams, images, and sound, animation enhances analysts' ability to express data in a variety of complementary forms.


title: Isolated Boost DC–DC Converter With Three Switches

abstract: This paper documents a new three-switch, isolated boost dc–dc converter. The major features of the proposed converter are as follows: 1) continuous input current; 2) reduced one active switch, one additional diode, and one additional capacitor; 3) unchanged primary and secondary voltage waveforms of the transformer when the duty cycle is changed; and 4) removal of the snubber circuit. This paper presents the operating principles, analysis, parameter design guidelines, and simulation results for the proposed converter. To verify the performance of the proposed converter, a 400 W prototype was constructed with a 40–60 V dc input. A PID controller was used to maintain the dc output voltage at 400 V. The simulation and experimental results matched those of the theoretical analysis.


title: Combined 2D–3D categorization and classification for multimodal perception systems

abstract: In this article we describe an object perception system for autonomous robots performing everyday manipulation tasks in kitchen environments. The perception system gains its strengths by exploiting that the robots are to perform the same kinds of tasks with the same objects over and over again. It does so by learning the object representations necessary for the recognition and reconstruction in the context of pick-and-place tasks. The system employs a library of specialized perception routines that solve different, well-defined perceptual sub-tasks and can be combined into composite perceptual activities including the construction of an object model database, multimodal object classification, and object model reconstruction for grasping. We evaluate the effectiveness of our methods, and give examples of application scenarios using our personal robotic assistants acting in a human living environment.


title: Polyhedral Source-to-Source Compiler

abstract: This paper describes a tool which enables source to source compilation. Implemented Polyhedral Source-to-Source Compiler (PSSC) is based on Polly compiler and LLVM infrastructure and it enables automatic recognition of parallel regions of C/C++ code and annotating them with OpenMP / OpenACC pragmas. The analysis of the input code is done by Polly compiler and then the results are mapped to original source code. This approach allows automatic parallelization of old code, it also ensures high level of code portability and it provides the possibility of manual optimization of the output code. Experimental results of the PSSC compiler have shown that the proposed compiler is able to reach the comparable performance as the original Polly compiler does.


title: Context-aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity

abstract: There are various applications of physical activity monitoring for medical purposes, such as therapeutic rehabilitation, fitness enhancement or the use of physical activity as context information for evaluation of other vital data. Physical activity can be estimated using acceleration sensor-systems fixed on a person's body. By means of pattern recognition methods, it is possible to identify with certain accuracy which movement is being performed. This work presents a comparison of different methods for recognition of daily-life activities, which will serve as basis for the development of an online activity monitoring system.


title: It's Creepy, But it Doesn't Bother Me

abstract: Undergraduates interviewed about privacy concerns related to online data collection made apparently contradictory statements. The same issue could evoke concern or not in the span of an interview, sometimes even a single sentence. Drawing on dual-process theories from psychology, we argue that some of the apparent contradictions can be resolved if privacy concern is divided into two components we call intuitive concern, a "gut feeling," and considered concern, produced by a weighing of risks and benefits. Consistent with previous explanations of the so-called privacy paradox, we argue that people may express high considered concern when prompted, but in practice act on low intuitive concern without a considered assessment. We also suggest a new explanation: a considered assessment can override an intuitive assessment of high concern without eliminating it. Here, people may choose rationally to accept a privacy risk but still express intuitive concern when prompted.


title: Design and implementation of a Ka-band corrugated feed horn for reflector antennas

abstract: This paper presents the relationship between Gaussian beam and reflector antennas and deals with the most important conical corrugated feed horn design parameters for reflector antennas. Furthermore, a compact, easily to manufacture and easily to frequency-scale conical corrugated horn with more than 40% relative bandwidth is proposed. The hybrid-mode (HE11) conical corrugated feed horn operating from 26.5 to 40GHz has been designed, fabricated and measured.


Fall Detection using Recurrent Neural Networks


Marginals for Exploring and Using Dependency Parses


Asymmetric Discrete Graph Hashing.


title: Multifunction Cognitive Radar Task Scheduling Using Monte Carlo Tree Search and Policy Networks

abstract: A modern radar may be designed to perform multiple functions, such as surveillance, tracking, and fire control. Each function requires the radar to execute a number of transmit-receive tasks. A radar resource management (RRM) module makes decisions on parameter selection, prioritization, and scheduling of such tasks. RRM becomes especially challenging in overload situations, where some tasks may need to be delayed or even dropped. In general, task scheduling is an NP-hard problem. In this work, we develop the branch-and-bound (B&B) method which obtains the optimal solution but at exponential computational complexity. On the other hand, heuristic methods have low complexity but provide relatively poor performance. We resort to machine learning-based techniques to address this issue; specifically we propose an approximate algorithm based on the Monte Carlo tree search method. Along with using bound and dominance rules to eliminate nodes from the search tree, we use a policy network to help to reduce the width of the search. Such a network can be trained using solutions obtained by running the B&B method offline on problems with feasible complexity. We show that the proposed method provides near-optimal performance, but with computational complexity orders of magnitude smaller than the B&B algorithm.


Building Task-Oriented Dialogue Systems for Online Shopping


Gut-Microbiota and Mental Health : Current and Future Perspectives


title: Segmentation of overlapping/aggregating nuclei cells in biological images

abstract: This paper presents a method of overlapping/aggregating nuclei cells segmentation. This method is based on the watershed segmentation algorithm, but the specificity of this work is to introduce some prior information about the usual shape of normal/pathological nuclei cells. Such prior information will help to optimize the right set of markers, from which the flooding will be done. This approach has been implemented and tested, and the results evaluated by cell biology experts show the efficiency of the proposed approach.


title: On the Control of the Berkeley Lower Extremity Exoskeleton (BLEEX)

abstract: The first functional load-carrying and energetically autonomous exoskeleton was demonstrated at U.C. Berkeley, walking at the average speed of 1.3 m/s while carrying a 34 kg (75 lb) payload. Four fundamental technologies associated with the Berkeley Lower Extremity Exoskeleton (BLEEX) were tackled during the course of this project. These four core technologies include: the design of the exoskeleton architecture, control schemes, a body local area network (bLAN) to host the control algorithm and an on-board power unit to power the actuators, sensors and the computers. This article gives an overview of one of the control schemes. The analysis here is an extension of the classical definition of the sensitivity function of a system: the ability of a system to reject disturbances or the measure of system robustness. The control algorithm developed here increases the closed loop system sensitivity to its wearer’s forces and torques without any measurement from the wearer (such as force, position, or electromyogram signal). The control method has little robustness to parameter variations and therefore requires a relatively good dynamic model of the system. The tradeoffs between having sensors to measure human variables and the lack of robustness to parameter variation are described.


title: Invariant Scattering Convolution Networks.

abstract: A wavelet scattering network computes a translation invariant image representation, which is stable to deformations and preserves high frequency information for classification. It cascades wavelet transform convolutions with non-linear modulus and averaging operators. The first network layer outputs SIFT-type descriptors whereas the next layers provide complementary invariant information which improves classification. The mathematical analysis of wavelet scattering networks explain important properties of deep convolution networks for classification. A scattering representation of stationary processes incorporates higher order moments and can thus discriminate textures having same Fourier power spectrum. State of the art classification results are obtained for handwritten digits and texture discrimination, with a Gaussian kernel SVM and a generative PCA classifier.


title: Nested Autonomy for Unmanned Marine Vehicles with MOOS-IvP

abstract: This document describes the MOOS-IvP autonomy software for unmanned marine vehicles and its use in large-scale ocean sensing systems. MOOS-IvP is composed of two open-source software projects funded by the Office of Naval Research. MOOS provides a core autonomy middleware capability, and the MOOS project additionally provides a set of ubiquitous infrastructure utilities. The IvP Helm is the primary component of an additional set of capabilities implemented to form a full marine autonomy suite known as MOOS-IvP. This software and architecture are platform and mission agnostic and allow for a scalable nesting of unmanned vehicle nodes to form large-scale, long-endurance ocean sensing systems composed of heterogeneous platform types with varying degrees of communications connectivity, bandwidth, and latency. © 2010 Wiley Periodicals, Inc.


title: Development of the Corticothalamic Projections

abstract: In this review we discuss recent advances in the understanding of corticothalamic axon guidance; patterning of the early telencephalon, the sequence and choreography of the development of projections from subplate, layers 5 and 6. These cortical subpopulations display different axonal outgrowth kinetics and innervate distinct thalamic nuclei in a temporal pattern determined by cortical layer identity and subclass specificity. Guidance by molecular cues, structural cues, and activity-dependent mechanisms contribute to this development. There is a substantial rearrangement of the corticofugal connectivity outside the thalamus at the border of and within the reticular thalamic nucleus, a region that shares some of the characteristics of the cortical subplate during development. The early transient circuits are not well understood, nor the extent to which this developmental pattern may be driven by peripheral sensory activity. We hypothesize that transient circuits during embryonic and early postnatal development are critical in the matching of the cortical and thalamic representations and forming the cortical circuits in the mature brain.


title: The Effect of Macroeconomic Policies on Poverty in Iran

abstract: The objective of this study is to analysis the effectiveness of government intervention on poverty groups using a general equilibrium model. The social accounting matrix of year 2002 was used to estimate the GE model. The results indicate that absolute poverty line for the urban and rural regions are 3.7 and 2.4 million Rials respectively. Also the result shows that the majority of Iranian households are living under the poverty line. Thus, it is expected that the average propensity to consume among the households is high but on the other hand the average propensity to save is low.


title: Information Technologies Based Questionnaire Tool in Human-Automation Systems

abstract: In this paper we present an Information Technologies (IT) based questionnaire tool in order to support evaluation activities related to industrial automation. We assess the questionnaire tool by giving special attention to teaching activities at different universities and industrial cooperation, all of them within the industrial automation field. In an initial assessment, the use of an evaluation questionnaire tool within an academic platform allows members from different universities to collaborate and to share knowledge. By means of an example, the Barcelona Tech University in Spain provides support for teaching activities in automation to the ESPE University in Quito, Ecuador. A second case study example is presented in order to show how to detect sources of complexity in human supervisory control room tasks in wastewater treatment plants. The first results show that the interface is effective, easy to use and understand and can help engineering students in a training program to play the role of control room operators and the elicitation of sources of complexity can be useful to improve the interaction between humans and safety-critical systems in the industrial domain.


title: The biplot graphic display of matrices with application to principal component analysis

abstract: SUMMARY Any matrix of rank two can be displayed as a biplot which consists of a vector for each row and a vector for each column, chosen so that any element of the matrix is exactly the inner product of the vectors corresponding to its row and to its column. If a matrix is of higher rank, one may display it approximately by a biplot of a matrix of rank two which approximates the original matrix. The biplot provides a useful tool of data analysis and allows the visual appraisal of the structure of large data matrices. It is especially revealing in principal component analysis, where the biplot can show inter-unit distances and indicate clustering of units as well as display variances and correlations of the variables. Any matrix may be represented by a vector for each row and another vector for each column, so chosen that the elements of the matrix are the inner products of the vectors representing the corresponding rows and columns. This is conceptually helpful in understanding properties of matrices. When the matrix is of rank 2 or 3, or can be closely approximated by a matrix of such rank, the vectors may be plotted or modelled and the matrix representation inspected physically. This is of obvious practical interest for the analysis of large matrices. Any n x m matrix Y of rank r can be factorized as


title: An 11b 450 MS/s Three-Way Time-Interleaved Subranging Pipelined-SAR ADC in 65 nm CMOS

abstract: This paper presents an 11 bit 450 MS/s three-way time-interleaved (TI) subranging pipelined-successive approximation register (SAR) analog-to-digital converter (ADC). The proposed hybrid architecture combines the design benefits of different ADC structures to achieve a high conversion rate and accuracy with good power efficiency. The design employs multiple offset calibration schemes to compensate the offset mismatches at each stage. The solutions require less calibration efforts, thus allowing the ADC to achieve a compact area. Furthermore, a dynamic SAR controller embedded with error-decision-correction (EDC) logic is proposed to reduce large transition error. Measurement results on a 65 nm CMOS prototype operated at 450 MS/s and 1.2 V supply show 7.4 mW total power consumption with a peak signal-to-noise distortion ratio (SNDR) of 60.8 dB and an FOM of 32 fJ/conv.step at Nyquist.


A tutorial on training recurrent neural networks , covering BPPT , RTRL , EKF and the " echo state network " approach


title: A Contrast Enhancement-Based Filter for Removal of Random Valued Impulse Noise

abstract: This letter presents a new filtering scheme based on contrast enhancement within the filtering window for removing the random valued impulse noise. The application of a nonlinear function for increasing the difference between a noise-free and noisy pixels results in efficient detection of noisy pixels. As the performance of a filtering system, in general, depends on the number of iterations used, an effective stopping criterion based on noisy image characteristics to determine the number of iterations is also proposed. Extensive simulation results exhibit that the proposed method significantly outperforms many other well-known techniques.


title: A Literature Review on Circle and Sphere Packing Problems: Models and Methodologies

abstract: This paper reviews the most relevant literature on efficient models and methods for packing circular objects/items into Euclidean plane regions where the objects/items and regions are either two- or three-dimensional. These packing problems are NP hard optimization problems with a wide variety of applications. They have been tackled using various approaches-based algorithms ranging from computer-aided optimality ::: proofs, to branch-and-bound procedures, to constructive approaches, to multi-start nonconvex minimization, to billiard simulation, to multiphase heuristics, and metaheuristics.


title: Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection

abstract: To assure that an autonomous car is driving safely on public roads, its deep learning-based object detector should not only predict correctly, but show its prediction confidence as well. In this work, we present practical methods to capture uncertainties in object detection for autonomous driving. We propose a probabilistic 3D vehicle detector for Lidar point clouds that can model both classification and spatial uncertainty. Experimental results show that our method captures reliable uncertainties related to the detection accuracy, vehicle distance and occlusion. The results also show that we can improve the detection performance by 1%-5% by modeling the uncertainty.


title: AAT: a tool for accessing and analysing students' behaviour data in learning systems

abstract: In online learning environments, teachers and course designers often get little feedback about how students actually interact with and learn in online courses. Most of the learning systems used by educational institutions store comprehensive log data associated with students' behaviours and actions. However, these systems typically reveal or report on very general and limited information based on this data. In order to provide teachers and course designers with more detailed and meaningful information about students' behaviour and their use of learning resources within online courses, an analytics tool has been developed. The tool incorporates functionality to access and analyse data related to students' behaviours in learning systems. This tool can provide valuable information about students' learning processes allowing the identification of difficult or inappropriate learning material, and can therefore significantly contribute to the design of improved student support activities and resources.


title: A machine learning solution to assess privacy policy completeness: (short paper)

abstract: A privacy policy is a legal document, used by websites to communicate how the personal data that they collect will be managed. By accepting it, the user agrees to release his data under the conditions stated by the policy. Privacy policies should provide enough information to enable users to make informed decisions. Privacy regulations support this by specifying what kind of information has to be provided. As privacy policies can be long and difficult to understand, users tend not to read them. Because of this, users generally agree with a policy without knowing what it states and whether aspects important to him are covered at all. In this paper we present a solution to assist the user by providing a structured way to browse the policy content and by automatically assessing the completeness of a policy, i.e. the degree of coverage of privacy categories important to the user. The privacy categories are extracted from privacy regulations, while text categorization and machine learning techniques are used to verify which categories are covered by a policy. The results show the feasibility of our approach; an automatic classifier, able to associate the right category to paragraphs of a policy with an accuracy approximating that obtainable by a human judge, can be effectively created.


title: Social network analysis and mining to support the assessment of on-line student participation

abstract: There is a growing number of courses delivered using elearning environments and their online discussions play an important role in collaborative learning of students. Even in courses with a few number of students, there could be thousands of messages generated in a few months within these forums. Manually evaluating the participation of students in such case is a significant challenge, considering the fact that current e-learning environments do not provide much information regarding the structure of interactions between students. There is a recent line of research on applying social network analysis (SNA) techniques to study these interactions. Here we propose to exploit SNA techniques, including community mining, in order to discover relevant structures in social networks we generate from student communications but also information networks we produce from the content of the exchanged messages. With visualization of these discovered relevant structures and the automated identification of central and peripheral participants, an instructor is provided with better means to assess participation in the online discussions. We implemented these new ideas in a toolbox, named Meerkat-ED, which automatically discovers relevant network structures, visualizes overall snapshots of interactions between the participants in the discussion forums, and outlines the leader/peripheral students. Moreover, it creates a hierarchical summarization of the discussed topics, which gives the instructor a quick view of what is under discussion. We believe exploiting the mining abilities of this toolbox would facilitate fair evaluation of students' participation in online courses.


title: Restaurant Recommendation for Group of People in Mobile Environments Using Probabilistic Multi-criteria Decision Making

abstract: Since 1990s, with an advancement of network technology and the popularization of the Internet, information that people can access has proliferated, thus information recommendation has been investigated as an important issue. Because preference to information recommendation can be different as context that the users are related to, we should consider this context to provide a good service. This paper proposes the recommendation system that considers the preferences of group users in mobile environment and applied the system to recommendation of restaurants. Since mobile environment has plenty of uncertainty, our system have used Bayesian network which showed reliable performance with uncertain input to model individual user's preference. Also, restaurant recommendation mostly considers the preference of group users, so we have used AHP (Analytic Hierarchy Process) of multi-criteria decision making method to get the preference of group users from individual users' preferences. For experiments, we have assumed 10 different situations and compared the proposed method with random recommendation and simple rule-based recommendation. Finally, we have confirmed that the proposed system provides high usability with SUS (System Usability Scale).


title: TMG: A MATLAB Toolbox for Generating Term-Document Matrices from Text Collections

abstract: A wide range of computational kernels in data mining and information retrieval from text collections involve techniques from linear algebra. These kernels typically operate on data that are presented in the form of large sparse term-document matrices (tdm). We present TMG, a research and teaching toolbox for the generation of sparse tdms from text collections and for the incremental modification of these tdms by means of additions or deletions. The toolbox is written entirely in MATLAB, a popular problem-solving environment that is powerful in computational linear algebra, in order to streamline document preprocessing and prototyping of algorithms for information retrieval. Several design issues that concern the use of MATLAB sparse infrastructure and data structures are addressed. We illustrate the use of the tool in numerical explorations of the effect of stemming and different term-weighting policies on the performance of querying and clustering tasks.


Optimal Design of Shell-and-Tube Heat Exchangers by Different Strategies of Differential Evolution


title: Wireless Non-contact EEG/ECG Electrodes for Body Sensor Networks

abstract: A wireless EEG/ECG system using non-contact sensors is presented. The system consists of a set of simple capacitive electrodes manufactured on a standard printed circuit board that can operate through fabric or other insulation. Each electrode provides 46dB of gain over a .7-100Hz bandwidth with a noise level of 3.80uV RMS for high quality brain and cardiac recordings. Signals are digitized directly on top of the electrode and transmitted in a digital serial daisy chain, minimizing the number of wires required on the body. A small wireless base unit transmits EEG/ECG telemetry to a computer for storage and processing.


title: Stippling by example

abstract: In this work, we focus on stippling as an artistic style and discuss our technique for capturing and reproducing stipple features unique to an individual artist. We employ a texture synthesis algorithm based on the gray-level co-occurrence matrix (GLCM) of a texture field. This algorithm uses a texture similarity metric to generate stipple textures that are perceptually similar to input samples, allowing us to better capture and reproduce stipple distributions. First, we extract example stipple textures representing various tones in order to create an approximate tone map used by the artist. Second, we extract the stipple marks and distributions from the extracted example textures, generating both a lookup table of stipple marks and a texture representing the stipple distribution. Third, we use the distribution of stipples to synthesize similar distributions with slight variations using a numerical measure of the error between the synthesized texture and the example texture as the basis for replication. Finally, we apply the synthesized stipple distribution to a 2D grayscale image and place stipple marks onto the distribution, thereby creating a stippled image that is statistically similar to images created by the example artist.


title: Neural encoding of the speech envelope by children with developmental dyslexia

abstract: Abstract Developmental dyslexia is consistently associated with difficulties in processing phonology (linguistic sound structure) across languages. One view is that dyslexia is characterised by a cognitive impairment in the “phonological representation” of word forms, which arises long before the child presents with a reading problem. Here we investigate a possible neural basis for developmental phonological impairments. We assess the neural quality of speech encoding in children with dyslexia by measuring the accuracy of low-frequency speech envelope encoding using EEG. We tested children with dyslexia and chronological age-matched (CA) and reading-level matched (RL) younger children. Participants listened to semantically-unpredictable sentences in a word report task. The sentences were noise-vocoded to increase reliance on envelope cues. Envelope reconstruction for envelopes between 0 and 10 Hz showed that the children with dyslexia had significantly poorer speech encoding in the 0–2 Hz band compared to both CA and RL controls. These data suggest that impaired neural encoding of low frequency speech envelopes, related to speech prosody, may underpin the phonological deficit that causes dyslexia across languages.


title: Discovering complex matchings across web query interfaces: a correlation mining approach

abstract: To enable information integration, schema matching is a critical step for discovering semantic correspondences of attributes across heterogeneous sources. While complex matchings are common, because of their far more complex search space, most existing techniques focus on simple 1:1 matchings. To tackle this challenge, this paper takes a conceptually novel approach by viewing schema matching as correlation mining, for our task of matching Web query interfaces to integrate the myriad databases on the Internet. On this "deep Web," query interfaces generally form complex matchings between attribute groups (e.g., [author] corresponds to [first name, last name] in the Books domain). We observe that the co-occurrences patterns across query interfaces often reveal such complex semantic relationships: grouping attributes (e.g., [first name, last name]) tend to be co-present in query interfaces and thus positively correlated. In contrast, synonym attributes are negatively correlated because they rarely co-occur. This insight enables us to discover complex matchings by a correlation mining approach. In particular, we develop the DCM framework, which consists of data preparation, dual mining of positive and negative correlations, and finally matching selection. Unlike previous correlation mining algorithms, which mainly focus on finding strong positive correlations, our algorithm cares both positive and negative correlations, especially the subtlety of negative correlations, due to its special importance in schema matching. This leads to the introduction of a new correlation measure, $H$-measure, distinct from those proposed in previous work. We evaluate our approach extensively and the results show good accuracy for discovering complex matchings.


title: Double Bow-Tie Slot Antennas for Wideband Millimeter-Wave and Terahertz Applications

abstract: This paper presents millimeter-wave and terahertz double bow-tie slot antennas on a synthesized elliptical silicon lens. Two different antennas are designed to cover 0.1-0.3 and 0.2-0.6 THz, respectively. The double bow-tie slot antenna results in a wide impedance bandwidth and 78-97% Gaussian coupling efficiency over a 3:1 frequency range. A wideband coplanar-waveguide low-pass filter is designed using slow-wave techniques, and the measured filter response shows an S21 <; -25 dB over a 3:1 frequency range. Absolute gain measurements done at 100-300 GHz and 200-600 GHz confirm the wideband operation of this design. The double bow-tie slot antenna is intended to fill the gap between standard double-slot antennas and log periodic and sinuous antennas, with applications areas in radio astronomy and imaging systems.


title: The acceptance and use of a virtual learning environment in China

abstract: The success of a virtual learning environment (VLE) depends to a considerable extent on student acceptance and use of such an e-learning system. After critically assessing models of technology adoption, including the Technology Acceptance Model (TAM), TAM2, and the Unified Theory of Acceptance and Usage of Technology (UTAUT), we build a conceptual model to explain the differences between individual students in the level of acceptance and use of a VLE. This model extends TAM2 and includes subjective norm, personal innovativeness in the domain of information technology, and computer anxiety. Data were collected from 45 Chinese participants in an Executive MBA program. After performing satisfactory reliability and validity checks, the structural model was tested with the use of PLS. Results indicate that perceived usefulness has a direct effect on VLE use. Perceived ease of use and subjective norm have only indirect effects via perceived usefulness. Both personal innovativeness and computer anxiety have direct effects on perceived ease of use only. Implications are that program managers in education should not only concern themselves with basic system design but also explicitly address individual differences between VLE users.


title: A Novel Broadband Planar Antenna for 2G/3G/LTE Base Stations

abstract: A novel broadband planar antenna is developed for mobile communication base stations. The antenna is composed of a pair of folded dipoles which are coupling fed by an L-shaped microstrip line. Both the dipoles and the coupling microstrip line are etched on the same substrate. The planar antenna achieves a bandwidth of 53% for ${\rm return\ loss} > 15$ dB, covering the frequency range 1.65–2.85 GHz for 2G/3G/LTE applications. The antenna gain of the broadband antenna element is about 9 dBi. A ${\pm}45^{\circ}$ dual-polarized planar antenna consisting of two broadband antenna elements is proposed, which achieves a bandwidth of about 50% and an isolation of 30 dB. Two 8-element antenna arrays are developed respectively for the broadband antenna and for the ${\pm}45^{\circ}$ dual-polarized antenna. Both antenna arrays achieve a bandwidth of more than 58% (1.6–2.9 GHz). The antenna gains achieved for the both antenna arrays are higher than 15.5 dBi. The half-power beam widths in the horizontal plane for the antenna arrays are approximately $65\pm 10^{\circ}$ , suitable for base station applications.


title: Prognostic Physiology: Modeling Patient Severity in Intensive Care Units Using Radial Domain Folding

abstract: Real-time scalable predictive algorithms that can mine big health data as the care is happening can become the new “medical tests” in critical care. This work describes a new unsupervised learning approach, radial domain folding, to scale and summarize the enormous amount of data collected and to visualize the degradations or improvements in multiple organ systems in real time. Our proposed system is based on learning multi-layer lower dimensional abstractions from routinely generated patient data in modern Intensive Care Units (ICUs), and is dramatically different from most of the current work being done in ICU data mining that rely on building supervised predictive models using commonly measured clinical observations. We demonstrate that our system discovers abstract patient states that summarize a patient’s physiology. Further, we show that a logistic regression model trained exclusively on our learned layer outperforms a customized SAPS II score on the mortality prediction task.


abstract: From the Publisher: ::: As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years. Now, in Fundamentals of Artificial Neural Networks, he provides the first systematic account of artificial neural network paradigms by identifying clearly the fundamental concepts and major methodologies underlying most of the current theory and practice employed by neural network researchers. ::: Such a systematic and unified treatment, although sadly lacking in most recent texts on neural networks, makes the subject more accessible to students and practitioners. Here, important results are integrated in order to more fully explain a wide range of existing empirical observations and commonly used heuristics. There are numerous illustrative examples, over 200 end-of-chapter analytical and computer-based problems that will aid in the development of neural network analysis and design skills, and a bibliography of nearly 700 references. ::: Proceeding in a clear and logical fashion, the first two chapters present the basic building blocks and concepts of artificial neural networks and analyze the computational capabilities of the basic network architectures involved. Supervised, reinforcement, and unsupervised learning rules in simple nets are brought together in a common framework in chapter three. The convergence and solution properties of these learning rules are then treated mathematically in chapter four, using the "average learning equation" analysis approach. This organization of material makes it natural to switch into learning multilayer nets using backpropand its variants, described in chapter five. Chapter six covers most of the major neural network paradigms, while associative memories and energy minimizing nets are given detailed coverage in the next chapter. The final chapter takes up Boltzmann machines and Boltzmann learning along with other global search/optimization algorithms such as stochastic gradient search, simulated annealing, and genetic algorithms.


title: Addressing the concerns of the lacks family: quantification of kin genomic privacy

abstract: The rapid progress in human-genome sequencing is leading to a high availability of genomic data. This data is notoriously very sensitive and stable in time. It is also highly correlated among relatives. A growing number of genomes are becoming accessible online (e.g., because of leakage, or after their posting on genome-sharing websites). What are then the implications for kin genomic privacy? We formalize the problem and detail an efficient reconstruction attack based on graphical models and belief propagation. With this approach, an attacker can infer the genomes of the relatives of an individual whose genome is observed, relying notably on Mendel's Laws and statistical relationships between the nucleotides (on the DNA sequence). Then, to quantify the level of genomic privacy as a result of the proposed inference attack, we discuss possible definitions of genomic privacy metrics. Genomic data reveals Mendelian diseases and the likelihood of developing degenerative diseases such as Alzheimer's. We also introduce the quantification of health privacy, specifically the measure of how well the predisposition to a disease is concealed from an attacker. We evaluate our approach on actual genomic data from a pedigree and show the threat extent by combining data gathered from a genome-sharing website and from an online social network.


title: Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs

abstract: Previous objective speech quality assessment models, such as bark spectral distortion (BSD), the perceptual speech quality measure (PSQM), and measuring normalizing blocks (MNB), have been found to be suitable for assessing only a limited range of distortions. A new model has therefore been developed for use across a wider range of network conditions, including analogue connections, codecs, packet loss and variable delay. Known as perceptual evaluation of speech quality (PESQ), it is the result of integration of the perceptual analysis measurement system (PAMS) and PSQM99, an enhanced version of PSQM. PESQ is expected to become a new ITU-T recommendation P.862, replacing P.861 which specified PSQM and MNB.