title: Mechanisms of Antibiotic Resistance

abstract: Emergence of resistance among the most important bacterial pathogens is recognized as a major public health threat affecting humans worldwide. Multidrug-resistant organisms have not only emerged in the hospital environment but are now often identified in community settings, suggesting that reservoirs of antibiotic-resistant bacteria are present outside the hospital. The bacterial response to the antibiotic "attack" is the prime example of bacterial adaptation and the pinnacle of evolution. "Survival of the fittest" is a consequence of an immense genetic plasticity of bacterial pathogens that trigger specific responses that result in mutational adaptations, acquisition of genetic material, or alteration of gene expression producing resistance to virtually all antibiotics currently available in clinical practice. Therefore, understanding the biochemical and genetic basis of resistance is of paramount importance to design strategies to curtail the emergence and spread of resistance and to devise innovative therapeutic approaches against multidrug-resistant organisms. In this chapter, we will describe in detail the major mechanisms of antibiotic resistance encountered in clinical practice, providing specific examples in relevant bacterial pathogens.


title: Why people use social networking sites: An empirical study integrating network externalities and motivation theory

abstract: Fast-developing social networking sites (SNS) have become the major media by which people develop their personal network online in recent years. To explore factors affecting user's joining SNS, this study applies network externalities and motivation theory to explain why people continue to join SNS. This study used an online questionnaire to conduct empirical research, and collected and analyzed data of 402 samples by structural equation modeling (SEM) approach. The findings show that enjoyment is the most influential factor in people's continued use of SNS, followed by number of peers, and usefulness. The number of peers and perceived complementarity have stronger influence than the number of members on perceived benefits (usefulness and enjoyment). This work also ran clustering analysis by gender, which found notable difference in both number of peers and number of members between men and women. The number of peers is an important factor affecting the continued intention to use for women but not for men; the number of members has no significant effect on enjoyment for men. The findings suggest that gender difference also produces different influences. The implication of research and discussions provides reference for SNS operators in marketing and operation.


title: Game theory based distributed energy trading for microgrids parks

abstract: To realize the energy sharing among multiple microgrids (MGs) within MG parks, a non-cooperative game based distributed trading is proposed. A linear supply and demand market clearing mechanism is proposed for energy prosumers. A day-ahead energy trading problem is proposed for each MG, correlated with other MGs' trading plans in the utility function. A non-cooperative game is formulated for the energy trading competition among MGs. The existence and uniqueness of Nash equilibrium (NE) corresponding to the proposed game is verified from the perspective of potential games. Based on best response functions, a globally converged distributed algorithm is adopted to compute the NE. The simulation is carried out on the MG park with three MGs. Results have verified the proposed method can obtain an efficient NE where MGs act as prosumers.


title: Intentional rounding: facilitators, benefits and barriers.

abstract: AIMS AND OBJECTIVES ::: To describe the implementation, practice and sustainability of Intentional Rounding (IR) within two diverse settings (aged care and maternity). ::: ::: ::: BACKGROUND ::: The profile of patients in hospitals has changed over time, generally being more severe, placing heavy demands on nurses' time. Routine non-urgent care is often provided only when there is time. IR has been found to increase both patient and staff satisfaction, also resulting in improved patient outcomes such as reduced falls and call bell use. IR is also used as a time management tool for safe and reliable provision of routine care. ::: ::: ::: METHODS ::: This descriptive qualitative research study comprised of three focus groups in a metropolitan hospital. ::: ::: ::: RESULTS ::: Fifteen nurses participated in three focus groups. Seven main themes emerged from the thematic analysis of the verbatim transcripts: implementation and maintenance, how IR works, roles and responsibilities, context and environment, benefits, barriers and legal issues. ::: ::: ::: CONCLUSION ::: IR was quickly incorporated into normal practice, with clinicians being able to describe the main concepts and practices. IR was seen as a management tool, facilitating accountability and continuity of management support being essential for sustainability. Clinicians reported increases in patient and staff satisfaction, and the opportunity to provide patient education. While patient type and acuity, ward layout and staff experience affected the practice of IR, the principles of IR are robust enough to allow for differences in the ward specialty and patient type. However, care must be taken when implementing IR to reduce the risk of alienating experienced staff. Incorporation of IR charts into the patient health care record is recommended. ::: ::: ::: RELEVANCE TO CLINICAL PRACTICE ::: Engaging all staff, encouraging ownership and stability of management are key factors in the successful implementation and maintenance of IR. IR is flexible and robust enough to accommodate different patient types and acuity.


title: Low-Rank Kernel Learning with Bregman Matrix Divergences

abstract: In this paper, we study low-rank matrix nearness problems, with a focus on learning low-rank positive semidefinite (kernel) matrices for machine learning applications. We propose efficient algorithms that scale linearly in the number of data points and quadratically in the rank of the input matrix. Existing algorithms for learning kernel matrices often scale poorly, with running times that are cubic in the number of data points. We employ Bregman matrix divergences as the measures of nearness---these divergences are natural for learning low-rank kernels since they preserve rank as well as positive semidefiniteness. Special cases of our framework yield faster algorithms for various existing learning problems, and experimental results demonstrate that our algorithms can effectively learn both low-rank and full-rank kernel matrices.


title: Taxonomy and Evolution of the Cotton Genus, Gossypium

abstract: We present an overview of the taxonomy of Gossypium L. (the cotton genus) and its evolutionary history. Gossypium contains more than 50 recognized species, including several recently described, distributed in arid to semiarid regions of the tropics and subtropics. Diversity in Gossypium has been promoted by two seemingly unlikely processes: transoceanic, long-distance dispersal and wide hybridization among lineages that presently are widely separated geographically. Included are four species that were independently domesticated for their seed fiber—two diploids from Africa–Asia and two allopolyploids from the Americas. This repeated domestication of different wild progenitors represents a remarkable case of human-driven parallel evolution. Morphological variation in Gossypium is extensive; growth forms in the genus range from sprawling herbaceous perennials to ?15-m-tall trees, representing a notable array of reproductive and vegetative characteristics. Equally impressive is the striking cytogenetic and genomic diversity that emerged as Gossypium diversified and spread worldwide, ultimately spawning eight groups of closely related diploid (n = 13) species (i.e., genome groups A through G, and K). DNA sequence data place the origin of Gossypium at about 5 to 10 million years ago (mya), which rapidly diversified into these major genome groups shortly thereafter. Allopolyploid cottons appeared within the last 1 to 2 million years, a consequence of the improbable transoceanic dispersal of an A genome taxon to the New World and subsequent hybridization with an indigenous D genome diploid. Diversification of the nascent allopolyploid gave rise to three modern lineages containing seven species, including the agronomically important G. hirsutum L. and G. barbadense L.


Fake News Detection using Stacked Ensemble of Classifiers


title: QoE management in DASH systems using the segment aware rate adaptation algorithm

abstract: Dynamic Adaptive Streaming over HTTP (DASH) enables the video player to adapt the bitrate of the video while streaming to ensure playback without interruptions even with varying throughput. A DASH server hosts multiple representations of the same video, each of which is broken down into small segments of fixed playback duration. The video bitrate adaptation is purely driven by the player at the endhost. Typically, the player employs an Adaptive Bitrate (ABR) algorithm, that determines the most appropriate representation for the next segment to be downloaded, based on the current network conditions and user preferences. The aim of an ABR algorithm is to dynamically manage the Quality of Experience (QoE) of the user during the playback. ABR algorithms manage the QoE by maximizing the bitrate while at the same time trying to minimize the other QoE metrics: playback start time, duration and number of buffering events, and the number of bitrate switching events. Typically, the ABR algorithms manage the QoE by using the measured network throughput and buffer occupancy to adapt the playback bitrate. However, due to the video encoding schemes employed, the sizes of the individual segments may vary significantly. For low bandwidth networks, fluctuation in the segment sizes results in inaccurate estimation the expected segment fetch times, thereby resulting in inaccurate estimation of the optimum bitrate. In this paper we demonstrate how the Segment-Aware Rate Adaptation (SARA) algorithm, that considers the measured throughput, buffer occupancy, and the variation in segment sizes helps in better management of the users' QoE in a DASH system. By comparing with a typical throughput-based and buffer-based adaptation algorithm under varying network conditions, we demonstrate that SARA manages the QoE better, especially in a low bandwidth network. We also developed AStream, an open-source Python-based emulated DASH-video player that was used to evaluate three different ABR algorithms and measure the QoE metrics with each of them.


title: New Maximum Power Point Tracker Using Sliding-Mode Observer for Estimation of Solar Array Current in the Grid-Connected Photovoltaic System

abstract: A new maximum power point tracker (MPPT) for a grid-connected photovoltaic system without solar array current sensor is proposed. The solar array current information is obtained from the sliding-mode observer and fed into the MPPT to generate the reference voltage. The parameter values such as capacitances can be changed up to 50% from their nominal values, and the linear observer cannot estimate the correct state values under the parameter variations and noisy environments. The structure of a sliding-mode observer is simple, but it shows the robust tracking property against modeling uncertainties and parameter variations. In this paper, the sliding-mode observer for the solar array current has been proposed to compensate for the parameter variations. The mathematical modeling and the experimental results verify the validity of the proposed method


title: A honeypot system with honeyword-driven fake interactive sessions

abstract: Honeypots are an indispensable tool for network and system security as well as for computer forensic investigations. They can be helpful for detecting possible intrusions, as well as for gathering information about their source, attack patterns, final target and purpose. Highly interactive honeypots, are probably the most useful and enlightening ones, since they reveal many information about intruders' behavior and skills, even though the implementation and setup of such tools might require considerable efforts and computational resources. Accordingly we present an architecture for highly interactive honeypots aiming at detecting password-cracking attacks by means of honeywords and leveraging container-based virtualization to provide persistent sessions needed to capture attacker activities.


Comparison of Automatic Keyphrase Extraction Systems in Scientific Papers


title: A Novel Calibration Method of Magnetic Compass Based on Ellipsoid Fitting

abstract: Magnetic compass is widely used to indicate the heading of vehicle by measuring the Earth's magnetic field. However, it suffers from local magnetic interferences; thus, the calibration of the magnetic compass is very essential before it is used. The traditional calibration methods require reference information and special requirements such as keeping the magnetic compass level during calibration, which is very difficult to manage outdoor. This paper presents an efficient method for calibrating the magnetic compass without the aforementioned traditional requirements. This method is based on the fact that the error model of magnetic compass is an ellipsoid, and a constraint least-square method is adopted to estimate the parameters of an ellipsoid by rotating the magnetic compass in various (random) orientations. This method can estimate all the parameters of the error model and compensate errors caused by sensor defects, hard-iron interferences, and soft-iron interferences. Although the calibration parameters are relative values, it does not have any influence on the heading calculated. The experimental results show that this method is effective in calibrating the magnetic compass, and the heading precision of the magnetic compass acquired after calibration is better than 0.4°.


title: Microservices as an Evolutionary Architecture of Component-Based Development: A Think-aloud Study

abstract: Microservices become a fast growing and popular architectural style based on service-oriented development. One of the major advantages using component-based approaches is to support reuse. In this paper, we present a study of microservices and how these systems are related to the traditional abstract models of component-based systems. This research focuses on the core properties of microservices including their scalability, availability and resilience, consistency, coupling and cohesion, and data storage capability, while highlighting their limitations and challenges in relation to components. To support our study, we investigated the existing literature and provided potential directions and interesting points in this growing field of research. As a result, using microservices as components is promising and would be a good mechanism for building applications that were used to be built with component-based approaches.


Spoken dialogue system for restaurant recommendation and reservation.


title: Entity Linking for Queries by Searching Wikipedia Sentences

abstract: We present a simple yet effective approach for linking entities in queries. The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. Then, we employ a rich set of features, such as link-probability, context-matching, word embeddings, and relatedness among candidate entities as well as their related entities, to rank the candidates under a regression based framework. The advantages of our approach lie in two aspects, which contribute to the ranking process and final linking result. First, it can greatly reduce the number of candidate entities by filtering out irrelevant entities with the words in the query. Second, we can obtain the query sensitive prior probability in addition to the static link-probability derived from all Wikipedia articles. We conduct experiments on two benchmark datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ dataset. Experimental results show that our method outperforms state-of-the-art systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ dataset.


title: Staff from the National Center for Biotechnology Information in the US describe recent improvements to the PubMed search engine and outline plans for the future , including a new experimental site called PubMed Labs

abstract: Staff from the National Center for Biotechnology Information in the US describe recent improvements to the PubMed search engine and outline plans for the future, including a new experimental site called PubMed Labs.


title: TurboQuad: A Novel Leg–Wheel Transformable Robot With Smooth and Fast Behavioral Transitions

abstract: This report is on the design, control strategy, implementation, and performance evaluation of a novel leg–wheel transformable robot called TurboQuad, which can perform fast gait/mode coordination and transitions in wheeled mode, in legged trotting, and in legged walking while in motion. This functionality was achieved by including two novel setups in the robot that were not included in its predecessor, Quattroped. First, a new leg–wheel mechanism was used, in which the leg/wheel operation and its in situ transition can be driven by the same set of motors, so the actuation system and power can be utilized efficiently. Second, a bio-inspired control strategy was applied based on the central pattern generator and coupled oscillator networks, in which the gait/mode generation, coordination, and transitions can be integrally controlled. The robot was empirically built and its performances in the described three gaits/modes as well as the transitions among them were experimentally evaluated and will be discussed in this paper.


title: Accessing Structure, Dynamics and Function of Biological Macromolecules by NMR Through Advances in Isotope Labeling

abstract: NMR spectroscopy has become an indispensable tool for high-resolution structure determination of biomolecules at physiological conditions both in solutions and solids. Currently, NMR is routinely used to study the structure and dynamics of high molecular weight biomolecules in sizes ranging up to ~ 50–100 kDa and to evaluate complexes as large as 500–1 MDa. The latest advances in spectrometer technology, methodologies and advents in newer and highly innovative NMR active isotope-labeling strategies now enable us to overcome an earlier speculated size barrier of ~ 20 kDa for de novo structure determination. Of these, developments in NMR active isotope-labeling strategies are of great significance as they allow reduction in spectral crowding and yield selective spin correlations. Moreover, NMR isotope enrichment schemes permit exploitation of heteronuclear magnetization transfer pathways for enhanced sensitivity and selectivity. Functionally relevant sites or domains in very large complexes can also be selectively evaluated by specific labeling strategies in which other regions are masked. Further, labeling schemes can be effectively used to favourably overcome deleterious relaxation effects. Recently evolved labeling strategies include uniform labeling, perdeuteration, specific labeling of an amino acid or a side chain, selective deuteration or protonation, segmental labeling and biosynthesis of biomolecules in various organisms, cell lines and cell-free systems. The present review is aimed at introducing various NMR isotope labeling strategies and discusses their impact in widening the scope of biomolecular NMR spectroscopy driven structural biology.


title: Retinal Fundus Image Registration via Vascular Structure Graph Matching

abstract: Motivated by the observation that a retinal fundus image may contain some unique geometric structures within its vascular trees which can be utilized for feature matching, in this paper, we proposed a graph-based registration framework called GM-ICP to align pairwise retinal images. First, the retinal vessels are automatically detected and represented as vascular structure graphs. A graph matching is then performed to find global correspondences between vascular bifurcations. Finally, a revised ICP algorithm incorporating with quadratic transformation model is used at fine level to register vessel shape models. In order to eliminate the incorrect matches from global correspondence set obtained via graph matching, we proposed a structure-based sample consensus (STRUCT-SAC) algorithm. The advantages of our approach are threefold: (1) global optimumsolution can be achieved with graph matching; (2) ourmethod is invariant to linear geometric transformations; and (3) heavy local feature descriptors are not required. The effectiveness of our method is demonstrated by the experiments with 48 pairs retinal images collected from clinical patients.


title: β-Catenin and NF-κB co-activation triggered by TLR3 stimulation facilitates stem cell-like phenotypes in breast cancer

abstract: Cancer stem cells (CSCs) are responsible for tumor initiation and progression. Toll-like receptors (TLRs) are highly expressed in cancer cells and associated with poor prognosis. However, a linkage between CSCs and TLRs is unclear, and potential intervention strategies to prevent TLR stimulation-induced CSC formation and underlying mechanisms are lacking. Here, we demonstrate that stimulation of toll-like receptor 3 (TLR3) promotes breast cancer cells toward a CSC phenotype in vitro and in vivo. Importantly, conventional NF-κB signaling pathway is not exclusively responsible for TLR3 activation-enriched CSCs. Intriguingly, simultaneous activation of both β-catenin and NF-κB signaling pathways, but neither alone, is required for the enhanced CSC phenotypes. We have further identified a small molecule cardamonin that can concurrently inhibit β-catenin and NF-κB signals. Cardamonin is capable of effectively abolishing TLR3 activation-enhanced CSC phenotypes in vitro and successfully controlling TLR3 stimulation-induced tumor growth in human breast cancer xenografts. These findings may provide a foundation for developing new strategies to prevent the induction of CSCs during cancer therapies.


title: Whispered Speech Recognition Using Deep Denoising Autoencoder and Inverse Filtering

abstract: Due to the profound differences between acoustic characteristics of neutral and whispered speech, the performance of traditional automatic speech recognition (ASR) systems trained on neutral speech degrades significantly when whisper is applied. In order to deeply analyze this mismatched train/test situation and to develop an efficient way for whisper recognition, this study first analyzes acoustic characteristics of whispered speech, addresses the problems of whispered speech recognition in mismatched conditions, and then proposes a new robust cepstral features and preprocessing approach based on deep denoising autoencoder (DDAE) that enhance whisper recognition. The experimental results confirm that Teager-energy-based cepstral features, especially TECCs, are more robust and better whisper descriptors than traditional Mel-frequency cepstral coefficients (MFCC). Further detailed analysis of cepstral distances, distributions of cepstral coefficients, confusion matrices, and experiments with inverse filtering, prove that voicing in speech stimuli is the main cause of word misclassification in mismatched train/test scenarios. The new framework based on DDAE and TECC feature, significantly improves whisper recognition accuracy and outperforms traditional MFCC and GMM-HMM (Gaussian mixture density—Hidden Markov model) baseline, resulting in an absolute 31% improvement of whisper recognition accuracy. The achieved word recognition rate in neutral/whisper scenario is 92.81%.


title: S Web-Marketing Mix model

abstract: This paper reviews the criticism on the 4Ps Marketing Mix framework, the most popular tool of traditional marketing management, and categorizes the main objections of using the model as the foundation of physical marketing. It argues that applying the traditional approach, based on the 4Ps paradigm, is also a poor choice in the case of virtual marketing and identifies two main limitations of the framework in online environments: the drastically diminished role of the Ps and the lack of any strategic elements in the model. Next to identifying the critical factors of the Web marketing, the paper argues that the basis for successful E-Commerce is the full integration of the virtual activities into the company’s physical strategy, marketing plan and organisational processes. The four S elements of the Web-Marketing Mix framework present a sound and functional conceptual basis for designing, developing and commercialising Business-to-Consumer online projects. The model was originally developed for educational purposes and has been tested and refined by means of field projects; two of them are presented as case studies in the paper.


Public Use Files of EU-SILC and EU-LFS data


title: Reciprocal Climate Negotiators

abstract: International climate negotiations have been troubled by mutual mistrust. At the same time, a hope seems to prevail that once enough countries moved forward, others would follow suit. If the abatement game faced by climate negotiators is a Prisoners' Dilemma, and countries are narrowly self-interested, such a hope seems unfounded. However, if countries display reciprocity – a preference to repay meanness by meanness and kindness by kindness – their willingness to abate will be conditional on others' abatement. I show that a full or majority coalition can be stable. This requires, however, that a majority of countries have relatively strong reciprocity preferences. No coalition participation is always stable. In addition, a stable minority coalition may exist; if so, it is weakly larger than the maximum stable coalition with standard preferences, but is characterized by mutually negative sentiments.


title: Atlas of the muscle motor points for the lower limb: implications for electrical stimulation procedures and electrode positioning

abstract: The aim of the study was to investigate the uniformity of the muscle motor point location for lower limb muscles in healthy subjects. Fifty-three subjects of both genders (age range: 18-50 years) were recruited. The muscle motor points were identified for the following ten muscles of the lower limb (dominant side): vastus medialis, rectus femoris, and vastus lateralis of the quadriceps femoris, biceps femoris, semitendinosus, and semimembranosus of the hamstring muscles, tibialis anterior, peroneus longus, lateral and medial gastrocnemius. The muscle motor point was identified by scanning the skin surface with a stimulation pen electrode and corresponded to the location of the skin area above the muscle in which an electrical pulse evoked a muscle twitch with the least injected current. For each investigated muscle, 0.15 ms square pulses were delivered through the pen electrode at low current amplitude (<10 mA) and frequency (2 Hz). 16 motor points were identified in the 10 investigated muscles of almost all subjects: 3 motor points for the vastus lateralis, 2 motor points for rectus femoris, vastus medialis, biceps femoris, and tibialis anterior, 1 motor point for the remaining muscles. An important inter-individual variability was observed for the position of the following 4 out of 16 motor points: vastus lateralis (proximal), biceps femoris (short head), semimembranosus, and medial gastrocnemius. Possible implications for electrical stimulation procedures and electrode positioning different from those commonly applied for thigh and leg muscles are discussed.


title: Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters

abstract: We consider the problem of part-of-speech tagging for informal, online conversational text. We systematically evaluate the use of large-scale unsupervised word clustering and new lexical features to improve tagging accuracy. With these features, our system achieves state-of-the-art tagging results on both Twitter and IRC POS tagging tasks; Twitter tagging is improved from 90% to 93% accuracy (more than 3% absolute). Qualitative analysis of these word clusters yields insights about NLP and linguistic phenomena in this genre. Additionally, we contribute the first POS annotation guidelines for such text and release a new dataset of English language tweets annotated using these guidelines. Tagging software, annotation guidelines, and large-scale word clusters are available at: http://www.ark.cs.cmu.edu/TweetNLP This paper describes release 0.3 of the “CMU Twitter Part-of-Speech Tagger” and annotated data. [This paper is forthcoming in Proceedings of NAACL 2013; Atlanta, GA, USA.]


title: SeVR+: Secure and privacy-aware cloud-assisted video reporting service for 5G vehicular networks

abstract: Recently, Eiza et al. proposed a secure and privacy-aware scheme for video reporting service in 5G enabled Vehicular Ad hoc Networks (VANET). They employ heterogeneous network and cloud platform to obtain more availability with low latency platform for an urgent accident video reporting service. In their study, for the first time the security issues of 5G enabled vehicular networks have been addressed. Eiza et al. claimed that their scheme guarantees user's privacy, confidentiality, non-repudiation, message integrity and availability for participant vehicles. In this paper, we show that Eiza et al. scheme is vulnerable to replay, message fabrication and DoS attacks. Regarding the sensibility of video reporting services in VANET, then, we propose an efficient protocol to overcome security weaknesses of Eiza et.al. scheme and show that the proposed protocol resists against commonplace attacks in VANET with acceptable communication and computation overhead.


title: Regression-based weight generation algorithm in neural network for solution of initial and boundary value problems

abstract: This paper introduces a new algorithm for solving ordinary differential equations (ODEs) with initial or boundary conditions. In our proposed method, the trial solution of differential equation has been used in the regression-based neural network (RBNN) model for single input and single output system. The artificial neural network (ANN) trial solution of ODE is written as sum of two terms, first one satisfies initial/boundary conditions and contains no adjustable parameters. The second part involves a RBNN model containing adjustable parameters. Network has been trained using the initial weights generated by the coefficients of regression fitting. We have used feed-forward neural network and error back propagation algorithm for minimizing error function. Proposed model has been tested for first, second and fourth-order ODEs. We also compare the results of proposed algorithm with the traditional ANN algorithm. The idea may very well be extended to other complicated differential equations.


title: A scalable collaborative filtering framework based on co-clustering

abstract: Collaborative filtering-based recommender systems have become extremely popular due to the increase in Web-based activities such as e-commerce and online content distribution. Current collaborative filtering (CF) techniques such as correlation and SVD based methods provide good accuracy, but are computationally expensive and can be deployed only in static off-line settings. However, a number of practical scenarios require dynamic real-time collaborative filtering that can allow new users, items and ratings to enter the system at a rapid rate. In this paper, we consider a novel CF approach based on a proposed weighted co-clustering algorithm (Banerjee et al., 2004) that involves simultaneous clustering of users and items. We design incremental and parallel versions of the co-clustering algorithm and use it to build an efficient real-time CF framework. Empirical evaluation demonstrates that our approach provides an accuracy comparable to that of the correlation and matrix factorization based approaches at a much lower computational cost.


title: Recognizing emotions from EEG subbands using wavelet analysis

abstract: Objectively recognizing emotions is a particularly important task to ensure that patients with emotional symptoms are given the appropriate treatments. The aim of this study was to develop an emotion recognition system using Electroencephalogram (EEG) signals to identify four emotions including happy, sad, angry, and relaxed. We approached this objective by firstly investigating the relevant EEG frequency band followed by deciding the appropriate feature extraction method. Two features were considered namely: 1. Wavelet Energy, and 2. Wavelet Entropy. EEG Channels reduction was then implemented to reduce the complexity of the features. The ground truth emotional states of each subject were inferred using Russel's circumplex model of emotion, that is, by mapping the subjectively reported degrees of valence (pleasure) and arousal to the appropriate emotions - for example, an emotion with high valence and high arousal is equivalent to a `happy' emotional state, while low valence and low arousal is equivalent to a `sad' emotional state. The Support Vector Machine (SVM) classifier was then used for mapping each feature vector into corresponding discrete emotions. The results presented in this study indicated thatWavelet features extracted from alpha, beta and gamma bands seem to provide the necessary information for describing the aforementioned emotions. Using the DEAP (Dataset for Emotion Analysis using electroencephalogram, Physiological and Video Signals), our proposed method achieved an average sensitivity and specificity of 77.4% ± 14.1% and 69.1% ± 12.8%, respectively.


title: Towards the fabrication of a UV light source based on CuCl thin films

abstract: CuCl is an ionic wide band gap I–VII semiconductor which has a band gap of 3.4 eV (at 300 K) and a large excitonic binding energy of 190 meV compared to other wide band gap semiconductors, e.g. ZnO (60 meV) and GaN (25 meV). It can be useful as a UV source which can emit light in the blue-UV range. The large excitonic binding energy of CuCl should improve the quantum efficiency compared to ZnO or GaN. In room temperature UV–VIS spectroscopy measurements we have observed strong free Z3 and Z1,2 excitonic peaks in vacuum-deposited CuCl films incorporated within structures for electroluminescent devices. Using room temperature photoluminescence (PL) we have also observed a strong free excitonic peak which is attributed to Z3. We also report on the design of a UV source using electrically pumped CuCl thin films on Si and ITO coated glass substrates. This could open up the possibility of fabricating UV/blue light emitters utilizing CuCl.


title: A Benchmark for Breast Ultrasound Image Segmentation (BUSIS)

abstract: Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Computer-Aided Diagnosis (CAD) systems. Many BUS segmentation approaches have been proposed in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which result in discrepancy in performance comparison.


title: Compensation of vehicle-specific antenna radome effects at 5.9 GHz

abstract: Future vehicles are expected to integrate multiple radio access technologies to offer traffic information services for Intelligent Transportation Systems (ITS). Dedicated short range communication (DSRC) being designed exclusively for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is one of the major candidates to enable such services. The focus of this paper is to shed light on the integration aspects of this technology, especially in regards to the antenna design. A methodology is presented to overcome the difficulties occurring from the integration of the antenna in the radome on the vehicle rooftop. With help of simulations and measurements it is illustrated how the conventionally unwanted dielectric lens effect of the antenna radome resulting in nulls in the radiation pattern can be used to deliberately increase the antenna performance. The performance enhancement by using the antenna radome as a dielectric lens is validated with four prototypes in a semi-anechoic chamber. It is moreover shown that simplified geometrical shapes such as cuboids can be used to describe efficiently the behavior of geometrical more complex radome designs. The greatest advantage of our proposed methodology to enhance antenna performance is that it can be employed without changes of any electronic control units. Thus it can easily be included in a running production cycle without even altering any design elements of the vehicle. To demonstrate to which degree the achieved enhancement on component level translates into system level we also present simulation results of a test scenario including a vehicle and a road-side unit. The outcome of the system level simulations shows substantial improvements in the received signal strength (RSS).


title: Microneedling in All Skin Types: A Review.

abstract: Abstract INTRODUCTION: Microneedling procedures are growing in popularity for a wide variety of skin conditions. This paper comprehensively reviews the medical literature regarding skin needling efficacy and safety in all skin types and in multiple dermatologic conditions. METHODS: A PubMed literature search was conducted in all languages without restriction and bibliographies of relevant articles reviewed. Search terms included: "microneedling," "percutaneous collagen induction," "needling," "skin needling," and "dermaroller." RESULTS: Microneedling is most commonly used for acne scars and cosmetic rejuvenation, however, treatment benefit has also been seen in varicella scars, burn scars, keloids, acne, alopecia, and periorbital melanosis, and has improved flap and graft survival, and enhanced transdermal delivery of topical products. Side effects were mild and self-limited, with few reports of post-inflammatory hyperpigmentation, and isolated reports of tram tracking, facial allergic granuloma, and systemic hypersensitivity. DISCUSS: Microneedling represents a safe, cost-effective, and efficacious treatment option for a variety of dermatologic conditions in all skin types. More double-blinded, randomized, controlled trials are required to make more definitive conclusions. J Drugs Dermatol. 2017;16(4):308-314. .


title: Stretchable organic optoelectronic sensorimotor synapse

abstract: Emulation of human sensory and motor functions becomes a core technology in bioinspired electronics for next-generation electronic prosthetics and neurologically inspired robotics. An electronic synapse functionalized with an artificial sensory receptor and an artificial motor unit can be a fundamental element of bioinspired soft electronics. Here, we report an organic optoelectronic sensorimotor synapse that uses an organic optoelectronic synapse and a neuromuscular system based on a stretchable organic nanowire synaptic transistor (s-ONWST). The voltage pulses of a self-powered photodetector triggered by optical signals drive the s-ONWST, and resultant informative synaptic outputs are used not only for optical wireless communication of human-machine interfaces but also for light-interactive actuation of an artificial muscle actuator in the same way that a biological muscle fiber contracts. Our organic optoelectronic sensorimotor synapse suggests a promising strategy toward developing bioinspired soft electronics, neurologically inspired robotics, and electronic prostheses.


title: Attributing Fake Images to GANs: Analyzing Fingerprints in Generated Images.

abstract: Research in computer graphics has been in pursuit of realistic image generation for a long time. Recent advances in machine learning with deep generative models have shown increasing success of closing the realism gap by using data-driven and learned components. There is an increasing concern that real and fake images will become more and more difficult to tell apart. We take a first step towards this larger research challenge by asking the question if and to what extend a generated fake image can be attribute to a particular Generative Adversarial Networks (GANs) of a certain architecture and trained with particular data and random seed. Our analysis shows single samples from GANs carry highly characteristic fingerprints which make attribution of images to GANs possible. Surprisingly, this is even possible for GANs with same architecture and same training that only differ by the training seed.


title: Incomplete high-dimensional data imputation algorithm using feature selection and clustering analysis on cloud

abstract: Incomplete data imputation plays an important role in big data analysis and smart computing. Existing algorithms are of low efficiency and effectiveness in imputing incomplete high-dimensional data. The paper proposes an incomplete high-dimensional data imputation algorithm based on feature selection and cluster analysis (IHDIFC), which works in three steps. First, a hierarchical clustering-based feature subset selection algorithm is designed to reduce the dimensions of the data set. Second, a parallel $$k$$k-means algorithm based on partial distance is derived to cluster the selected data subset efficiently. Finally, the data objects in the same cluster with the target are utilized to estimate its missing feature values. Extensive experiments are carried out to compare IHDIFC to two representative missing data imputation algorithms, namely FIMUS and DMI. The results demonstrate that the proposed algorithm achieves better imputation accuracy and takes significantly less time than other algorithms for imputing high-dimensional data.


High-resolution sea ice dynamics modeling using the discrete element method


title: Head mouse: generalisability of research focused on the disabled to able bodied users

abstract: This paper discusses a study which aimed to determine whether the results obtained from studies focusing on users with disabilities are comparable with the results from users without disabilities for head operated cursor control. Four categories of research findings were investigated namely: the optimum sensitivity setting, the amount of neck fatigue, learnability and the level of user satisfaction. A shooting genre game was developed, and a task was included during development to simulate gaming actions with moving targets. Data for four efficiency metrics was gathered during two phases of user testing. The low sensitivity setting resulted in the fastest gameplay for users without disabilities, which contradicted research results from studies focusing on individuals with disabilities. It was also found that the amount of neck fatigue decreased after several sessions for users without disabilities. The efficient use of the head mouse by individuals without disabilities showed significant improvement over time for certain elements, indicating that learning took place. The level of user satisfaction also increased with extended use of the head mouse by individuals without disabilities. Although some findings can be generalized to both types of users, this study shows that new knowledge was gained in terms of able bodied use of a head mouse.


title: A taste of tweets: reverse engineering Twitter spammers

abstract: In this paper, through reverse engineering Twitter spammers' tastes (their preferred targets to spam), we aim at providing guidelines for building more effective social honeypots, and generating new insights to defend against social spammers. Specifically, we first perform a measurement study by deploying "benchmark" social honeypots on Twitter with diverse and fine-grained social behavior patterns to trap spammers. After five months' data collection, we make a deep analysis on how Twitter spammers find their targets. Based on the analysis, we evaluate our new guidelines for building effective social honeypots by implementing "advanced" honeypots. Particularly, within the same time period, using those advanced honeypots can trap spammers around 26 times faster than using "traditional" honeypots. In the second part of our study, we investigate new active collection approaches to complement the fundamentally passive procedure of using honeypots to slowly attract spammers. Our goal is that, given limited resources/time, instead of blindly crawling all possible (or randomly sampling) Twitter accounts at the first place (for later spammer analysis), we need a lightweight strategy to prioritize the active crawling/sampling of more likely spam accounts from the huge Twittersphere. Applying what we have learned about the tastes of spammers, we design two new, active and guided sampling approaches for collecting most likely spammer accounts during the crawling. According to our evaluation, our strategies could efficiently crawl/sample over 17,000 spam accounts within a short time with a considerably high "Hit Ratio", i.e., collecting 6 correct spam accounts in every 10 sampled accounts.


title: Emotion-based Music Visualization using Photos

abstract: Music players for personal computers are often featured with music visualization by generating animated patterns according to the music's low-level features such as loudness and spectrum. This paper proposes an emotion-based music player which synchronizes visualization (photos) with music based on the emotions evoked by auditory stimulus of music and visual content of visualization. For emotion detection from photos, we collected 398 photos with their emotions annotated by 496 users through the web. With these annotations, a Bayesian classification method is proposed for automatic photo emotion detection. For emotion detection from music, we adopt an existing method. Finally, for composition of music and photos, in addition to matching high-level emotions, we also consider low-level feature harmony and temporal visual coherence. It is formulated as an optimization problem and solved by a greedy algorithm. Subjective evaluation shows emotion-based music visualization enriches users' listening experiences.


title: Mobile device fingerprinting considered harmful for risk-based authentication

abstract: In this paper, we present a critical assessment of the use of device fingerprinting for risk-based authentication in a state-of-practice identity and access management system. Risk-based authentication automatically elevates the level of authentication whenever a particular risk threshold is exceeded. Contemporary identity and access management systems frequently leverage browser-based device fingerprints to recognize trusted devices of a certain individual. We analyzed the variability and the predictability of mobile device fingerprints. Our research shows that particularly for mobile devices the fingerprints carry a lot of similarity, even across models and brands, making them less reliable for risk assessment and step-up authentication.


title: The Proficiency-Congruency Dilemma: Virtual Team Design and Performance in Multiplayer Online Games

abstract: Multiplayer online battle arena games provide an excellent opportunity to study team performance. When designing a team, players must negotiate a proficiency-congruency dilemma between selecting roles that best match their experience and roles that best complement the existing roles on the team. We adopt a mixed-methods approach to explore how players negotiate this dilemma. Using data from League of Legends, we define a similarity space to operationalize team design constructs about role proficiency, generality, and congruency. We collect publicly available data from 3.36 million players to test the influence of these constructs on team performance. We also conduct focus groups with novice and elite players to understand how players' team design practices vary with expertise. We find that the two factors, player proficiency and team congruency, both increase team performance, with the former having a stronger impact. We also find that elite players are better at balancing the two factors than the novice players. These findings have implications for players, designers, and theorists about how to recommend team designs that jointly prioritize individuals' expertise and teams' compatibility.


title: Automatic crop irrigation system

abstract: India is agriculture based nation. It is necessary to improve the productivity and quality of agro based products. The proposed design is an automatic system that aids the famers in irrigation process. It keeps notifying the farmer through an on-board LCD display and messages that is sent on the farmer's cellular number. This proposed design is also helpful for the farmers who are facing power failure issues to maintain a uniform water supply due to power failure or inadequate and non-uniform water supply. The automatic irrigation system also keeps the farmer updated with all the background activities through a SIM900 Module that sends messages on the registered number. This device can be a turning point for our society. The device is easily affordable by the farmers of the country. This proposed design is helpful for reducing the human labour. This is a low budget system with an essential social application.


title: Bayesian Poker

abstract: Poker is ideal for testing automated reasoning under uncertainty. It introduces uncertainty both by physical randomization and by incomplete information about opponents hands.Another source OF uncertainty IS the limited information available TO construct psychological models OF opponents, their tendencies TO bluff, play conservatively, reveal weakness, etc. AND the relation BETWEEN their hand strengths AND betting behaviour. ALL OF these uncertainties must be assessed accurately AND combined effectively FOR ANY reasonable LEVEL OF skill IN the game TO be achieved, since good decision making IS highly sensitive TO those tasks.We describe our Bayesian Poker Program(BPP), which uses a Bayesian network TO model the programs poker hand, the opponents hand AND the opponents playing behaviour conditioned upon the hand, and betting curves which govern play given a probability of winning. The history of play with opponents is used to improve BPPs understanding OF their behaviour.We compare BPP experimentally WITH : a simple RULE - based system; a program which depends exclusively ON hand probabilities(i.e., without opponent modeling); AND WITH human players.BPP has shown itself TO be an effective player against ALL these opponents, barring the better humans.We also sketch out SOME likely ways OF improving play.


NOVEL NAVIGATION MOBILE ROBOT IMPLEMENTATION BASED ON RFID NAVIGATION SYSTEM


title: Data mining approach to situation-aware sensor actuation in wireless sensor networks

abstract: As wireless sensor networks (WSN) are used in seemingly limitless applications with more sensor types available, large sensor data sets are acquired and transmitted for additional processing. These large data sets may cause network traffics slow unnecessarily. The unfiltered transmission of raw data will also consume the WSN batteries inefficiently. In recent years, a few techniques have been developed for event-driven WSNs and sensor collaborations. For example, in traffic surveillance camera systems, video clips are transmitted in the event of car collision noises. Event-driven services run as a preset parameter of surveillance sensing. It is likely that there may be a false-positive event which drives video data to be transmitted unnecessarily, or false-negative event which misses alarms, both leading to accurate surveillance. This paper proposes an adaptive technique for event-driven WSNs, in which learning from a classification process is employed to fine-tune pre-defined data-driven WSNs. Unlike to traditional data classification techniques, outliers of collected sensor data will be one of the critical data points. The proposed technique functions more proactively by being aware of the environmental situation changes. The contribution of this paper includes an adaptive technique of acquiring and utilizing sensor data.


title: Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns

abstract: Although opinion spam (or fake review) detection has attracted significant research attention in recent years, the problem is far from solved. One key reason is that there is no large-scale ground truth labeled dataset available for model building. Some review hosting sites such as Yelp.com and Dianping.com have built fake review filtering systems to ensure the quality of their reviews, but their algorithms are trade secrets. Working with Dianping, we present the first large-scale analysis of restaurant reviews filtered by Dianping's fake review filtering system. Along with the analysis, we also propose some novel temporal and spatial features for supervised opinion spam detection. Our results show that these features significantly outperform existing state-of-art features.


title: Data mining based fragmentation and prediction of medical data

abstract: Data mining concerns theories, methodologies, and in particular, computer systems for knowledge extraction or mining from large amounts of data. Association rule mining is a general purpose rule discovery scheme. It has been widely used for discovering rules in medical applications. The diagnosis of diseases is a significant and tedious task in medicine. The detection of heart disease from various factors or symptoms is an issue which is not free from false presumptions often accompanied by unpredictable effects. Thus the effort to utilize knowledge and experience of numerous specialists and clinical screening data of patients collected in databases to facilitate the diagnosis process is considered a valuable option. In this paper, we presented an efficient approach for the prediction of heart attack risk levels from the heart disease database. Firstly, the heart disease database is clustered using the K-means clustering algorithm, which will extract the data relevant to heart attack from the database. This approach allows mastering the number of fragments through its k parameter. Subsequently the frequent patterns are mined from the extracted data, relevant to heart disease, using the MAFIA (Maximal Frequent Itemset Algorithm) algorithm. The machine learning algorithm is trained with the selected significant patterns for the effective prediction of heart attack. We have employed the ID3 algorithm as the training algorithm to show level of heart attack with the decision tree. The results showed that the designed prediction system is capable of predicting the heart attack effectively.


A Mostly Data-Driven Approach to Inverse Text Normalization


title: VideoSense: A Contextual In-Video Advertising System

abstract: With Internet delivery of video content surging to an unprecedented level, video has become one of the primary sources for online advertising. In this paper, we present VideoSense as a novel contextual in-video advertising system, which automatically associates the relevant video ads and seamlessly inserts the ads at the appropriate positions within each individual video. Unlike most video sites which treat video advertising as general text advertising by displaying video ads at the beginning or the end of a video or around a video, VideoSense aims to embed more contextually relevant ads at less intrusive positions within the video stream. Specifically, given a Web page containing an online video, VideoSense is able to extract the surrounding text related to this video, detect a set of candidate ad insertion positions based on video content discontinuity and attractiveness, select a list of relevant candidate ads according to multimodal relevance. To support contextual advertising, we formulate this task as a nonlinear 0-1 integer programming problem by maximizing contextual relevance while minimizing content intrusiveness at the same time. The experiments proved the effectiveness of VideoSense for online video service.


title: Public health in community pharmacy: A systematic review of pharmacist and consumer views

abstract: BackgroundThe increasing involvement of pharmacists in public health will require changes in the behaviour of both pharmacists and the general public. A great deal of research has shown that attitudes and beliefs are important determinants of behaviour. This review aims to examine the beliefs and attitudes of pharmacists and consumers towards pharmaceutical public health in order to inform how best to support and improve this service.MethodsFive electronic databases were searched for articles published in English between 2001 and 2010. Titles and abstracts were screened by one researcher according to the inclusion criteria. Papers were included if they assessed pharmacy staff or consumer attitudes towards pharmaceutical public health. Full papers identified for inclusion were assessed by a second researcher and data were extracted by one researcher.ResultsFrom the 5628 papers identified, 63 studies in 67 papers were included. Pharmacy staff: Most pharmacists viewed public health services as important and part of their role but secondary to medicine related roles. Pharmacists' confidence in providing public health services was on the whole average to low. Time was consistently identified as a barrier to providing public health services. Lack of an adequate counselling space, lack of demand and expectation of a negative reaction from customers were also reported by some pharmacists as barriers. A need for further training was identified in relation to a number of public health services. Consumers: Most pharmacy users had never been offered public health services by their pharmacist and did not expect to be offered. Consumers viewed pharmacists as appropriate providers of public health advice but had mixed views on the pharmacists' ability to do this. Satisfaction was found to be high in those that had experienced pharmaceutical public healthConclusionsThere has been little change in customer and pharmacist attitudes since reviews conducted nearly 10 years previously. In order to improve the public health services provided in community pharmacy, training must aim to increase pharmacists' confidence in providing these services. Confident, well trained pharmacists should be able to offer public health service more proactively which is likely to have a positive impact on customer attitudes and health.


Pincer nail


title: Flicker correction for archived film sequences using a nonlinear model

abstract: A new model for the correction of flicker in archived film sequences is formulated from first principles. The model takes into account the Hurter-Driffield density versus log exposure characteristic to estimate image intensity errors caused by exposure inconsistencies. The model is shown to be highly nonlinear and can accommodate typical grayscale manipulations performed during film-to-video transfers. Motivated by the above model, an algorithm for the estimation of a correction profile across the available grayscale is developed. This profile is obtained from histogrammed gray-level differences between a reference and a degraded frame and is robust with respect to intensity changes due to scene and camera motion. Our results show that the proposed algorithm is very effective toward removing flicker for a variety of archived sequences and compares favorably with other state-of-the-art techniques developed for the same purpose. Additionally, the parametric form of the profile makes it particularly suitable for metadata use and allows fast inversion of the restoration process and recovery of the original material.


title: Effect of pH-Responsive Alginate/Chitosan Multilayers Coating on Delivery Efficiency, Cellular Uptake and Biodistribution of Mesoporous Silica Nanoparticles Based Nanocarriers

abstract: Surface fuctionalization plays a crucial role in developing efficient nanoparticulate drug-delivery systems by improving their therapeutic efficacy and minimizing adverse effects. Here we propose a simple layer-by-layer self-assembly technique capable of constructing mesoporous silica nanoparticles (MSNs) into a pH-responsive drug delivery system with enhanced efficacy and biocompatibility. In this system, biocompatible polyelectrolyte multilayers of alginate/chitosan were assembled on MSN’s surface to achieve pH-responsive nanocarriers. The functionalized MSNs exhibited improved blood compatibility over the bare MSNs in terms of low hemolytic and cytotoxic activity against human red blood cells. As a proof-of-concept, the anticancer drug doxorubicin (DOX) was loaded into nanocarriers to evaluate their use for the pH-responsive drug release both in vitro and in vivo. The DOX release from nanocarriers was pH dependent, and the release rate was much faster at lower pH than that of at higher pH. The in vitro...


title: Hybrid Wood Materials with Magnetic Anisotropy Dictated by the Hierarchical Cell Structure

abstract: Anisotropic and hierarchical structures are bound in nature and highly desired in engineered materials, due to their outstanding functions and performance. Mimicking such natural features with synthetic materials and methods has been a highly active area of research in the last decades. Unlike these methods, we use the native biomaterial wood, with its intrinsic anisotropy and hierarchy as a directional scaffold for the incorporation of magnetic nanoparticles inside the wood material. Nanocrystalline iron oxide particles were synthesized in situ via coprecipitation of ferric and ferrous ions within the interconnected pore network of bulk wood. Imaging with low-vacuum and cryogenic electron microscopy as well as spectral Raman mapping revealed layered nanosize particles firmly attached to the inner surface of the wood cell walls. The mineralogy of iron oxide was identified by XRD powder diffraction and Raman spectroscopy as a mixture of the spinel phases magnetite and maghemite. The intrinsic structural architecture of native wood entails a three-dimensional assembly of the colloidal iron oxide which results in direction-dependent magnetic features of the wood-mineral hybrid material. This superinduced magnetic anisotropy, as quantified by direction-dependent magnetic hysteresis loops and low-field susceptibility tensors, allows for directional lift, drag, alignment, (re)orientation, and actuation, and opens up novel applications of the natural resource wood.


title: Ecotoxicology of Metals—Sources, Transport, and Effects on the Ecosystem

abstract: Abstract Metal-smelting and combustion processes are the major anthropogenic sources for the emission of metals to the atmosphere. Metals emitted this way are generally bound in small particles with the potential for atmospheric long-range transport and contamination. Sediments are the ultimate sink for metals emitted to the aquatic environment; this is most evident in coastal and estuarine sediments. Besides direct emissions, man-made environmental changes such as acidification, oxygen depletion, and draining of waterlogged areas may mobilize metals not otherwise available to organisms. The speciation of metals is an important determinant for their bioavailability, uptake, and toxicity in the environment. Organisms have developed a wide range of mechanisms for the internal handling and storage of essential and/or toxic metals. No metal other than mercury is consistently biomagnified along food chains. In the environment, the most adverse impacts of metals (on populations) have been caused by organic tin compounds used in antifouling paints and aluminum mobilized by acid rain. High concentrations of organic mercury and cadmium in certain organisms may cause concerns for the health of human populations that rely heavily on their use as food items.


title: SoftWater: Software-defined networking for next-generation underwater communication systems

abstract: Abstract Underwater communication systems have drawn the attention of the research community in the last 15 years. This growing interest can largely be attributed to new civil and military applications enabled by large-scale networks of underwater devices (e.g., underwater static sensors, unmanned autonomous vehicles (AUVs), and autonomous robots), which can retrieve information from the aquatic and marine environment, perform in-network processing on the extracted data, and transmit the collected information to remote locations. Currently underwater communication systems are inherently hardware-based and rely on closed and inflexible architectural design. This imposes significant challenges into adopting new underwater communication and networking technologies, prevent the provision of truly-differentiated services to highly diverse underwater applications, and induce great barriers to integrate heterogeneous underwater devices. Software-defined networking (SDN), recognized as the next-generation networking paradigm, relies on the highly flexible, programmable, and virtualizable network architecture to dramatically improve network resource utilization, simplify network management, reduce operating cost, and promote innovation and evolution. In this paper, a software-defined architecture, namely SoftWater, is first introduced to facilitate the development of the next-generation underwater communication systems. More specifically, by exploiting the network function virtualization (NFV) and network virtualization concepts, SoftWater architecture can easily incorporate new underwater communication solutions, accordingly maximize the network capacity, can achieve the network robustness and energy efficiency, as well as can provide truly differentiated and scalable networking services. Consequently, the SoftWater architecture can simultaneously support a variety of different underwater applications, and can enable the interoperability of underwater devices from different manufacturers that operate on different underwater communication technologies based on acoustic, optical, or radio waves. Moreover, the essential network management tools of SoftWater are discussed, including reconfigurable multi-controller placement, hybrid in-band and out-of-band control traffic balancing, and utility-optimal network virtualization. Furthermore, the major benefits of SoftWater architecture are demonstrated by introducing software-defined underwater networking solutions, including the throughput-optimal underwater routing, SDN-enhanced fault recovery, and software-defined underwater mobility management. The research challenges to realize the SoftWater are also discussed in detail.


title: Functional Brain Connectivity Using fMRI in Aging and Alzheimer’s Disease

abstract: Normal aging and Alzheimer’s disease (AD) cause profound changes in the brain’s structure and function. AD in particular is accompanied by widespread cortical neuronal loss, and loss of connections between brain systems. This degeneration of neural pathways disrupts the functional coherence of brain activation. Recent innovations in brain imaging have detected characteristic disruptions in functional networks. Here we review studies examining changes in functional connectivity, measured through fMRI (functional magnetic resonance imaging), starting with healthy aging and then Alzheimer’s disease. We cover studies that employ the three primary methods to analyze functional connectivity—seed-based, ICA (independent components analysis), and graph theory. At the end we include a brief discussion of other methodologies, such as EEG (electroencephalography), MEG (magnetoencephalography), and PET (positron emission tomography). We also describe multi-modal studies that combine rsfMRI (resting state fMRI) with PET imaging, as well as studies examining the effects of medications. Overall, connectivity and network integrity appear to decrease in healthy aging, but this decrease is accelerated in AD, with specific systems hit hardest, such as the default mode network (DMN). Functional connectivity is a relatively new topic of research, but it holds great promise in revealing how brain network dynamics change across the lifespan and in disease.


title: Acquired preferences for piquant foods by chimpanzees

abstract: Humans frequently develop likings for innately unpalatable substances, while this occurs very rarely in non-humans. In this study, we establish a preference for crackers seasoned with chili pepper in two domesticated chimpanzees. Chimps were offered a series of increasingly piquant crackers by their caretaker, and gradually came to prefer these crackers to unseasoned crackers. The preferences were stable over months, and generalized to a different piquant cracker. Available evidence suggests that these are acquired likes rather than preferences maintained because of positive consequences that follow ingestion. We note that all existing instances of acquired likings for innately aversive foods in animals (including some informal results from dogs presented in this paper) involve animals with a close personal relationship with humans, suggesting an important role for social-affective factors in the reversal of innate aversions.


title: Understanding together: sensemaking in collaborative information seeking

abstract: An important aspect of collaborative information seeking (CIS) is making sense of the information found, i.e., collaborative sensemaking. We conducted an ethnographic study of the CIS practices of healthcare providers in a hospital emergency department to gain a conceptual understanding of when and how collaborative sensemaking occurs during CIS activities. We present occasions and characteristics of collaborative sensemaking and design implications for collaborative information retrieval tools to support sensemaking.


title: Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation

abstract: Collaborative recommendation has attracted various research works in recent years. However, an important problem setting, i.e., "a user examined several items but only rated a few", has not received much attention yet. We coin this problem heterogeneous collaborative recommendation (HCR) from the perspective of users' heterogeneous feedbacks of implicit examinations and explicit ratings. In order to fully exploit such different types of feedbacks, we propose a novel and generic solution called compressed knowledge transfer via factorization machine (CKT-FM). Specifically, we assume that the compressed knowledge of user homophily and item correlation, i.e., user groups and item sets behind two types of feedbacks, are similar and then design a two-step transfer learning solution including compressed knowledge mining and integration. Our solution is able to transfer high quality knowledge via noise reduction, to model rich pairwise interactions among individual-level and cluster-level entities, and to adapt the potential inconsistent knowledge from implicit feedbacks to explicit feedbacks. Furthermore, the analysis on time complexity and space complexity shows that our solution is much more efficient than the state-of-the-art method for heterogeneous feedbacks. Extensive empirical studies on two large data sets show that our solution is significantly better than the state-of-the-art non-transfer learning method w.r.t. recommendation accuracy, and is much more efficient than that of leveraging the raw implicit examinations directly instead of compressed knowledge w.r.t. CPU time and memory usage. Hence, our CKT-FM strikes a good balance between effectiveness and efficiency of knowledge transfer in HCR.


title: Enabling Supply Chain Agility through IOS Integration and Supply Chain Flexibility

abstract: In today’s turbulent environment, competitive pressures and market unpredictability have dramatically lashed business profits. Agility is an essential ability for firms facing such an environment. However, coping with the hostile business environment requires not only the agility from individual firms but also the collaboration from their supply chain partners. Supply chain agility (SCA) therefore is vital to the competitiveness and performance of a firm and its supply chain partners. Exploring how to effectively enable SCA is significant for both practice and theory development. Recently, firms are increasingly relying on integrated information systems and analytical tools, such as business intelligent systems, and close collaboration with their supplier chain partners to enhance their responsiveness. This paper seeks to broaden the understanding about the enabling roles of IOS integration, analytical ability of inter-organizational information systems (IOS), and supply chain flexibility on SCA based on the dynamic capabilities view and real options theory. With a sample of 147 matched-pair data gathered from the top 2000 Taiwanese manufacturing firms, our results support the effect of IOS integration on supply chain flexibility, including offering and sourcing flexibilities, which in turn facilitate SCA. We also propose and show the moderating effect of analytical ability of IOS on the process of enabling SCA. These results contribute to a better understanding of how SCA may be achieved in theory and in practice. The model and findings of this study should be able to serve as a basis for future research for studying SCA.


A Survey on LEACH-Based Energy Aware Protocols for Wireless Sensor Networks


title: Towards Conversational Search and Recommendation: System Ask, User Respond

abstract: Conversational search and recommendation based on user-system dialogs exhibit major differences from conventional search and recommendation tasks in that 1) the user and system can interact for multiple semantically coherent rounds on a task through natural language dialog, and 2) it becomes possible for the system to understand the user needs or to help users clarify their needs by asking appropriate questions from the users directly. We believe the ability to ask questions so as to actively clarify the user needs is one of the most important advantages of conversational search and recommendation. In this paper, we propose and evaluate a unified conversational search/recommendation framework, in an attempt to make the research problem doable under a standard formalization. Specifically, we propose a System Ask -- User Respond (SAUR) paradigm for conversational search, define the major components of the paradigm, and design a unified implementation of the framework for product search and recommendation in e-commerce. To accomplish this, we propose the Multi-Memory Network (MMN) architecture, which can be trained based on large-scale collections of user reviews in e-commerce. The system is capable of asking aspect-based questions in the right order so as to understand the user needs, while (personalized) search is conducted during the conversation, and results are provided when the system feels confident. Experiments on real-world user purchasing data verified the advantages of conversational search and recommendation against conventional search and recommendation algorithms in terms of standard evaluation measures such as NDCG.


title: Roadside Magnetic Sensor System for Vehicle Detection in Urban Environments

abstract: Intelligent Transportation Systems (ITS) are widely researched to improve the traffic situation. In ITS, vehicle detection system plays a significant role. At present, vehicle detection is often conducted by inductive loops, which are very expensive and inconvenient to install and maintain. Video camera is another frequently used detector, but it needs high computing power. In order to solve these problems, this paper focuses on the development of a roadside magnetic sensor system for vehicle detection. The device is installed at the side of the road and measures traffic in the adjacent lane (the closest lane to the sensor node). The data are transmitted by the IEEE 802.15.4 communication protocol. A novel adapted threshold state machine algorithm is proposed to detect vehicles. Since false judgments created by large vehicles passing in the nonadjacent lane (the lane next to the closest lane to the sensor node) are frequent in urban environments, a novel feature extracted by fusion of three magnetic sensor signals are proposed to reduce this error. The developed magnetic sensor system is wireless, compact, and cost-effective. The experimental results show that the proposed system achieves high accuracy and is viable in urban environments.


title: Measuring firm performance using financial ratios: A decision tree approach

abstract: Determining the firm performance using a set of financial measures/ratios has been an interesting and challenging problem for many researchers and practitioners. Identification of factors (i.e., financial measures/ratios) that can accurately predict the firm performance is of great interest to any decision maker. In this study, we employed a two-step analysis methodology: first, using exploratory factor analysis (EFA) we identified (and validated) underlying dimensions of the financial ratios, followed by using predictive modeling methods to discover the potential relationships between the firm performance and financial ratios. Four popular decision tree algorithms (CHAID, C5.0, QUEST and C&RT) were used to investigate the impact of financial ratios on firm performance. After developing prediction models, information fusion-based sensitivity analyses were performed to measure the relative importance of independent variables. The results showed the CHAID and C5.0 decision tree algorithms produced the best prediction accuracy. Sensitivity analysis results indicated that Earnings Before Tax-to-Equity Ratio and Net Profit Margin are the two most important variables.


title: Combatting Online Fraud in Saudi Arabia Using General Deterrence Theory (GDT)

abstract: Online fraud, described as dubious business transactions and deceit carried out electronically, has reached an alarming rate worldwide and has become a major challenge to organizations and governments. In the Gulf region, particularly Saudi Arabia, where there is high Internet penetration and many online financial transactions, the need to put effective measures to deter, prevent and detect online fraud, has become imperative. This paper examines how online fraud control measures in financial institutions in Saudi Arabia are organized and managed. Through qualitative interviews with experts in Saudi Arabia, the study found that people’s perceptions (from their moral, social, cultural and religious backgrounds) have significant effect on awareness and fraud prevention and detection. It also argues that technological measures alone may not be adequate. Deterrence, prevention, detection and remedy activities, together making General Deterrence Theory (GDT) as an approach for systematically and effectively combatting online fraud in Saudi.


title: Automatic creation of WordNets from parallel corpora

abstract: In this paper we present the evaluation results for the creation of WordNets for five languages (Spanish, French, German, Italian and Portuguese) using an approach based on parallel corpora. We have used three very large parallel corpora for our experiments: DGT-TM, EMEA and ECB. The English part of each corpus is semantically tagged using Freeling and UKB. After this step, the process of WordNet creation is converted into a word alignment problem, where we want to align WordNet synsets in the English part of the corpus with lemmata on the target language part of the corpus. The word alignment algorithm used in these experiments is a simple most frequent translation algorithm implemented into the WN-Toolkit. The obtained precision values are quite satisfactory, but the overall number of extracted synset-variant pairs is too low, leading into very poor recall values. In the conclusions, the use of more advanced word alignment algorithms, such as Giza++, Fast Align or Berkeley aligner is suggested.


title: A high-throughput and energy-efficient RRAM-based convolutional neural network using data encoding and dynamic quantization

abstract: To solve the scaling, memory wall and high power density issues, recently RRAM-based accelerators, which show a better energy and area efficiency compared with the CMOS-based counterparts, have been proposed for convolutional neural networks. However, the RRAM-based architectures still face several design challenges, including the high energy and timing overhead at the analog/digital (A/D) conversion and interfacing circuits. To address these issues, we propose several novel optimization schemes in this work. First an encoding scheme for the synaptic weights and the input feature maps is proposed to reduce the energy of the in-situ computation and the bit-resolution of the A/D conversion. Then the resolution of the A/D conversion is further optimized for a lower energy consumption. Moreover, a dynamic quantization scheme for the multiply-accumulate operations (MACs) is proposed to improve the throughput and the energy efficiency by reducing the number of partial products. Experimental results show that the throughput, the energy efficiency and the area efficiency are improved by 2 to 4 times when compared with the state-of-the-art RRAM-based accelerators.


title: Efficacy of carbocisteine in the treatment of chronic obstructive pulmonary disease and impact on the quality of life.

abstract: Aim To investigate the effects of carbocisteine treatment in the reduction of frequency of productive cough episodes, preventing disease progression and improving the quality of life as well as the tolerability of the administered treatment and patient compliance during the study. Methods This observational, non-interventional, multicenter, cohort study included 501 patients with chronic obstructive pulmonary disease (COPD) who were administrated carbocisteine capsules 375 mg and followed up during the next 15 days. The patients were observed at 3 points, baseline and two additional assessments. General clinical condition of patients, along with the spirometry testing at all three points were examined. Thr quality of life was assessed on the 1st and 3rd observation with Leicester Cough Questionnaire. Tolerability and patient compliance were measured throughout the study. Results There was a significant change of forced expiratory volume in 1 second (FEV1) status between the second and third observation (p=0.002). Examination of general symptoms showed a statistically significant reduction in cough by 74.9%, in sputum production by 48.5%, in dyspnea by 29% and in fatigue by 50%. After the administration of carbocisteine the median value of overall quality of life was 3.79 (3.63 - 3.89). Conclusion 375mg carbocisteine capsules were found to be effective and well-tolerated in the treatment of COPD, with a small percentage of reported mild adverse reactions and with a significant improvement of quality of life.


title: SA-EAST: Security-Aware Efficient Data Transmission for ITS in Mobile Heterogeneous Cloud Computing

abstract: The expected advanced network explorations and the growing demand for mobile data sharing and transferring have driven numerous novel applications in Cyber-Physical Systems (CPSs), such as Intelligent Transportation Systems (ITSs). However, current ITS implementations are restricted by the conflicts between security and communication efficiency. Focusing on this issue, this article proposes a Security-Aware Efficient Data Sharing and Transferring (SA-EAST) model, which is designed for securing cloud-based ITS implementations. In applying this approach, we aim to obtain secure real-time multimedia data sharing and transferring. Our experimental evaluation has shown that our proposed model provides an effective performance in securing communications for ITS.


title: Inferring user demographics and social strategies in mobile social networks

abstract: Demographics are widely used in marketing to characterize different types of customers. However, in practice, demographic information such as age, gender, and location is usually unavailable due to privacy and other reasons. In this paper, we aim to harness the power of big data to automatically infer users' demographics based on their daily mobile communication patterns. Our study is based on a real-world large mobile network of more than 7,000,000 users and over 1,000,000,000 communication records (CALL and SMS). We discover several interesting social strategies that mobile users frequently use to maintain their social connections. First, young people are very active in broadening their social circles, while seniors tend to keep close but more stable connections. Second, female users put more attention on cross-generation interactions than male users, though interactions between male and female users are frequent. Third, a persistent same-gender triadic pattern over one's lifetime is discovered for the first time, while more complex opposite-gender triadic patterns are only exhibited among young people. We further study to what extent users' demographics can be inferred from their mobile communications. As a special case, we formalize a problem of double dependent-variable prediction-inferring user gender and age simultaneously. We propose the WhoAmI method, a Double Dependent-Variable Factor Graph Model, to address this problem by considering not only the effects of features on gender/age, but also the interrelation between gender and age. Our experiments show that the proposed WhoAmI method significantly improves the prediction accuracy by up to 10% compared with several alternative methods.


title: Linguistic Reflections of Student Engagement in Massive Open Online Courses

abstract: While data from Massive Open Online Courses (MOOCs) offers the potential to gain new insights into the ways in which online communities can contribute to student learning, much of the richness of the data trace is still yet to be mined. In particular, very little work has attempted fine-grained content analyses of the student interactions in MOOCs. Survey research indicates the importance of student goals and intentions in keeping them involved in a MOOC over time. Automated fine-grained content analyses offer the potential to detect and monitor evidence of student engagement and how it relates to other aspects of their behavior. Ultimately these indicators reflect their commitment to remaining in the course. As a methodological contribution, in this paper we investigate using computational linguistic models to measure learner motivation and cognitive engagement from the text of forum posts. We validate our techniques using survival models that evaluate the predictive validity of these variables in connection with attrition over time. We conduct this evaluation in three MOOCs focusing on very different types of learning materials. Prior work demonstrates that participation in the discussion forums at all is a strong indicator of student commitment. Our methodology allows us to differentiate better among these students, and to identify danger signs that a struggling student is in need of support within a population whose interaction with the course offers the opportunity for effective support to be administered. Theoretical and practical implications will be discussed.


title: Deep ranking: Triplet MatchNet for music metric learning

abstract: Metric learning for music is an important problem for many music information retrieval (MIR) applications such as music generation, analysis, retrieval, classification and recommendation. Traditional music metrics are mostly defined on linear transformations of handcrafted audio features, and may be improper in many situations given the large variety of music styles and instrumentations. In this paper, we propose a deep neural network named Triplet MatchNet to learn metrics directly from raw audio signals of triplets of music excerpts with human-annotated relative similarity in a supervised fashion. It has the advantage of learning highly nonlinear feature representations and metrics in this end-to-end architecture. Experiments on a widely used music similarity measure dataset show that our method significantly outperforms three state-of-the-art music metric learning methods. Experiments also show that the learned features better preserve the partial orders of the relative similarity than handcrafted features.


title: Unsupervised Video Adaptation for Parsing Human Motion

abstract: In this paper, we propose a method to parse human motion in unconstrained Internet videos without labeling any videos for training. We use the training samples from a public image pose dataset to avoid the tediousness of labeling video streams. There are two main problems confronted. First, the distribution of images and videos are different. Second, no temporal information is available in the training images. To smooth the inconsistency between the labeled images and unlabeled videos, our algorithm iteratively incorporates the pose knowledge harvested from the testing videos into the image pose detector via an adjust-and-refine method. During this process, continuity and tracking constraints are imposed to leverage the spatio-temporal information only available in videos. For our experiments, we have collected two datasets from YouTube and experiments show that our method achieves good performance for parsing human motions. Furthermore, we found that our method achieves better performance by using unlabeled video than adding more labeled pose images into the training set.


title: Synthetic aperture radar (SAR) pattern discovery using the science of emergence

abstract: All sensors including Synthetic Aperture Radar (SAR) capture vast amounts of critical knowledge that is often contained within raw and processed sensor data, yet is undetectable, obscured or never presented. Through the application of emergence algorithms, patterns, relationships and events that are hidden or hard to find within the data are revealed in rapid to near-real time speeds. Emergence algorithms originally developed to reveal hidden diseases, are extending the capability of SAR sensors to see in effect “below” the ocean surface, revealing hidden signatures of boats, submersibles, aircraft and missiles, and often revealing things no-one expected to discover. After a brief review of SAR theory and representative image products, examples of signatures “hidden” within these SAR images will be presented.


title: Orchestrating a science ‘event’: the case of the Human Genome Project

abstract: June 2000 saw the triumphal announcement of the completion of the human genome ::: ‘working draft’. This attracted extensive, peak and vivid coverage. While several studies have ::: explored media coverage of the announcement, there has been little discussion of the production ::: process: the overall aims, values and structures which underpinned this staged event. This ::: article redresses this gap and draws on ten interviews with UK journalists and their sources to ::: show how reporting was influenced by a number of factors, including news values, ::: organisational identity, the history of reporting and editorial interest. This paper thus gives ::: valuable insights into how science stories are ‘made’ and presented in terms of the dynamics of ::: coverage. It reveals the ways in which political and economic factors may drive a science story ::: and provides crucial insights into the key relationships which influence and shape media ::: reporting of scientific research.


title: Monocyte and macrophage plasticity in tissue repair and regeneration.

abstract: Heterogeneity and high versatility are the characteristic features of the cells of monocyte-macrophage lineage. The mononuclear phagocyte system, derived from the bone marrow progenitor cells, is primarily composed of monocytes, macrophages, and dendritic cells. In regenerative tissues, a central role of monocyte-derived macrophages and paracrine factors secreted by these cells is indisputable. Macrophages are highly plastic cells. On the basis of environmental cues and molecular mediators, these cells differentiate to proinflammatory type I macrophage (M1) or anti-inflammatory or proreparative type II macrophage (M2) phenotypes and transdifferentiate into other cell types. Given a central role in tissue repair and regeneration, the review focuses on the heterogeneity of monocytes and macrophages with current known mechanisms of differentiation and plasticity, including microenvironmental cues and molecular mediators, such as noncoding RNAs.


title: Mental Health Apps: Innovations, Risks and Ethical Considerations

abstract: The purpose of this article is to briefly review some of the innovations that mobile mental health apps present to consumers and mental health practitioners. Particular attention was given to understanding some of the important risks and the potential ethical dilemmas which may arise for counselors and psychologists who embrace them in their practice. Key considerations of issues pertinent to regulations, privacy concerns, and research are being discussed.


Toward a Social Psychology of Loneliness


title: The Coach-Athlete Relationship Questionnaire (CART-Q): development and initial validation

abstract: The purpose of the present study was to develop and validate a self-report instrument that measures the nature of the coach-athlete relationship. Jowett et al.'s (Jowett & Meek, 2000; Jowett, in press) qualitative case studies and relevant literature were used to generate items for an instrument that measures affective, cognitive, and behavioral aspects of the coach-athlete relationship. Two studies were carried out in an attempt to assess content, predictive, and construct validity, as well as internal consistency, of the Coach-Athlete Relationship Questionnaire (CART-Q), using two independent British samples. Principal component analysis and confirmatory factor analysis were used to reduce the number of items, identify principal components, and confirm the latent structure of the CART-Q. Results supported the multidimensional nature of the coach-athlete relationship. The latent structure of the CART-Q was underlined by the latent variables of coaches' and athletes' Closeness (emotions), Commitment (cognitions), and Complementarity (behaviors).


Mining Bipolar Argumentation Frameworks from Natural Language Text


title: Mechanisms of Laser Hair Removal: Could Persistent Photoepilation Induce Vitiligo or Defects in Wound Repair?

abstract: BACKGROUND Current laser hair removal modalities achieve a long-term but not persistent (irreversible) hair loss. ::: ::: OBJECTIVE This review highlights the mechanisms of the current laser hair removal technology and explores possible side effects. ::: ::: METHODS The literature is reviewed. ::: ::: RESULTS The hair shaft plays a key role in the mechanisms underlying current photoepilation procedures by acting as a vector for heat transfer. Together with inherent properties of the hair growth cycle and the anatomic specifics of the follicular stem cells located in the bulge, the crucial role of the hair shaft and its lack of complete destruction with present technology are also likely culprits for the nonpersistent nature of present laser hair removal. Future persistent photoepilation may be associated with vitiligo or vitiligolike changes. Disturbances in wound repair of previously lasered sites are less likely. ::: ::: CONCLUSIONS The currently available laser hair removal protocols are safe, not the least because they achieve long-term but not persistent epilation. The adverse effects of persistent laser hair removal technology possibly available in the future are potentially problematic.


title: Cancer immunosurveillance, immunoediting and inflammation: independent or interdependent processes?

abstract: When immune cells and developing tumor cells localize to a common microenvironment, an assemblage of interactions takes place; this results in either tumor destruction by way of immunosurveillance or tumor outgrowth. These events put a functional imprint onto the emerging tumor repertoire because tumor cells arising in the presence of a fully functional immune system are less immunogenic than those that develop in the absence of immunity (i.e. in RAG2−/− and perforin−/− mice). However, other studies suggest that the immune system can also actively promote formation of certain tumors. These apparent disparate effects of immunity on tumorigenesis provide a unique model for study of the decision-making process that dictates immune function within a tumor.


title: Kinematic modeling and observer based control of soft robot using real-time Finite Element Method

abstract: This paper aims at providing a novel approach to modeling and controlling soft robots. Based on real-time Finite Element Method (FEM), we obtain a globally defined discrete-time kinematic model in the workspace of soft robots. From the kinematic equations, we deduce the soft-robot Jacobian matrix and discuss the conditions to avoid singular configurations. Then, we propose a novel observer based control methodology where the observer is built by Finite Element Model in this paper to deal with the control problem of soft robots. A closed-loop controller for position control of soft robot is designed based on the discrete-time model with feedback signal being extracted by means of visual servoing. Finally, experimental results on a parallel soft robot show the efficiency and performance of our proposed controller.


title: Accurate and Efficient Computation of the Inductance Matrix of Transformer Windings for the Simulation of Very Fast Transients

abstract: When a transformer is subjected to very fast front transients, the penetration of the magnetic flux into the core becomes, in general, negligible. To represent this phenomenon, it is common practice to compute the winding inductance matrix (turn-to-turn) at very high frequencies using the expressions for air-core inductors. However, since the core becomes a magnetic insulating wall at very high frequencies (MHz), the distribution of the magnetic field is altered because the field cannot enter the region occupied by the core. Therefore, the air-core approximation formulae overestimate the inductance at high frequencies. This is especially true in the region inside the core window. Large errors are found when the inductances are computed with the air-core approximation. This paper presents a technique, based on the application of a multilayer method of images, to take the presence of the core into consideration. The final expressions are very simple, yet they give remarkably accurate results. Comparisons with finite-element analyses prove the excellent accuracy of the technique.