title: Virtual Conditional Generative Adversarial Networks

abstract: When trained on multimodal image datasets, normal Generative Adversarial Networks (GANs) are usually outperformed by class-conditional GANs and ensemble GANs, but conditional GANs is restricted to labeled datasets and ensemble GANs lack efficiency. We propose a novel GAN variant called virtual conditional GAN (vcGAN) which is not only an ensemble GAN with multiple generative paths while adding almost zero network parameters, but also a conditional GAN that can be trained on unlabeled datasets without explicit clustering steps or objectives other than the adversary loss. Inside the vcGAN's generator, a learnable ``analog-to-digital converter (ADC)" module maps a slice of the inputted multivariate Gaussian noise to discrete/digital noise (virtual label), according to which a selector selects the corresponding generative path to produce the sample. All the generative paths share the same decoder network while in each path the decoder network is fed with a concatenation of a different pre-computed amplified one-hot vector and the inputted Gaussian noise. We conducted a lot of experiments on several balanced/imbalanced image datasets to demonstrate that vcGAN converges faster and achieves improved Frechet Inception Distance (FID). In addition, we show the training byproduct that the ADC in vcGAN learned the categorical probability of each mode and that each generative path generates samples of specific mode, which enables class-conditional sampling. Codes are available at \url{this https URL}


title: A Formal Object-Oriented Analysis for Software Reliability Design for Verification

abstract: This paper presents the OOA design step in a methodology which integrates automata-based model checking into a commercially supported OO software development process.We define and illustrate a set of design rules for OOA models with executable semantics, which lead to automata models with tractable state spaces. The design rules yield OOA models with functionally structured designs similar to those of hardware systems. These structures support model-checking through techniques known to be feasible for hardware. The formal OOA methodology, including the design rules, was applied to the design of NASA robot control software. Serious logical design errors that had eluded prior testing, were discovered in the course of model-checking.


title: The adwords problem: online keyword matching with budgeted bidders under random permutations

abstract: We consider the problem of a search engine trying to assign a sequence of search keywords to a set of competing bidders, each with a daily spending limit. The goal is to maximize the revenue generated by these keyword sales, bearing in mind that, as some bidders may eventually exceed their budget, not all keywords should be sold to the highest bidder. We assume that the sequence of keywords (or equivalently, of bids) is revealed on-line. Our concern will be the competitive ratio for this problem versus the off-line optimum. We extend the current literature on this problem by considering the setting where the keywords arrive in a random order. In this setting we are able to achieve a competitive ratio of 1-e under some mild, but necessary, assumptions. In contrast, it is already known that when the keywords arrive in an adversarial order, the best competitive ratio is bounded away from 1. Our algorithm is motivated by PAC learning, and proceeds in two parts: a training phase, and an exploitation phase.


title: MRI-Compatible Pneumatic Robot for Transperineal Prostate Needle Placement

abstract: Magnetic resonance imaging (MRI) can provide high-quality 3-D visualization of prostate and surrounding tissue, thus granting potential to be a superior medical imaging modality for guiding and monitoring prostatic interventions. However, the benefits cannot be readily harnessed for interventional procedures due to difficulties that surround the use of high-field (1.5T or greater) MRI. The inability to use conventional mechatronics and the confined physical space makes it extremely challenging to access the patient. We have designed a robotic assistant system that overcomes these difficulties and promises safe and reliable intraprostatic needle placement inside closed high-field MRI scanners. MRI compatibility of the robot has been evaluated under 3T MRI using standard prostate imaging sequences and average SNR loss is limited to 5%. Needle alignment accuracy of the robot under servo pneumatic control is better than 0.94 mm rms per axis. The complete system workflow has been evaluated in phantom studies with accurate visualization and targeting of five out of five 1 cm targets. The paper explains the robot mechanism and controller design, the system integration, and presents results of preliminary evaluation of the system.


The Rich Event Ontology


title: An evaluation of models for predicting opponent positions in first-person shooter video games

abstract: A well-known Artificial Intelligence (AI) problem in video games is designing AI-controlled humanoid characters. It is desirable for these characters to appear both skillful and believably human-like. Many games address the former objective by providing their agents with unfair advantages. Although challenging, these agents are frustrating to humans who perceive the AI to be cheating. In this paper we evaluate hidden semi-Markov models and particle filters as a means for predicting opponent positions. Our results show that these models can perform with similar or better accuracy than the average human expert in the game Counter-Strike: Source. Furthermore, the mistakes these models make are more human-like than perfect predictions.


title: Comparison of basal-like triple-negative breast cancer defined by morphology, immunohistochemistry and transcriptional profiles

abstract: Basal-like invasive breast cancer is an important clinical group because of its association with a triple-negative phenotype defined by the lack of expression of estrogen, progesterone and human epidermal growth factor receptors 2, relative lack of therapeutic options and poor prognosis. However, depending on the method used to define these lesions, morphological assessment, immunohistochemical markers or gene expression, a different set of tumors is captured. The aim of this study was to investigate the consequences of using different methodological approaches to define basal-like lesions among triple-negative breast carcinomas with regard to their clinicopathological features and patient outcome. The cohort consisted of 142 invasive breast cancers with a triple-negative receptor status. First, each was reviewed histologically and those with morphological basal-like features were characterized as 'Path-Basal'. Second, the 'Core Basal' immunohistochemical lesions, defined as cytokeratin 5/6 and/or epidermal growth factor receptor 1 positive, within the triple-negative breast cancers were identified, and third their classification based on gene expression profiling was retrieved and those in the molecular 'PAM50 basal-like' subtype recorded. A total of 116 basal-like breast cancers were identified among the 142 triple-negative breast cancers by at least one of these three classifications (80%), but only 13 samples were defined as basal-like with all three methods. None of these 13 tumors were associated with lymphovascular invasion. The 34 morphological 'Path-Basal' lesions were significantly associated with a lack of nodal metastases. Comparing the estimates of death in the three classifications, the highest risk of death was seen for the 'Core Basal' group. In this study, we highlight that the definition of basal-like breast cancer based on different methodologies varies significantly and does not identify the same lesions. This incomplete overlap of cases emphasizes the need for consistent or new approaches to improve precise identification.


title: TE: Clustering molecular dynamics trajectories: 1. Characterizing the Performance of different clustering algorithms

abstract: Molecular dynamics simulation methods produce trajectories of atomic positions (and optionally velocities and energies) as a function of time and provide a representation of the sampling of a given molecule's energetically accessible conformational ensemble. As simulations on the 10-100 ns time scale become routine, with sampled configurations stored on the picosecond time scale, such trajectories contain large amounts of data. Data-mining techniques, like clustering, provide one means to group and make sense of the information in the trajectory. In this work, several clustering algorithms were implemented, compared, and utilized to understand MD trajectory data. The development of the algorithms into a freely available C code library, and their application to a simple test example of random (or systematically placed) points in a 2D plane (where the pairwise metric is the distance between points) provide a means to understand the relative performance. Eleven different clustering algorithms were developed, ranging from top-down splitting (hierarchical) and bottom-up aggregating (including single-linkage edge joining, centroid-linkage, average-linkage, complete-linkage, centripetal, and centripetal-complete) to various refinement (means, Bayesian, and self-organizing maps) and tree (COBWEB) algorithms. Systematic testing in the context of MD simulation of various DNA systems (including DNA single strands and the interaction of a minor groove binding drug DB226 with a DNA hairpin) allows a more direct assessment of the relative merits of the distinct clustering algorithms. Additionally, means to assess the relative performance and differences between the algorithms, to dynamically select the initial cluster count, and to achieve faster data mining by "sieved clustering" were evaluated. Overall, it was found that there is no one perfect "one size fits all" algorithm for clustering MD trajectories and that the results strongly depend on the choice of atoms for the pairwise comparison. Some algorithms tend to produce homogeneously sized clusters, whereas others have a tendency to produce singleton clusters. Issues related to the choice of a pairwise metric, clustering metrics, which atom selection is used for the comparison, and about the relative performance are discussed. Overall, the best performance was observed with the average-linkage, means, and SOM algorithms. If the cluster count is not known in advance, the hierarchical or average-linkage clustering algorithms are recommended. Although these algorithms perform well, it is important to be aware of the limitations or weaknesses of each algorithm, specifically the high sensitivity to outliers with hierarchical, the tendency to generate homogenously sized clusters with means, and the tendency to produce small or singleton clusters with average-linkage.


Initializing Convolutional Filters with Semantic Features for Text Classification


title: NFC — Vulnerabilities and defense

abstract: Near Field Communication (NFC) has been in use for quite some time by many users in mobile devices. Its use is increasing by the rapid increase in the availability of the NFC enabled devices in the market. It enables data transfer by bringing the two devices in close proximity, about 3-5 inches. It is designed for integration with mobile phones, which can communicate with other phones (peer-to-peer) or read information on tags and cards (reader). An NFC device can also be put in card emulation mode, to offer compatibility with other contactless smart card standards. This enables NFC enabled smart-phones to replace traditional contactless plastic cards used in public transport ticketing, access control, ATMs and other similar applications. NFC is a new and innovative technology with futuristic uses, but technology comes at a price both in terms of financial effects as well as the maintenance costs. The most pertinent concern would be that how much vulnerable the new technology is. There had already been instances where the security of NFC has been put to questions. It is vulnerable to numerous kinds of attacks. This research paper will list down the basic working principles of NFC, the protocols involved, vulnerabilities reported so far and possible countermeasures against the weaknesses.


title: A Human-annotated Dataset for Evaluating Tweet Ranking Algorithms

abstract: Social media monitoring is now an essential part of brand management, political science, and news production. Automatic tweet ranking and content recommendation methods are required, in order to support human analysts in deriving useful insights from large-scale social media data. To facilitate the development and comparative evaluation of tweet ranking methods, a task for which re-tweets do not form a reliable gold standard, a new, openly available Twitter corpus has been created. A number of results for several popular recommendation algorithms are presented for this corpus.


title: Understanding Modern Banking Ledgers through Blockchain Technologies: Future of Transaction Processing and Smart Contracts on the Internet of Money

abstract: In this chapter we provide an overview of the concept of blockchain technology and its potential to disrupt the world of banking through facilitating global money remittance, smart contracts, automated banking ledgers and digital assets. In this regard, we first provide a brief overview of the core aspects of this technology, as well as the second-generation contract-based developments. From there we discuss key issues that must be considered in developing such ledger based technologies in a banking context.


title: Hardware Trojan detection for gate-level ICs using signal correlation based clustering

abstract: Malicious tampering of the internal circuits of ICs can lead to detrimental results. Insertion of Trojan circuits may change system behavior, cause chip failure or send information to a third party. This paper presents an information-theoretic approach for Trojan detection. It estimates the statistical correlation between the signals in a design, and explores how this estimation can be used in a clustering algorithm to detect the Trojan logic. Compared with the other algorithms, our tool does not require extensive logic analysis. We neither need the circuit to be brought to the triggering state, nor the effect of the Trojan payload to be propagated and observed at the output. Instead we leverage already available simulation data in this information-theoretic approach. We conducted experiments on the TrustHub benchmarks to validate the practical efficacy of this approach. The results show that our tool can detect Trojan logic with up to 100% coverage with low false positive rates.


title: Promoting personal responsibility for internet safety

abstract: Online safety is everyone's responsibility---a concept much easier to preach than to practice.


title: An Annotated Corpus of Film Dialogue for Learning and Characterizing Character Style

abstract: Interactive story systems often involve dialogue with virtual dramatic characters. However, to date most character dialogue is written by hand. One way to ease the authoring process is to (semi-)automatically generate dialogue based on film characters. We extract features from dialogue of film characters in leading roles. Then we use these character-based features to drive our language generator to produce interesting utterances. This paper describes a corpus of film dialogue that we have collected from the IMSDb archive and annotated for linguistic structures and character archetypes. We extract different sets of features using external sources such as LIWC and SentiWordNet as well as using our own written scripts. The automation of feature extraction also eases the process of acquiring additional film scripts. We briefly show how film characters can be represented by models learned from the corpus, how the models can be distinguished based on different categories such as gender and film genre, and how they can be applied to a language generator to generate utterances that can be perceived as being similar to the intended character model.


title: Adaptive emotion regulation mediates the relationship between self‐compassion and depression in individuals with unipolar depression

abstract: Objectives ::: To identify the mechanisms involved in the association between self-compassion and depression, we examined whether adaptive emotion regulation would mediate the relationship between self-compassion and depression in individuals with unipolar depression. Furthermore, we explored which specific emotion regulation skills would be most important in this relationship. ::: ::: Design and method ::: Sixty-nine individuals with unipolar depression were assessed with the Self-Compassion Scale and the Emotion Regulation Skills Questionnaire at baseline and with the Beck Depression Inventory-II 1 week later. ::: ::: Results ::: The results showed that successful application of emotion regulation skills mediates the association between self-compassion and depression. Among eight specific emotion regulation skills, only the ability to tolerate negative emotions was identified as a significant mediator in the self-compassion–depression relationship. ::: ::: Conclusions ::: These findings provide preliminary evidence that systematically fostering self-compassion might help depressed individuals cope with their symptoms by enhancing their abilities to tolerate undesired emotions. ::: ::: Practitioner points ::: Systematically fostering self-compassion through specific compassion-focused interventions might facilitate a reduction in depressive symptoms by improving the person's emotion regulation abilities, especially by improving his or her ability to tolerate negative emotions. ::: Hence, compassion-focused interventions might be particularly promising in depressed patients with a tendency to avoid negative emotions and deficits in tolerating them.


Development of dynamic submarine MV power cable design solutions for floating offshore renewable energy applications


Challenge Based Learning in Cybersecurity Education


title: Collocated interaction with flying robots

abstract: We introduce a socially motivated interaction technique with collocated flying robots (a quadrotor in our current prototype). Instead of the traditional remote interaction controllers often used when interacting with flying robots and UAVs, we explore the collocated interaction space and suggest a direct interaction technique motivated by social human-robot interaction themes. Our approach is inspired by the types of interaction humans have with birds, specifically falconeering, and is facilitated by gestures-based interaction, while the user is within the field of view of the flying robot. This paper outlines our research goals, task examples, and our overall design approach. The paper also discusses our current prototyping efforts, as well as a preliminary evaluation of our approach, performed through two design critiques, studying our collocated interaction technique concept, and its potential, drawbacks and benefits for users.


title: China ’ s Outward Direct Investment in Africa *

abstract: The empirical determinants of China's outward direct investment (ODI) in Africa are examined using an officially approved ODI dataset and a relatively new OECD–IMF format ODI dataset. China's ODI is found responding to the canonical economic determinants that include the market seeking motive, the risk factor, and the resources seeking motive. It is also affected by the intensity of trade ties and the presence of China's contracted projects. A host country's natural resources have an impact on China's decision on how much to invest in the country rather than on whether to invest in the country or not. China's drive for Africa's natural resources is mainly a recent phenomenon and, probably, became prominent after the “Going Global” policy adopted in 2002.


title: Duet: cloud scale load balancing with hardware and software

abstract: Load balancing is a foundational function of datacenter infrastructures and is critical to the performance of online services hosted in datacenters. As the demand for cloud services grows, expensive and hard-to-scale dedicated hardware load balancers are being replaced with software load balancers that scale using a distributed data plane that runs on commodity servers. Software load balancers offer low cost, high availability and high flexibility, but suffer high latency and low capacity per load balancer, making them less than ideal for applications that demand either high throughput, or low latency or both. In this paper, we present Duet, which offers all the benefits of software load balancer, along with low latency and high availability -- at next to no cost. We do this by exploiting a hitherto overlooked resource in the data center networks -- the switches themselves. We show how to embed the load balancing functionality into existing hardware switches, thereby achieving organic scalability at no extra cost. For flexibility and high availability, Duet seamlessly integrates the switch-based load balancer with a small deployment of software load balancer. We enumerate and solve several architectural and algorithmic challenges involved in building such a hybrid load balancer. We evaluate Duet using a prototype implementation, as well as extensive simulations driven by traces from our production data centers. Our evaluation shows that Duet provides 10x more capacity than a software load balancer, at a fraction of a cost, while reducing latency by a factor of 10 or more, and is able to quickly adapt to network dynamics including failures.


title: High frequency early in vitro flowering of Dendrobium Madame Thong-In (Orchidaceae)

abstract: We have successfully developed a method to induce early in vitro flowering of the self-pollinated seedlings of a tropical orchid hybrid, Dendrobium Madame Thong-In. Transition of vegetative shoot apical meristem to inflorescence meristem was observed when young protocorms were cultured in modified KC liquid medium. In contrast, protocorms cultured on Gelrite-solidified medium only produced axillary shoots and roots. CW was required to trigger the transitional shoot apical meristem and BA enhanced inflorescence stalk initiation and flower bud formation. However, normal flower development was deformed in liquid medium but developed fully upon transferring to two-layered (liquid over Gelrite-solidified) medium. Under optimal condition, in vitro flowering was observed about 5 months after seed sowing. Segregation of flower colours was observed in these seedlings and seedpods formed upon artificial pollination of the in vitro flowers.


title: Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach

abstract: Multipurpose small reservoirs are important for livelihoods in rural semi-arid regions. To manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage dynamics. This paper introduces a Bayesian approach for monitoring small reservoirs with radar satellite images. The newly developed growing Bayesian classifier has a high degree of automation, can readily be extended with auxiliary information and reduces the confusion error to the land-water boundary pixels. A case study has been performed in the Upper East Region of Ghana, based on Radarsat-2 data from November 2012 until April 2013. Results show that the growing Bayesian classifier can deal with the spatial and temporal variability in synthetic aperture radar (SAR) backscatter intensities from small reservoirs. Due to its ability to incorporate auxiliary information, the algorithm is able to delineate open water from SAR imagery with a low land-water contrast in the case of wind-induced Bragg scattering or limited vegetation on the land surrounding a small reservoir.


title: Big data analytics over encrypted datasets with seabed

abstract: Today, enterprises collect large amounts of data and leverage the cloud to perform analytics over this data. Since the data is often sensitive, enterprises would prefer to keep it confidential and to hide it even from the cloud operator. Systems such as CryptDB and Monomi can accomplish this by operating mostly on encrypted data; however, these systems rely on expensive cryptographic techniques that limit performance in true "big data" scenarios that involve terabytes of data or more. ::: ::: This paper presents Seabed, a system that enables efficient analytics over large encrypted datasets. In contrast to previous systems, which rely on asymmetric encryption schemes, Seabed uses a novel, additively symmetric homomorphic encryption scheme (ASHE) to perform large-scale aggregations efficiently. Additionally, Seabed introduces a novel randomized encryption scheme called Splayed ASHE, or SPLASHE, that can, in certain cases, prevent frequency attacks based on auxiliary data.


title: Repairing Programs with Semantic Code Search (T)

abstract: Automated program repair can potentially reduce debugging costs and improve software quality but recent studies have drawn attention to shortcomings in the quality of automatically generated repairs. We propose a new kind of repair that uses the large body of existing open-source code to find potential fixes. The key challenges lie in efficiently finding code semantically similar (but not identical) to defective code and then appropriately integrating that code into a buggy program. We present SearchRepair, a repair technique that addresses these challenges by (1) encoding a large database of human-written code fragments as SMT constraints on input-output behavior, (2) localizing a given defect to likely buggy program fragments and deriving the desired input-output behavior for code to replace those fragments, (3) using state-of-the-art constraint solvers to search the database for fragments that satisfy that desired behavior and replacing the likely buggy code with these potential patches, and (4) validating that the patches repair the bug against program test suites. We find that SearchRepair repairs 150 (19%) of 778 benchmark C defects written by novice students, 20 of which are not repaired by GenProg, TrpAutoRepair, and AE. We compare the quality of the patches generated by the four techniques by measuring how many independent, not-used-during-repair tests they pass, and find that SearchRepair-repaired programs pass 97.3% of the tests, on average, whereas GenProg-, TrpAutoRepair-, and AE-repaired programs pass 68.7%, 72.1%, and 64.2% of the tests, respectively. We conclude that SearchRepair produces higher-quality repairs than GenProg, TrpAutoRepair, and AE, and repairs some defects those tools cannot.


title: Implicit Learning: News From the Front

abstract: Can we learn without awareness? While the current consensus is most likely to be `no', there is, however, considerable ongoing debate about the role that consciousness plays in cognition and about the nature of consciousness itself. In this article, we review recent advances in the field of implicit learning, based on three perspectives: empirical findings (including neuropsychological evidence), methodological issues, and theoretical positions (including computational models). The overall picture that emerges is complex and reflects a field that is very much in flux: while it seems undeniable that cognition involves some form of unconscious processing, it is as yet unclear how to best separate conscious and unconscious influences on learning, and how to best think about the status of the `cognitive unconscious'. We suggest that implicit learning is best construed as a complex form of priming taking place in continuously learning neural systems, and that the distributional knowledge so acquired can be causally efficacious in the absence of awareness that this knowledge was acquired or that it is currently influencing processing, that is, in the absence of metaknowledge.


title: Characterization of Surface-Wave and Leaky-Wave Propagation on Wire-Medium Slabs and Mushroom Structures Based on Local and Nonlocal Homogenization Models

abstract: In this paper, a nonlocal homogenization model is proposed for the analysis of the spectrum of natural modes on sub-wavelength mushroom-type high-impedance surfaces composed of a capacitive grid connected to a grounded wire-medium (WM) slab. Modal characteristics of mushroom structures are studied in conjunction with the surface-wave and leaky-wave propagation on WM slabs based on local and nonlocal homogenization models, showing the importance of spatial dispersion (SD) in WM. It is shown that mushroom structures support proper real (bound) forward and backward modes, whose dispersion determines the stopband properties of the mushroom structure, and proper (exponentially decaying from the surface) and improper (exponentially growing from the surface) complex leaky-wave modes related to the backward and forward radiation, respectively. Results obtained by different homogenization models are compared leading to important conclusions. Specifically, an interesting observation concerns the mushroom structures with short vias, wherein the SD of the WM slab is significantly reduced, and the results of local and nonlocal homogenization models are in excellent agreement.


title: SVM-Based Comments Classification and Mining of Virtual Community: For Case of Sentiment Classification of Hotel Reviews

abstract: Comments of virtual communities are very import to the operation and development of an enterprise, through analysis the sentiment of the comments, we can mine the user's semantic orientation and preferences or hates, it is very helpful for enterprises to improve their products and services and for individuals to use these information rationally. This paper takes hotel review of the virtual community for example, to use key technologies of support vector machine (SVM) and sentiment classification to build a small domain sentiment corpus, and to propose a sentiment classification model based on the corpus to reach the purpose of feature dimension reduction. We choose a hotel reviews corpus as instance to validate the method we proposed in this paper, the results of the experiment shows that the accuracy of support vector machine algorithm has been enhanced by adding a user's sentiment corpus. This method can be fatherly extended to apply to other comments of the virtual communities.


title: Doing justice differently : is restorative justice appropriate for domestic violence?

abstract: The South African government has made significant commitments towards protecting victims' rights. In response to the alarmingly high rates of reported and unreported domestic violence incidents, policies on victims are to be found in a range of legislative and policy provisions interalia: Services Charter for Victims of Crime; Bill of Human Rights in the South African Constitution and the Domestic Violence Act 116 of 1998. Interest in domestic violence has been growing in the restorative justice movement for many years, particularly on the academic level. In most western countries the realisation that these cases pose higher risks for victims, the lack of resources to properly address the dynamics of domestic violence, that mediators / facilitators need to have a deeper understanding, knowledge and training when dealing with domestic violence issues and a reluctance on the part of the criminal justice system to refer cases to restorative justice processes has meant that actual work in restorative processes with victims of domestic violence has been scarce. However, the scenario in South Africa is slightly different. Statistics from the National Prosecuting Authority, as well as recent evaluations of referrals to restorative justice processes indicate that prosecutors are not averse to referring domestic violence cases, where it is deemed appropriate. A growing number of cases are being referred to restorative justice service providers such as 'The Justice and Restoration Programme' in Phoenix, KwaZulu-Natal. This article outlines the findings of a pilot study on how victims of domestic violence experience restorative justice processes (victim-offender mediation and victim-offender conferencing) and re-offending.


title: Ovicidal, larvicidal and adulticidal properties of Asparagus racemosus (Willd.) (Family: Asparagaceae) root extracts against filariasis (Culex quinquefasciatus), dengue (Aedes aegypti) and malaria (Anopheles stephensi) vector mosquitoes (Diptera: Culicidae)

abstract: Several diseases are associated to the mosquito–human interaction. Mosquitoes are the carriers of severe and well-known illnesses such as malaria, arboviral encephalitis, dengue fever, chikungunya fever, West Nile virus and yellow fever. These diseases produce significant morbidity and mortality in humans and livestock around the world. The present investigation was undertaken to study the ovicidal, larvicidal and adulticidal activities of crude hexane, ethyl acetate, benzene, chloroform and methanol extracts of root of Asparagus racemosus were assayed for their toxicity against three important vector mosquitoes, viz., Culex quinquefasciatus, Aedes aegypti and Anopheles stephensi (Diptera: Culicidae). The mean percent hatchability of the eggs was observed after 48 h post-treatment. The percent hatchability was inversely proportional to the concentration of extract and directly proportional to the eggs. All the five solvent extracts showed moderate ovicidal activity; however, the methanol extract showed the highest ovicidal activity. The methanol extract of Asparagus racemosus against Culex quinquefasciatus, Aedes aegypti and Anopheles stephensi exerted 100 % mortality (zero hatchability) at 375, 300 and 225 ppm, respectively. Control eggs showed 99–100 % hatchability. The larval mortality was observed after 24 h of exposure. All extracts showed moderate larvicidal effects; however, the highest larval mortality was found in methanol extract of root of Asparagus racemosus against the larvae of Culex quinquefasciatus, Aedes aegypti and Anopheles stephensi with the LC50 and LC90 values were 115.13, 97.71 and 90.97 ppm and 210.96, 179.92, and 168.82 ppm, respectively. The adult mortality was observed after 24 h recovery period. The plant crude extracts showed dose-dependent mortality. At higher concentrations, the adult showed restless movement for some times with abnormal wagging and then died. Among the extracts tested, the highest adulticidal activity was observed in methanol extract against Anopheles stephensi followed by Aedes aegypti and Culex quinquefasciatus with the LD50 and LD90 values were 120.44, 135.60, and 157.71 ppm and 214.65, 248.35, and 290.95 ppm, respectively. No mortality was recorded in the control. The finding of the present investigation revealed that the root extract of Asparagus racemosus possess remarkable ovicidal, larvicidal and adulticidal activity against medically important vector mosquitoes and this is the low cost and ideal eco-friendly approach for the control of mosquitoes. This is the first report on the mosquito ovicidal, larvicidal and adulticidal activities of the reported Asparagus racemosus root.


title: Evaluating Social Perception of Human-to-Robot Handovers Using the Robot Social Attributes Scale (RoSAS)

abstract: This work explores social perceptions of robots within the domain of human-to-robot handovers. Using the Robotic Social Attributes Scale (RoSAS), we explore how users socially judge robot receivers as three factors are varied: initial position of the robot arm prior to handover, grasp method employed by the robot when receiving a handover object trading off perceived object safety for time efficiency, and retraction speed of the arm following handover. Our results show that over multiple handover interactions with the robot, users gradually perceive the robot receiver as being less discomforting and having more emotional warmth. Additionally, we have found that by varying grasp method and retraction speed, users may hold significantly different judgments of robot competence and discomfort. With these results, we recognize empirically that users are able to develop social perceptions of robots which can change through modification of robot receiving behaviour and through repeated interaction with the robot. More widely, this work suggests that measurement of user social perceptions should play a larger role in the design and evaluation of human-robot interactions and that the RoSAS can serve as a standardized tool in this regard.


title: Development and implementation of a Nordic grid model for Power System small-signal and transient stability studies in a free and open source software

abstract: This article presents an implementation of a Nordic grid model in Power System Analysis Toolbox (PSAT) - a free and open-source software. A newly developed hydro turbine and hydro governor (HTG) model is implemented with this grid model and compared with the currently available PSAT turbine and governor models. Small-signal and transient stability analyses of the system using the two models are carried out and compared to demonstrate the difference and necessity of accurate hydro turbine and governor model utilization. The paper ends with a validation of the linearized Nordic grid model generated by PSAT including the newly implemented HTG models. This validation is done through nonlinear time-domain simulation by applying both large and small disturbances.


title: Iterating between Tools to Create and Edit Visualizations

abstract: A common workflow for visualization designers begins with a generative tool, like D3 or Processing, to create the initial visualization; and proceeds to a drawing tool, like Adobe Illustrator or Inkscape, for editing and cleaning. Unfortunately, this is typically a one-way process: once a visualization is exported from the generative tool into a drawing tool, it is difficult to make further, data-driven changes. In this paper, we propose a bridge model to allow designers to bring their work back from the drawing tool to re-edit in the generative tool. Our key insight is to recast this iteration challenge as a merge problem - similar to when two people are editing a document and changes between them need to reconciled. We also present a specific instantiation of this model, a tool called Hanpuku, which bridges between D3 scripts and Illustrator. We show several examples of visualizations that are iteratively created using Hanpuku in order to illustrate the flexibility of the approach. We further describe several hypothetical tools that bridge between other visualization tools to emphasize the generality of the model.


title: Reliability of the chronic mild stress model of depression: A user survey

abstract: Abstract The chronic mild stress (CMS) model of depression is considered by many to be the animal model of depression that has the greatest validity and translational potential, but it has often been criticized for a perceived lack of reliability. The aims of this study were to establish the extent to which the procedure is reproducible, and to identify experimental variables relevant to its reliability. Because failures to replicate frequently remain unpublished, a survey methodology was used. A questionnaire was circulated to 170 labs identified from a PubMed search as having published a CMS study in the years 2010 or 2015 (with no selection in respect of the results reported). Responses were returned by 71 (42%) of the recipients, followed by further correspondence with some of them. Most of the respondents (n = 53: 75%) reported that the CMS procedure worked reliably in their hands. Of the others, 15 (21%) reported that the procedure was usually reliable, but not always (n = 9: 13%) or not for all measures (n = 6: 8%). Only three respondents (4%) reported being unable to reproduce the characteristic effects, two of whom may be using an insufficient duration of CMS exposure. A series of analyses compared the 75% of ‘reliable’ labs with the 25% of ‘less reliable’ labs on a range of experimenter, subject, stress and outcome variables. Few if any significant differences between these two samples were identified, possibly because of the small size and diversity of the ‘less reliable’ sample. Two other limitations of the study include the (unavoidable) omission of labs that may have worked with the model but not published their data, and the use of ad hoc measures to compare the severity of different stress regimes. The results are discussed in relation to relevant published observations. It is concluded that CMS is in fact a rather robust model, but the factors that result in a less effective implementation in a minority of laboratories remain to be firmly established.


title: Multi-Resolution Climate Ensemble Parameter Analysis with Nested Parallel Coordinates Plots

abstract: Due to the uncertain nature of weather prediction, climate simulations are usually performed multiple times with different spatial resolutions. The outputs of simulations are multi-resolution spatial temporal ensembles. Each simulation run uses a unique set of values for multiple convective parameters. Distinct parameter settings from different simulation runs in different resolutions constitute a multi-resolution high-dimensional parameter space. Understanding the correlation between the different convective parameters, and establishing a connection between the parameter settings and the ensemble outputs are crucial to domain scientists. The multi-resolution high-dimensional parameter space, however, presents a unique challenge to the existing correlation visualization techniques. We present Nested Parallel Coordinates Plot (NPCP), a new type of parallel coordinates plots that enables visualization of intra-resolution and inter-resolution parameter correlations. With flexible user control, NPCP integrates superimposition, juxtaposition and explicit encodings in a single view for comparative data visualization and analysis. We develop an integrated visual analytics system to help domain scientists understand the connection between multi-resolution convective parameters and the large spatial temporal ensembles. Our system presents intricate climate ensembles with a comprehensive overview and on-demand geographic details. We demonstrate NPCP, along with the climate ensemble visualization system, based on real-world use-cases from our collaborators in computational and predictive science.


title: Vehicular Cloud data collection for Intelligent Transportation Systems

abstract: The Internet of Things (IoT) envisions to connect billions of sensors to the Internet, in order to provide new applications and services for smart cities. IoT will allow the evolution of the Internet of Vehicles (IoV) from existing Vehicular Ad hoc Networks (VANETs), in which the delivery of various services will be offered to drivers by integrating vehicles, sensors, and mobile devices into a global network. To serve VANET with computational resources, Vehicular Cloud Computing (VCC) is recently envisioned with the objective of providing traffic solutions to improve our daily driving. These solutions involve applications and services for the benefit of Intelligent Transportation Systems (ITS), which represent an important part of IoV. Data collection is an important aspect in ITS, which can effectively serve online travel systems with the aid of Vehicular Cloud (VC). In this paper, we involve the new paradigm of VCC to propose a data collection model for the benefit of ITS. We show via simulation results that the participation of low percentage of vehicles in a dynamic VC is sufficient to provide meaningful data collection.


title: Dynamic scene shape reconstruction using a single structured light pattern

abstract: 3D acquisition techniques to measure dynamic scenes and deformable objects with little texture are extensively researched for applications like the motion capturing of human facial expression. To allow such measurement, several techniques using structured light have been proposed. These techniques can be largely categorized into two types. The first involves techniques to temporally encode positional information of a projectorpsilas pixels using multiple projected patterns, and the second involves techniques to spatially encode positional information into areas or color spaces. Although the former allows dense reconstruction with a sufficient number of patterns, it has difficulty in scanning objects in rapid motion. The latter technique uses only a single pattern, so this problem can be resolved, however, it often uses complex patterns or color intensities, which are weak to noise, shape distortions, or textures. Thus, it remains an open problem to achieve dense and stable 3D acquisition in real cases. In this paper, we propose a technique to achieve dense shape reconstruction that requires only a single-frame image of a grid pattern. The proposed technique also has the advantage of being robust in terms of image processing.


title: on Executive Attention and Metacognitive Regulation

abstract: Metacognition refers to any knowledge or cognitive process that monitors or controls cognition. We highlight similarities between metacognitive and executive control functions, and ask how these processes might be implemented in the human brain. A review of brain imaging studies reveals a circuitry of attentional networks involved in these control processes, with its source located in midfrontal areas. These areas are active during conflict resolution, error correction, and emotional regulation. A developmental approach to the organization of the anatomy involved in executive control provides an added perspective on how these mechanisms are influenced by maturation and learning, and how they relate to metacognitive activity.


title: Associations between Abortion, Mental Disorders, and Suicidal Behaviour in a Nationally Representative Sample

abstract: Objective: Most previous studies that have investigated the relation between abortion and mental illness have presented mixed findings. We examined the relation between abortion, mental disorders, and suicidality using a US nationally representative sample. Methods: Data came from the National Comorbidity Survey Replication (n = 3310 women, aged 18 years and older). The World Health Organization–Composite International Diagnostic Interview was used to assess mental disorders based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria and lifetime abortion in women. Multiple logistic regression analyses were employed to examine associations between abortion and lifetime mood, anxiety, substance use, eating, and disruptive behaviour disorders, as well as suicidal ideation and suicide attempts. We calculated the percentage of respondents whose mental disorder came after the first abortion. The role of violence was also explored. Population attributable fractions were calculated for significant associations between abortion and mental disorders. Results: After adjusting for sociodemographics, abortion was associated with an increased likelihood of several mental disorders—mood disorders (adjusted odds ratio [AOR] ranging from 1.75 to 1.91), anxiety disorders (AOR ranging from 1.87 to 1.91), substance use disorders (AOR ranging from 3.14 to 4.99), as well as suicidal ideation and suicide attempts (AOR ranging from 1.97 to 2.18). Adjusting for violence weakened some of these associations. For all disorders examined, less than one-half of women reported that their mental disorder had begun after the first abortion. Population attributable fractions ranged from 5.8% (suicidal ideation) to 24.7% (drug abuse). Conclusions: Our study confirms a strong association between abortion and mental disorders. Possible mechanisms of this relation are discussed.


title: Employers’ perception of the costs and the benefits of hiring individuals with autism spectrum disorder in open employment in Australia

abstract: Research has examined the benefits and costs of employing adults with autism spectrum disorder (ASD) from the perspective of the employee, taxpayer and society, but few studies have considered the employer perspective. This study examines the benefits and costs of employing adults with ASD, from the perspective of employers. Fifty-nine employers employing adults with ASD in open employment were asked to complete an online survey comparing employees with and without ASD on the basis of job similarity. The findings suggest that employing an adult with ASD provides benefits to employers and their organisations without incurring additional costs.


title: A survey on real time bidding advertising

abstract: Real-time bidding (RTB) is an emerging and promising business model for online computational advertising in the age of big data. Based on analysis of massive amounts of Cookie data generated by Internet users, RTB advertising has the potential of identifying in real-time the characteristic and interest of the target audience in each ad impression, automatically delivering best-matched ads, and optimizing their prices via auction-based programmatic buying scheme. RTB has significantly changed online advertising, evolving from the traditional pattern of "media buying" and "ad-slot buying" to "targetaudience buying", and is expected to be the standard business model for online advertising in the future. In this paper, we discussed the current market practice of RTB advertising, presented the key roles and typical business processes in RTB markets, and summarized the current research progresses in the existing literature. The aim of this paper is to provide useful reference and guidance for future works.


title: ElasticFusion: dense SLAM without a pose graph

abstract: We present a novel approach to real-time dense visual SLAM. Our system is capable of capturing comprehensive dense globally consistent surfel-based maps of room scale environments explored using an RGB-D camera in an incremental online fashion, without pose graph optimisation or any postprocessing steps. This is accomplished by using dense frame-tomodel camera tracking and windowed surfel-based fusion coupled with frequent model refinement through non-rigid surface deformations. Our approach applies local model-to-model surface loop closure optimisations as often as possible to stay close to the mode of the map distribution, while utilising global loop closure to recover from arbitrary drift and maintain global consistency.


title: A 56-Gb/s receiver front-end with a CTLE and 1-tap DFE in 20-nm CMOS

abstract: A 56-Gb/s receiver front-end suited for baud-rate clock recovery is demonstrated in 20-nm CMOS. Sharing the comparators for the data decision and phase detection minimizes the number of comparators in the front-end and reduces the power consumption. The front-end has a continuous-time linear equalizer followed by a 1-tap speculative decision-feedback equalizer. The front-end operates at 56Gb/s with a bit error rate of less than 10 -12 with a 0.4UI margin in the bathtub curve. It occupies 0.27mm 2 and consumes 177mW of power from a 0.9-V supply.


title: Dual strip weaving: interactive design of quad layouts using elastica strips

abstract: We introduce Dual Strip Weaving, a novel concept for the interactive design of quad layouts, i.e. partitionings of freeform surfaces into quadrilateral patch networks. In contrast to established tools for the design of quad layouts or subdivision base meshes, which are often based on creating individual vertices, edges, and quads, our method takes a more global perspective, operating on a higher level of abstraction: the atomic operation of our method is the creation of an entire cyclic strip, delineating a large number of quad patches at once. The global consistency-preserving nature of this approach reduces demands on the user's expertise by requiring less advance planning. Efficiency is achieved using a novel method at the heart of our system, which automatically proposes geometrically and topologically suitable strips to the user. Based on this we provide interaction tools to influence the design process to any desired degree and visual guides to support the user in this task.


title: Microscopic Image Classification Using DCT for the Detection of Acute Lymphoblastic Leukemia (ALL)

abstract: Development of a computer-aided diagnosis (CAD) system for early detection of leukemia is very essential for the betterment of medical purpose. In recent years, a variety of CAD system has been proposed for the detection of leukemia. Acute leukemia is a malignant neoplastic disorder that influences a larger fraction of world population. In modern medical science, there are sufficient newly formulated methodologies for the early detection of leukemia. Such advanced technologies include medical image processing methods for the detection of the syndrome. This paper shows that use of a highly appropriate feature extraction technique is required for the classification of a disease. In the field of image processing and machine learning approach, Discrete Cosine Transform (DCT) is a well-known technique. Nucleus features are extracted from the RGB image. The proposed method provides an opportunity to fine-tune the accuracy for the detection of the disease. Experimental results using publicly available dataset like ALL-IDB shows the superiority of the proposed method with SVM classifier comparing it with some other standard classifiers.


title: Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian

abstract: Most visual recognition methods implicitly assume the data distribution remains unchanged from training to testing. However, in practice domain shift often exists, where real-world factors such as lighting and sensor type change between train and test, and classifiers do not generalise from source to target domains. It is impractical to train separate models for all possible situations because collecting and labelling the data is expensive. Domain adaptation algorithms aim to ameliorate domain shift, allowing a model trained on a source to perform well on a different target domain. However, even for the setting of unsupervised domain adaptation, where the target domain is unlabelled, collecting data for every possible target domain is still costly. In this paper, we propose a new domain adaptation method that has no need to access either data or labels of the target domain when it can be described by a parametrised vector and there exits several related source domains within the same parametric space. It greatly reduces the burden of data collection and annotation, and our experiments show some promising results.


Modelling of wind energy sources in real time platform


title: A soft, bistable valve for autonomous control of soft actuators

abstract: Almost all pneumatic and hydraulic actuators useful for mesoscale functions rely on hard valves for control. This article describes a soft, elastomeric valve that contains a bistable membrane, which acts as a mechanical “switch” to control air flow. A structural instability—often called “snap-through”—enables rapid transition between two stable states of the membrane. The snap-upward pressure, Δ P 1 (kilopascals), of the membrane differs from the snap-downward pressure, Δ P 2 (kilopascals). The values Δ P 1 and Δ P 2 can be designed by changing the geometry and the material of the membrane. The valve does not require power to remain in either “open” or “closed” states (although switching does require energy), can be designed to be bistable, and can remain in either state without further applied pressure. When integrated in a feedback pneumatic circuit, the valve functions as a pneumatic oscillator (between the pressures Δ P 1 and Δ P 2 ), generating periodic motion using air from a single source of constant pressure. The valve, as a component of pneumatic circuits, enables (i) a gripper to grasp a ball autonomously and (ii) autonomous earthworm-like locomotion using an air source of constant pressure. These valves are fabricated using straightforward molding and offer a way of integrating simple control and logic functions directly into soft actuators and robots.


title: Phase Plots of Complex Functions: a Journey in Illustration

abstract: T his work was inspired by the article “Mobius transformations revealed” by Douglas Arnold and Jonathan Rogness [3]. There the authors write: “Among the most insightful tools that mathematics has developed is the representation of a function of a real variable by its graph. ... The situation is quite different for a function of a complex variable. The graph is then a surface in four-dimensional space, andnot so easily drawn.Many texts in complex analysis are without a single depiction of a function. Nor is it unusual for average students to complete a course in the subject with little idea of what even simple functions, say trigonometric functions, ‘look like’.” In the printed literature there are a few laudable exceptions to this rule, such as the prize-winning Visual Complex Analysis by Tristan Needham [28], Steven Krantz’s textbook [21] with a chapter on computer packages for studying complex variables, and the Maple-based (German) introduction to complex function theory [15] by Wilhelm Forst and Dieter Hoffmann. But looking behind the curtain, one encounters a different situation that is evolving very quickly. Some of us have developed our own techniques for visualizing complex functions in teaching and research, and one can find many beautiful illustrations of complex functions on the Internet.


Who will pass? Analyzing learner behaviors in MOOCs


title: A novel stereovision algorithm for obstacles detection based on U-V-disparity approach

abstract: Reliable classification of the 3D driving environment is critical for obstacle detection applications. An efficient obstacle detection algorithm using stereovision, and based on U-V-disparity maps analysis is presented in this paper. Obstacles detection through U-V-disparity is based on the calculation of the disparity map generated from a stereo matching step. Our algorithm is based on novel horizontal and vertical obstacles alignment's extraction with road plane estimation. U-V-disparity enables to classify 3D road scenes into free regions and non transversal areas or simply obstacles. Validation results demonstrate the efficiency, and the robustness of the proposed algorithm in different environments (indoors and outdoors), weather conditions and light illuminations (day or night).


Profiled SCA with a New Twist: Semi-supervised Learning


title: Constructions of dynamic geometry: A study of the interpretative flexibility of educational software in classroom practice

abstract: The idea of ‘interpretative flexibility’ underpins new approaches to studying technological artefacts and curricular resources in use. This paper opens by reviewing – in this light – the evolving design of dynamic geometry, its pioneering use within classroom projects, and early sketches of its mainstream use in ordinary classrooms. After examining curricular context and its instrumental dimension, the paper then reports a study of teacher constructions of dynamic geometry in classroom practice, conducted in professionally well-regarded mathematics departments in English secondary schools. From departmental focus-group interviews, four teacher-nominated examples of successful practice were selected for study in depth through lesson observation and post-lesson interview. Iterative thematic analysis was employed, first to establish a narrative outline of each case, and then the ideas and issues salient across cases. The study illustrates the interpretative flexibility surrounding the emergent use of dynamic geometry. It found important differences in practical elaboration of the widespread idea of employing dynamic geometry to support guided discovery. The process of evaluating the costs and benefits of student software use was influenced by the extent to which such use was seen as providing experience of a mathematical reference model, and more fundamentally as promoting mathematically disciplined interaction. Approaches to handling apparent mathematical anomalies of software operation depended on whether these were seen as providing opportunities to develop students’ mathematical understanding, in line with a more fundamental pedagogical orientation towards supporting learning through analysis of mathematical discrepancies. Such variation was associated with differences in positioning dynamic geometry in relation to curricular norms and in privileging a mathematical register for framing figural properties. Across all cases, however, incorporating dynamic manipulation into mathematical discourse moved implicitly beyond established norms when dragging was used to focus attention on continuous dynamic variation, rather than being treated as an efficient means of generating multiple static figures. 2007 Elsevier Ltd. All rights reserved.


title: Accuracy analysis for TDOA localization in sensor networks

abstract: In this paper, passive emitter localization using Time Difference of Arrival (TDOA) measurements within a sensor network is investigated. Contrary to the case of a moving sensor pair the fact of correlated measurements in a single time step arises dependent on the choice of the measurement set. In tracking simulations, the Crame´r Rao Lower Bound (CRLB) and simulation results are often computed assuming constant standard deviations of the incoming measurements. Though, the measurement error is parameter dependent, it depends for example on the emitter-sensors-geometry. In Monte Carlo simulations, a comparison is performed between the localization in a static sensor network with three sensors and the localization using a moving sensor pair. Measurements are generated assuming additive white Gaussian noise with constant and parameter dependent variance. Results confirm the attainable localization accuracy and show the superior performance of an additional sensor compared to additional motion.


Learning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network


title: When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective

abstract: Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing. Inspired by social networks, one can establish trust among participatory agents to leverage the wisdom of crowds for mobile sensing. A prototype of social-network-inspired mobile multimedia and sensing application is presented for illustrative purposes. Numerical experiments on real-world datasets show improved performance of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect to Internet layers are discussed.


title: Situation-awareness for adaptive coordination in service-based systems

abstract: Service-based systems have many applications, including collaborative research and development, e-business, health care, environmental control, military applications, and homeland security. Service coordination is required for these systems to coordinate distributed activities. To achieve adaptive service coordination under changing environment and workload, situation-awareness is needed. In this paper, a model is presented for situation-awareness (SAW) requirements in service-based systems. Based on this model, SAW agents are developed to incorporate situation-awareness and adaptive coordination in service-based systems.


title: Platform ecosystems: how developers invert the firm

abstract: For a period starting in 2015, Apple, Google, and Microsoft became the most valuable companies in the world. Each was marked by an external developer ecosystem. Anecdotally, at least, developers matter. Using a formal model of code spillovers, we show how a rising number of developers can invert the firm. That is, firms will choose to innovate using open external contracts in preference to closed vertical integration. The locus of value creation moves from inside the firm to outside. Distinct from physical goods, digital goods afford firms the chance to optimize spillovers. Further, firms that pursue high risk innovations with more developers can be more profitable than firms that pursue low risk innovations with fewer developers. More developers give platform firms more chances at success. Our contribution is to show why developers might cause a shift in organizational form and to provide a theory of how platform firms optimize their own intellectual property regimes in order to maximize growth. We use stylized facts from multiple platform firms to illustrate our theory and results.


title: Quantum machine learning for electronic structure calculations

abstract: Considering recent advancements and successes in the development of efficient quantum algorithms for electronic structure calculations—alongside impressive results using machine learning techniques for computation—hybridizing quantum computing with machine learning for the intent of performing electronic structure calculations is a natural progression. Here we report a hybrid quantum algorithm employing a restricted Boltzmann machine to obtain accurate molecular potential energy surfaces. By exploiting a quantum algorithm to help optimize the underlying objective function, we obtained an efficient procedure for the calculation of the electronic ground state energy for a small molecule system. Our approach achieves high accuracy for the ground state energy for H2, LiH, H2O at a specific location on its potential energy surface with a finite basis set. With the future availability of larger-scale quantum computers, quantum machine learning techniques are set to become powerful tools to obtain accurate values for electronic structures. With the rapid development of quantum computers, quantum machine learning approaches are emerging as powerful tools to perform electronic structure calculations. Here, the authors develop a quantum machine learning algorithm, which demonstrates significant improvements in solving quantum many-body problems.


title: Moon slotted circular planar monopole UWB antenna design and analysis

abstract: Design and analysis of a moon slotted circular planar monopole UWB (ultra-wide band) antenna is discussed. The proposed antenna operates within a frequency spectrum of 2.62GHz to 12.63GHz, which includes the unlicensed band 3.1GHz-10.6GHz approved by FCC in 2002. It achieves a maximal peak gain of 4.85dBi at 9.75GHz and highest radiation efficiency of 95.94% at 3.1GHz. This antenna is mounted on a FR 4 substrate having dimensions 30mm×19mm×1.6mm. This paper initially reports a systematic way of conventional circular planar monopole UWB antenna design and then presents a step-by-step design process to obtain the proposed UWB antenna from the conventional one. In this process transmission line equivalent models for different design steps are also discussed. These models are basically derived from the conventional assumption of copper elements interms of R, L and C. Then after, it reports a comparison study of the two UWB antennas. Finally, the proposed UWB antenna geometry is fabricated and tested. Experimental result shows an agreement with the simulated result.


title: Hierarchical differential image filters for skin analysis

abstract: In this paper we present a framework for analyzing skin parameters from portrait images and videos. Using a series of Hierarchical Differential Image Filters (HDIF), it becomes possible to detect different skin features such as wrinkles, spots, and roughness. These detected features are used to compute skin ratings that are compared to actual ratings by dermatologists. Analyzing a database of 49 images with ratings by a panel of dermatologists, the proposed HDIF method is able to detect skin roughness, dark spots, and deep wrinkles with an average rating error of 11.3%, 17.6%, and 15.6%, respectively, as compared to individual dermatologist rating errors of 8.2%, 7.4%, and 6.5%. Although dermatologist ratings are more accurate than the proposed HDIF method, the ratings are close enough that the HDIF ratings can be a viable solution where dermatologist ratings are not readily available.


title: Product returns, asymmetric information, and firm performance

abstract: There has been a scarcity of research that studies the value of product return information to supply chain firms. In this research, we assume that the online retailer has the product return information but the manufacturer does not. Our results show that a two-part price contract can motivate the online retailer to share its private information only under certain condition, but the revenue sharing contract plus profit split mechanism always is a valuable strategy to be utilized to seek sharing the online retailer's private information and create a Pareto result. Particularly when the product is highly compatible with online sales, the manufacturer has a strong motivation to seek sharing the online retailer's information. A follow-up empirical study investigates what information can be used to predict product returns and is valuable to be shared with the manufacturer. Through sharing the product return information, both the manufacturer and the online retailer can achieve a higher performance.


title: CARL: Content-Aware Representation Learning for Heterogeneous Networks

abstract: Heterogeneous networks not only present a challenge of heterogeneity in the types of nodes and relations, but also the attributes and content associated with the nodes. While recent works have looked at representation learning on homogeneous and heterogeneous networks, there is no work that has collectively addressed the following challenges: (a) the heterogeneous structural information of the network consisting of multiple types of nodes and relations; (b) the unstructured semantic content (e.g., text) associated with nodes; and (c) online updates due to incoming new nodes in growing network. We address these challenges by developing a Content-Aware Representation Learning model (CARL). CARL performs joint optimization of heterogeneous SkipGram and deep semantic encoding for capturing both heterogeneous structural closeness and unstructured semantic relations among all nodes, as function of node content, that exist in the network. Furthermore, an additional online update module is proposed for efficiently learning representations of incoming nodes. Extensive experiments demonstrate that CARL outperforms state-of-the-art baselines in various heterogeneous network mining tasks, such as link prediction, document retrieval, node recommendation and relevance search. We also demonstrate the effectiveness of the CARL's online update module through a category visualization study.


title: Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing

abstract: We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for the processing of graph data. It founds on a constructive methodology to build a deep architecture comprising layers of probabilistic models that learn to encode the structured information in an incremental fashion. Context is diffused in an efficient and scalable way across the graph vertexes and edges. The resulting graph encoding is used in combination with discriminative models to address structure classification benchmarks.


title: Rare Earth Reduction Using a Novel Variable Magnetomotive Force Flux-Intensified IPM Machine

abstract: This paper presents a novel variable magnetomotive force, flux intensifying IPM (VFI-IPM) machine which uses only low coercive force magnets to reduce usage of rare earth metals. The VFI-IPM machine is designed with a characteristic of L d >L q , which is typical for FI-IPM machines, and uses low coercive force magnets with magnetization level control. A theoretical magnet operating point analysis was conducted to investigate the design methodology. A proof-of-principle prototype machine was then designed based on these principles. Simulation results show that the VFI-IPM prototype can achieve a wide torque-speed envelope and variable torque-speed characteristics, while not requiring high coercive force magnets that would include far more expensive rare earth materials, such as dysprosium.


title: Audio Event Detection using Weakly Labeled Data

abstract: Acoustic event detection is essential for content analysis and description of multimedia recordings. The majority of current literature on the topic learns the detectors through fully-supervised techniques employing strongly labeled data. However, the labels available for majority of multimedia data are generally weak and do not provide sufficient detail for such methods to be employed. In this paper we propose a framework for learning acoustic event detectors using only weakly labeled data. We first show that audio event detection using weak labels can be formulated as an Multiple Instance Learning problem. We then suggest two frameworks for solving multiple-instance learning, one based on support vector machines, and the other on neural networks. The proposed methods can help in removing the time consuming and expensive process of manually annotating data to facilitate fully supervised learning. Moreover, it can not only detect events in a recording but can also provide temporal locations of events in the recording. This helps in obtaining a complete description of the recording and is notable since temporal information was never known in the first place in weakly labeled data.


title: Single-Channel and Multi-Channel Sinusoidal Audio Coding Using Compressed Sensing

abstract: Compressed sensing (CS) samples signals at a much lower rate than the Nyquist rate if they are sparse in some basis. In this paper, the CS methodology is applied to sinusoidally modeled audio signals. As this model is sparse by definition in the frequency domain (being equal to the sum of a small number of sinusoids), we investigate whether CS can be used to encode audio signals at low bitrates. In contrast to encoding the sinusoidal parameters (amplitude, frequency, phase) as current state-of-the-art methods do, we propose encoding few randomly selected samples of the time-domain description of the sinusoidal component (per signal segment). The potential of applying compressed sensing both to single-channel and multi-channel audio coding is examined. The listening test results are encouraging, indicating that the proposed approach can achieve comparable performance to that of state-of-the-art methods. Given that CS can lead to novel coding systems where the sampling and compression operations are combined into one low-complexity step, the proposed methodology can be considered as an important step towards applying the CS framework to audio coding applications.


title: Attention-Based Memory Network for Text Sentiment Classification

abstract: In order to explore the impact of different memory modules under the framework of memory network for aspect level sentiment classification, we use convolutional neural networks (CNN) and bidirectional long short term memory (BiLSTM) to design four kinds of memory network models in this paper. The first model uses CNN for building a memory module, which is capable of capturing local information in documents. The second uses BiLSTM for building another memory module, which captures sequence information in documents. At the same time, the following two models use CNN and BiLSTM for building memory module——one builds a hierarchical neural network by using CNN and BiLSTM for building memory module, which combines both local and sequence information together. The other, respectively, uses CNN and BiLSTM for building two memory modules, respectively, which captures local information and sequence information through different modules. And then, we combine the final representations, which are generated from the different memory networks, for further sentiment classification. Experiments on laptop and restaurant datasets demonstrate that our four methods achieving better results than MemNet. In particular, the latter two models achieve better performance than the first two models and feature-based support vector machine approach.


title: 3D Convolutional Generative Adversarial Networks for Detecting Temporal Irregularities in Videos

abstract: In this work, we introduce a novel method for video temporal irregularity detection using the discriminative framework of 3D convolutional generative adversarial networks (3D-GANs). Temporal irregularities indicate unusual video segments. Detecting such irregularities is essential to video analysis applications like video anomaly detection and video summarization. To detect temporal irregularities in videos we need to address two problems: 1) temporal irregularities are difficult to define, different situations have different irregularities, and 2) irregularities are scarce in videos. Therefore, we formulate video temporal irregularity detection as fake data detection via the discriminative framework of a designed 3D-GAN. This new formulation only employs regular videos during the training phase and detects irregularities according to the deviation estimated by the discriminator of 3D-GAN. We take regular videos as real data and construct a 3D-GAN to learn the distribution of regular videos during the training phase. Since testing data contain irregular videos or fake data, whose distribution is different from regular videos or real data, the trained discriminator of our networks is able to detect temporal regularities and irregularities. Experiments show that 3D-GANs outperforms 2D-GANs in temporal irregularity detection, and demonstrate the effectiveness and competitive performance of our approach on anomaly detection datasets.


title: Virtual spatial registration of stand-alone fNIRS data to MNI space

abstract: Abstract The registration of functional brain data to common stereotaxic brain space facilitates data sharing and integration across different subjects, studies, and even imaging modalities. Thus, we previously described a method for the probabilistic registration of functional near-infrared spectroscopy (fNIRS) data onto Montreal Neurological Institute (MNI) coordinate space that can be used even when magnetic resonance images of the subjects are not available. This method, however, requires the careful measurement of scalp landmarks and fNIRS optode positions using a 3D-digitizer. Here we present a novel registration method, based on simulations in place of physical measurements for optode positioning. First, we constructed a holder deformation algorithm and examined its validity by comparing virtual and actual deformation of holders on spherical phantoms and real head surfaces. The discrepancies were negligible. Next, we registered virtual holders on synthetic heads and brains that represent size and shape variations among the population. The registered positions were normalized to MNI space. By repeating this process across synthetic heads and brains, we statistically estimated the most probable MNI coordinate values, and clarified errors, which were in the order of several millimeters across the scalp, associated with this estimation. In essence, the current method allowed the spatial registration of completely stand-alone fNIRS data onto MNI space without the use of supplementary measurements. This method will not only provide a practical solution to the spatial registration issues in fNIRS studies, but will also enhance cross-modal communications within the neuroimaging community.


title: A Path Planning and Navigation Control System Design for Driverless Electric Bus

abstract: Extensive studies have been conducted on unmanned vehicle recently due to its bright prospect. However, few of them have studied the technology of driverless bus. In this paper, a path planning and navigation control system is designed for 12-m length driverless electric bus. The global path planning is adopted by the Dijkstra algorithm based on the analysis tool of ArcGIS. For local planning, a triple-layer finite-state machine is proposed to plan the driving maneuver, and it reduces computational complexity and improves driving effectiveness. Then, optimal trajectory is generated by a curve fitting technique based on path planning and driving maneuver, which takes full account of the safety and kinetics for driverless bus. In addition, we address the speed planning by expert rules to imitate an experienced driver. Finally, the trajectory tracking controller is devised by an adaptive rolling window. Moreover, a heading error compensation model and a self-adaptive strategy of controller parameters are set up to improve the control precision. Finally, simulation by TruckSim and scenes experiments verify the validity and practicability of our proposed path planning and navigation control system for driverless bus.


title: RDF versus attributed graphs: The war for the best graph representation

abstract: In this work, methods are developed to overcome the inherent problems of network abstraction and analysis from multiple heterogeneous data sources. RDF and attributed graphs are two common choices for graph modeling. While both are very similar with respect to the type of information that can be represented, characteristics intrinsic to each representation affect the analysis performed over the resultant network abstractions. By selecting a dual graph representation approach and leveraging the strengths of both models, the semantic analysis performed over RDF graphs is combined with the topological analysis applied to attributed graphs, to produce a comprehensive foundation for network analysis that cannot be easily achieved, nor its value matched, by one representation independently.


title: Traffic Priority and Load Adaptive MAC Protocol for QoS Provisioning in Body Sensor Networks

abstract: Body sensor networks (BSNs) carry heterogeneous traffic types having diverse QoS requirements, such as delay, reliability and throughput. In this paper, we design a priority-based traffic load adaptive medium access control (MAC) protocol for BSNs, namely, PLA-MAC, which addresses the aforementioned requirements and maintains efficiency in power consumption. In PLA-MAC, we classify sensed data packets according to their QoS requirements and accordingly calculate their priorities. The transmission schedules of the packets are determined based on their priorities. Also, the superframe structure of the proposed protocol varies depending on the amount of traffic load and thereby ensures minimal power consumption. Our performance evaluation shows that the PLA-MAC achieves significant improvements over the state-of-the-art protocols.


title: Larger-Context Language Modelling

abstract: In this work, we propose a novel method to incorporate corpus-level discourse information into language modelling. We call this larger-context language model. We introduce a late fusion approach to a recurrent language model based on long shortterm memory units (LSTM), which helps the LSTM unit keep intra-sentence dependencies and inter-sentence dependencies separate from each other. Through the evaluation on four corpora (IMDB, BBC, Penn TreeBank, and Fil9), we demonstrate that the proposed model improves perplexity significantly. In the experiments, we evaluate the proposed approach while varying the number of context sentences and observe that the proposed late fusion is superior to the usual way of incorporating additional inputs to the LSTM. By analyzing the trained larger-context language model, we discover that content words, including nouns, adjectives and verbs, benefit most from an increasing number of context sentences. This analysis suggests that larger-context language model improves the unconditional language model by capturing the theme of a document better and more easily.


title: Mitigating denial of service attack using CAPTCHA mechanism

abstract: Denial of Service (DoS henceforth) attack is performed solely with the intention to deny the legitimate users to access services. Since DoS attack is usually performed by means of bots, automated software. These bots send a large number of fake requests to the server which exceeds server buffer capacity which results in DoS attack. In this paper we propose an idea to prevent DoS attack on web-sites which ask for user credentials before it allows them to access resources. Our approach is based on CAPTCHA verification. We verify CAPTCHA submitted by user before allowing the access to credentials page. The CAPTCHA would consist of variety of patterns that would be distinct in nature and are randomly generated during each visit to the webpage. Most of the current web sites use a common methodology to generate all its CAPTCHAs. The bots usually take advantage of this approach since bots are able to decipher those CAPTCHAs. A set of distinct CAPTCHA patterns prevents bots to decipher it and consequently helps to reduce the generation of illicit traffic. This preserves the server bandwidth to allow the legitimate users to access the site.


title: Proposal of pneumatic rubber muscles with Shape-Memory Polymer reinforcement fibers realizing desirable motion

abstract: Pneumatic rubber actuators have advantages, light weigh, low cost, high safety and so on. Most of them have reinforcement fibers in their rubber body to realize anisotropic deformation for desirable motion when pneumatic pressure is applied. Once the actuator is fabricated, its motion type, bending, contracting, or extending, is determined uniquely. In this study, Shape Memory Polymer (SMP) fibers are utilized as reinforcement fibers for pneumatic rubber actuators. Owing to function of SMP, shape of the actuators can be varied, then the motion of the actuator can be changed easily. In this report, we focuse on two kinds of pneumatic rubber artificial muscles, one is a straight fiber type muscle and the other is a McKibben type muscle, they generate linear motion in axial direction. SMP fibers are embedded in these artificial muscles as reinforcement fibers, then by changing its shape by SMP, these realize not only simple linear motion but also torsional motion or extensional-flexional motion.


title: A Novel Dual-LLC Resonant Soft Switching Converter for Super High Frequency Induction Heating Power Supplies

abstract: A novel dual-LLC resonant tanks zero voltage softswitching converter for super high-frequency solid-state induction heating power supplies is proposed in this paper. The proposed converter is capable of absorbing the inductive and capacitive parasitical components in the circuit. Switch devices operate in zero-voltage soft switching mode. The problems of high voltage and current spikes caused by oscillation of parasitic inductors or capacitors, and huge switching loss in super high-frequency operation, which are troublesome in super high-frequency circuits, can be solved. Moreover, a large inductor is inset in the input, this novel converter shares the benefits of both voltage-type and current-type inverters simultaneously, and the dead-time between two switches does not need, therefore, this converter is suit to operate in super high frequency conditions. The working principle of the converter is introduced, simulation and experimental results operating around 1 MHz frequency are given.


title: Demographic Classification with Local Binary Patterns

abstract: LBP (Local Binary Pattern) as an image operator is used to extract LBPH (LBP histogram) features for texture description. In this paper, we present a novel method to use LBPH feature in ordinary binary classification problem. Given a restricted local patch, the Chi square distance between the extracted LBPH and a reference histogram is used as a measure of confidence belonging to the reference class, and an optimal reference histogram is obtained by iteratively optimization; real AdaBoost algorithm is used to learn a sequence of best local features iteratively and combine them into a strong classifier. The experiments on age, gender and ethnicity classification demonstrate its effectiveness.


title: Phase Coherent Digital Communications for Wireless Optical Links in Turbid Underwater Environments

abstract: Previous studies by theauthorshaveincluded a theoretical and experimental investigation of the spatial distribution ofan optical signal usedforcommunications in underwater scattering environments. Presented hereisan experimental studyofhow scattering affects thetemporally encodedinformation bearing component oftheoptical signal. Shortrangeunderwater optical links employing BPSK,QPSK,8- PSK,16-QAM,and32-QAMmodulation areimplemented ina laboratory setting, yielding datarates upto5Mb/s.Theeffect of linkquality isexamined versus waterturbidity.


title: Combining Memory-Based and Model-Based Collaborative Filtering in Recommender System

abstract: Collaborative filtering (CF) technique has been proved to be one of the most successful techniques in recommender systems. Two types of algorithms for collaborative filtering have been researched: memory-based CF and model-based CF. Memory-based approaches identify the similarity between two users by comparing their ratings on a set of items and have suffered from two fundamental problems: sparsity and scalability. Alternatively, the model-based approaches have been proposed to alleviate these problems, but these approaches tend to limit the range of users. This paper presents an approach that combines the advantages of these two kinds of approaches by joining the two methods. Firstly, it employs memory-based CF to fill the vacant ratings of the user-item matrix. Then, it uses the item-based CF as model-based to form the nearest neighbors of every item. At last, it produces prediction of the target user to the target item at real time. The collaborative filtering recommendation method combining memory-based CF and model-based CF can provide better recommendation than traditional collaborative filtering.


title: Maximizing the total weight value of just-in-time jobs in identical parallel machines with periodic time slots

abstract: We address the processing of jobs in an environment with periodic due dates. Every job must be completed exactly on a due date, the situation of which shall be referred to as just-in-time. Each job is associated with a weight which is non-increasing with time. The problem we address is to maximize the total weight of just-in-time jobs. We prove that this class of problem is NP-hard. The key idea is a reduction from the Hamiltonian path problem, known as strongly NP-hard. Moreover, we discuss some special cases where the problem is solvable. If no set-up times exist, there are cases where the problem is solvable, we then present a method to solve the problems. To achieve this, we derive partition and union procedures, and use network flow algorithms.


title: INTERSPEECH 2010 A spectral LF model based approach to voice source parameterisation

abstract: This paper presents a new method of extracting LF model based parameters using a spectral model matching approach. Strategies are described for overcoming some of the known difficulties of this type of approach, in particular high frequency noise. The new method performed well compared to a typical time based method particularly in terms of robustness against distortions introduced by the recording system and in terms of the ability of parameters extracted in this manner to differentiate three discrete voice qualities. Results from this study are very promising for the new method and offer a way of extracting a set of non-redundant spectral parameters that may be very useful in both recognition and synthesis systems.


title: Remote Sensing Image Registration Using Convolutional Neural Network Features

abstract: Successful remote sensing image registration is an important step for many remote sensing applications. The scale-invariant feature transform (SIFT) is a well-known method for remote sensing image registration, with many variants of SIFT proposed. However, it only uses local low-level information, and loses much middle- or high-level information to register. Image features extracted by a convolutional neural network (CNN) have achieved the state-of-the-art performance for image classification and retrieval problems, and can provide much middle- and high-level information for remote sensing image registration. Hence, in this letter, we investigate how to calculate the CNN feature, and study the way to fuse SIFT and CNN features for remote sensing image registration. The experimental results demonstrate that the proposed method yields a better registration performance in terms of both the aligning accuracy and the number of correct correspondences.


title: AMNESIA: analysis and monitoring for NEutralizing SQL-injection attacks

abstract: The use of web applications has become increasingly popular in our routine activities, such as reading the news, paying bills, and shopping on-line. As the availability of these services grows, we are witnessing an increase in the number and sophistication of attacks that target them. In particular, SQL injection, a class of code-injection attacks in which specially crafted input strings result in illegal queries to a database, has become one of the most serious threats to web applications. In this paper we present and evaluate a new technique for detecting and preventing SQL injection attacks. Our technique uses a model-based approach to detect illegal queries before they are executed on the database. In its static part, the technique uses program analysis to automatically build a model of the legitimate queries that could be generated by the application. In its dynamic part, the technique uses runtime monitoring to inspect the dynamically-generated queries and check them against the statically-built model. We developed a tool, AMNESIA , that implements our technique and used the tool to evaluate the technique on seven web applications. In the evaluation we targeted the subject applications with a large number of both legitimate and malicious inputs and measured how many attacks our technique detected and prevented. The results of the study show that our technique was able to stop all of the attempted attacks without generating any false positives.


title: Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers

abstract: Type-2 fuzzy sets, which are characterized by membership functions (MFs) that are themselves fuzzy, have been attracting interest. This paper focuses on advancing the understanding of interval type-2 fuzzy logic controllers (FLCs). First, a type-2 FLC is evolved using Genetic Algorithms (GAs). The type-2 FLC is then compared with another three GA evolved type-1 FLCs that have different design parameters. The objective is to examine the amount by which the extra degrees of freedom provided by antecedent type-2 fuzzy sets is able to improve the control performance. Experimental results show that better control can be achieved using a type-2 FLC with fewer fuzzy sets/rules so one benefit of type-2 FLC is a lower trade-off between modeling accuracy and interpretability.


title: Modularisation of domain ontologies implemented in description logics and related formalisms including OWL

abstract: Modularity is a key requirement for large ontologies in order to achieve re-use, maintainability, and evolution. Mechanisms for 'normalisation' to achieve analogous aims are standard for databases. However, no similar notion of normalisation has yet emerged for ontologies. This paper proposes initial criteria for a two-step normalisation of ontologies implemented using OWL or related DL based formalisms. For the first - "ontological normalisation" - we accept Welty and Guarino's analysis. For the second - "implementation normalisation" - we propose an approach based on decomposing ("untangling") the ontology into independent disjoint skeleton taxonomies restricted to be simple trees, which can then be recombined using definitions and axioms to represent the relationships between them explicitly.


title: Simulation of linear brushless dc motor speed-controlled system based on MATLAB/SIMULINK

abstract: This paper introduces a simulation method using MATLAB/SIMULINK to analyse speed-controlled system of linear permanent magnet brushless DC motor (LPMBDCM). The speed, thrust curves are presented for the starting procedure and sudden load of the LPMBDCM. The results of simulation can be available on the design of practical speed-controlled system.


title: Y-STR haplotypes in populations from the Eastern Mediterranean region of Turkey

abstract: We present the frequency distributions of Y-haplotypes determined by ten Y-chromosomal STR polymorphisms (i.e., DXYS156-Y, DYS19, DYS385, DYS389 I and II, DYS390, DYS391, DYS392, DYS393) in unrelated males from the Eastern Mediterranean region of Turkey (104 Turkish and Arabian-speaking Eti Turks from Adana area, 111 Roma and 110 Turks, Kahramanmara degrees area). The haplotype diversity was 0.974 in the Eti Turks, 0.979 in the Roma, and 0.989 in the Turks. Two variant alleles were characterized by sequencing, i.e., allele 9.2 at DXYS156 and 12.2 at DYS385. Some of the haplotypes are particular to one of the three populations; some are shared by all three populations. A search against the Y-Haplotype Reference Database revealed several matches to samples not only from Turkey and neighboring regions (e.g., Syria, Iraq) but also from all over Europe.


title: Integrated stacked patch antenna array on LTCC material operating at 24 GHz [automotive radar applications]

abstract: This paper presents the concept, design and measurement results of a broadband stacked patch antenna array integrated within an FM-CW radar module, realised in LTCC (low temperature cofired ceramics) technology, for automotive applications operating at 24 GHz. After a general introduction on the benefits of integrated LTCC antennas, the paper describes the antenna requirements and the consequential design concept. Subsequently, simulation and measurement results are presented to verify the feasibility of the concept.