title: Salient Subsequence Learning for Time Series Clustering

abstract: Time series has been a popular research topic over the past decade. Salient subsequences of time series that can benefit the learning task, e.g., classification or clustering, are called shapelets. Shapelet-based time series learning extracts these types of salient subsequences with highly informative features from a time series. Most existing methods for shapelet discovery must scan a large pool of candidate subsequences, which is a time-consuming process. A recent work, [1] , uses regression learning to discover shapelets in a time series; however, it only considers learning shapelets from labeled time series data. This paper proposes an Unsupervised Salient Subsequence Learning (USSL) model that discovers shapelets without the effort of labeling. We developed this new learning function by integrating the strengths of shapelet learning, shapelet regularization, spectral analysis and pseudo-label to simultaneously and automatically learn shapelets to help clustering unlabeled time series better. The optimization model is iteratively solved via a coordinate descent algorithm. Experiments show that our USSL can learn meaningful shapelets, with promising results on real-world and synthetic data that surpass current state-of-the-art unsupervised time series learning methods.


title: A Survey on Control of Hydraulic Robotic Manipulators With Projection to Future Trends

abstract: This paper presents the recent advancements in the control of multiple-degree-of-freedom hydraulic robotic manipulators. A literature review is performed on their control, covering both free-space and constrained motions of serial and parallel manipulators. Stability-guaranteed control system design is the primary requirement for all control systems. Thus, this paper pays special attention to such systems. An objective evaluation of the effectiveness of different methods and the state of the art in a given field is one of the cornerstones of scientific research and progress. For this purpose, the maximum position tracking error $|e|_{\rm max}$ and a performance indicator $\rho$ (the ratio of $|e|_{\rm max}$ with respect to the maximum velocity) are used to evaluate and benchmark different free-space control methods in the literature. These indicators showed that stability-guaranteed nonlinear model based control designs have resulted in the most advanced control performance. In addition to stable closed-loop control, lack of energy efficiency is another significant challenge in hydraulic robotic systems. This paper pays special attention to these challenges in hydraulic robotic systems and discusses their reciprocal contradiction. Potential solutions to improve the system energy efficiency without control performance deterioration are discussed. Finally, for hydraulic robotic systems, open problems are defined and future trends are projected.


title: Topological Centrality and Its Applications

abstract: Recent development of network structure analysis shows that it plays an important role in characterizing complex system of many branches of sciences. Different from previous network centrality measures, this paper proposes the notion of topological centrality (TC) reflecting the topological positions of nodes and edges in general networks, and proposes an approach to calculating the topological centrality. The proposed topological centrality is then used to discover communities and build the backbone network. Experiments and applications on research network show the significance of the proposed approach.


title: Manifestations of user personality in website choice and behaviour on online social networks

abstract: Individual differences in personality affect users’ online activities as much as they do in the offline world. This work, based on a sample of over a third of a million users, examines how users’ behaviour in the online environment, captured by their website choices and Facebook profile features, relates to their personality, as measured by the standard Five Factor Model personality questionnaire. Results show that there are psychologically meaningful links between users’ personalities, their website preferences and Facebook profile features. We show how website audiences differ in terms of their personality, present the relationships between personality and Facebook profile features, and show how an individual’s personality can be predicted from Facebook profile features. We conclude that predicting a user’s personality profile can be applied to personalize content, optimize search results, and improve online advertising.


title: Gesture-based interaction via finger tracking for mobile augmented reality

abstract: The goal of this research is to explore new interaction metaphors for augmented reality on mobile phones, i.e. applications where users look at the live image of the device’s video camera and 3D virtual objects enrich the scene that they see. Common interaction concepts for such applications are often limited to pure 2D pointing and clicking on the device’s touch screen. Such an interaction with virtual objects is not only restrictive but also difficult, for example, due to the small form factor. In this article, we investigate the potential of finger tracking for gesture-based interaction. We present two experiments evaluating canonical operations such as translation, rotation, and scaling of virtual objects with respect to performance (time and accuracy) and engagement (subjective user feedback). Our results indicate a high entertainment value, but low accuracy if objects are manipulated in midair, suggesting great possibilities for leisure applications but limited usage for serious tasks.


title: The Development Of A Multi-Purpose Spoken Dialogue System

abstract: In this paper we describe a multi-purpose Spoken Dialogue System platform associated with two distinct applications as an home intelligent environment and remote access to information databases. These applications differ substantially on contents and possible uses but gives us the chance to develop a platform where we were able to represent diverse services to be accessible by a spoken interface. The implemented voice input/output possibilities and the service independence level opens a wide range of possibilities for the development of new applications using the current components of our Spoken Dialogue System.


title: Corporate Governance Disclosure in Nigerian Listed Companies

abstract: Corporate Governance Disclosure (hereafter CGD) is the extent to which an organization transparently discloses its governance practices and strategies to stakeholders (UNCTAD, 2011). This paper aims to examine the impact of corporate governance disclosure on firm performance, board composition, and company size. The study used secondary data from companies listed on the Nigerian stock exchange and examined 31 companies across 5 sectors from 2010-2013. This study used panel regression techniques and the results indicate that asset turnover, board composition and number of employees are all significantly related to corporate governance disclosure. However, return on assets, return on equity and earnings per share are not significant. Overall, this study found that listed companies comply with the Securities Exchange Commission (SEC) Disclosure requirements has a positive influence on corporate governance performance for the firms listed in the Nigerian Stock Exchange. Keywords: Corporate Governance Disclosure, SEC Codes. Nigeria. https://doi.org/10.21632/irjbs.11.2.67-80


Flexibility versus efficiency? A case study of model changeovers in the Toyota production system


title: Provotypes for participatory innovation

abstract: Central to multi-stakeholder processes of participatory innovation is to generate knowledge about 'users' and to identify business opportunities accordingly. In these processes of collaborative analysis and synthesis, conflicting perceptions within and about a field of interest are likely to surface. Instead of the natural tendency to avoid these tensions, we demonstrate how tensions can be utilized by embodying them in provocative types (provotypes). Provotypes expose and embody tensions that surround a field of interest to support collaborative analysis and collaborative design explorations across stakeholders. In this paper we map how provotyping contributes to four related areas of contemporary Interaction Design practice. Through a case study that brings together stakeholders from the field of indoor climate, we provide characteristics of design provocations and design guidelines for provotypes for participatory innovation.


title: Informative art: information visualization in everyday environments

abstract: Thanks to advances in display technologies, it will soon be possible to have electronic information displays virtually everywhere. We have developed the concept of Informative Art as a way to integrate information visualization in the everyday human environment. Informative Art combines a dynamically updated information display with the decorative role of visual art, such as posters and paintings. We present four examples of Informative Art, where we borrowed the styles of various modern artists to show different kinds of information. For instance, a composition similar to the style of an abstract painter, Piet Mondriaan, showed the current weather in six different cities, while a piece of "landscape art" in the style of Richard Long gave a view of the last 30 days of global earthquake activity. We discuss how designing information visualizations for everyday environment introduces requirement that are different from those of graphical user interfaces for desktop computers.


title: Variable-On-Time-Controlled Critical-Conduction-Mode Flyback PFC Converter

abstract: A variable on-time (VOT) control strategy for a critical conduction mode (CRM) flyback power factor correction (PFC) converter with low total harmonic distortion (THD) and high power factor (PF) is proposed in this paper. By using input voltage and the voltage across the auxiliary winding of the flyback transformer to modulate the turn-on time of the switch of the CRM flyback PFC converter, the PF and THD of the converter can be improved. The operation principles of the traditional constant on-time (COT)-controlled CRM flyback PFC converter and VOT-controlled CRM flyback PFC converter are analyzed. The experimental results show that the PF and THD of the VOT-controlled CRM flyback PFC converter are better than those of the COT-controlled CRM flyback PFC converter.


title: Recognizing Image Style

abstract: The style of an image plays a significant role in how it is viewed, but style has received little attention in computer vision research. We describe an approach to predicting style of images, and perform a thorough evaluation of different image features for these tasks. We find that features learned in a multi-layer network generally perform best -- even when trained with object class (not style) labels. Our large-scale learning methods results in the best published performance on an existing dataset of aesthetic ratings and photographic style annotations. We present two novel datasets: 80K Flickr photographs annotated with 20 curated style labels, and 85K paintings annotated with 25 style/genre labels. Our approach shows excellent classification performance on both datasets. We use the learned classifiers to extend traditional tag-based image search to consider stylistic constraints, and demonstrate cross-dataset understanding of style.


Simfinder: A flexible clustering tool for summarization


title: A Simple Algorithm for Identifying Abbreviation Definitions in Biomedical Text

abstract: The volume of biomedical text is growing at a fast rate, creating challenges for humans and computer systems alike. One of these challenges arises from the frequent use of novel abbreviations in these texts, thus requiring that biomedical lexical ontologies be continually updated. In this paper we show that the problem of identifying abbreviations’ definitions can be solved with a much simpler algorithm than that proposed by other research efforts. The algorithm achieves 96% precision and 82% recall on a standard test collection, which is at least as good as existing approaches. It also achieves 95% precision and 82% recall on another, larger test set. A notable advantage of the algorithm is that, unlike other approaches, it does not require any training data.


title: Evaluation Measures for Text Summarization

abstract: We explain the ideas of automatic text summarization approaches and the taxonomy of summary evaluation methods. Moreover, we propose a new evaluation measure for assessing the quality of a summary. The core of the measure is covered by Latent Semantic Analysis (LSA) which can capture the main topics of a document. The summarization systems are ranked according to the similarity of the main topics of their summaries and their reference documents. Results show a high correlation between human rankings and the LSA-based evaluation measure. The measure is designed to compare a summary with its full text. It can compare a summary with a human written abstract as well; however, in this case using a standard ROUGE measure gives more precise results. Nevertheless, if abstracts are not available for a given corpus, using the LSA-based measure is an appropriate choice.


title: B1 destructive interferences and spatial phase patterns at 7 T with a head transceiver array coil

abstract: RF behavior in the human head becomes complex at ultrahigh magnetic fields. A bright center and a weak periphery are observed in images obtained with volume coils, while surface coils provide strong signal in the periphery. Intensity patterns reported with volume coils are often loosely referred to as “dielectric resonances,” while modeling studies ascribe them to superposition of traveling waves greatly dampened in lossy brain tissues, raising questions regarding the usage of this term. Here we address this question experimentally, taking full advantage of a transceiver coil array that was used in volume transmit mode, multiple receiver mode, or single transmit surface coil mode. We demonstrate with an appropriately conductive sphere phantom that destructive interferences are responsible for a weak B1 in the periphery, without a significant standing wave pattern. The relative spatial phase of receive and transmit B1 proved remarkably similar for the different coil elements, although with opposite rotational direction. Additional simulation data closely matched our phantom results. In the human brain the phase patterns were more complex but still exhibited similarities between coil elements. Our results suggest that measuring spatial B1 phase could help, within an MR session, to perform RF shimming in order to obtain more homogeneous B1 in user-defined areas of the brain. Magn Reson Med 54:1503–1518, 2005. © 2005 Wiley-Liss, Inc.


title: Scalable algorithms for clustering large datasets with mixed type attributes

abstract: Clustering is a widely used technique in data mining applications for discovering patterns in underlying data. Most traditional clustering algorithms are limited to handling datasets that contain either numeric or categorical attributes. However, datasets with mixed types of attributes are common in real life data mining applications. In this article, we present two algorithms that extend the Squeezer algorithm to domains with mixed numeric and categorical attributes. The performance of the two algorithms has been studied on real and artificially generated datasets. Comparisons with other clustering algorithms illustrate the superiority of our approaches. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1077–1089, 2005.


title: Abstract Efficient market hypothesis and forecasting

abstract: The efficient market hypothesis gives rise to forecasting tests that mirror those adopted when testing the optimality of a forecast in the context of a given information set. However, there are also important differences arising from the fact that market efficiency tests rely on establishing profitable trading opportunities in ‘real time’. Forecasters constantly search for predictable patterns and affect prices when they attempt to exploit trading opportunities. Stable forecasting patterns are therefore unlikely to persist for long periods of time and will self-destruct when discovered by a large number of investors. This gives rise to nonstationarities in the time series of financial returns and complicates both formal tests of market efficiency and the search for successful forecasting approaches.


title: Improved non-isolated high voltage gain boost converter using coupled inductors

abstract: In some cases of grid connected system using photovoltaic modules, high voltage gain is required to match the grid voltage. In this paper, non-isolated high gain boost converter with coupled inductors is proposed. The proposed converter consists of two-stage boost converter which has each inductor coupled and double/enhance circuit (DEC). This topology can reduce inductor current of each stage and diminish in inductor size so it is suitable for MIC(Module Integrated Converter). This coupled inductor method is verified through the theoretical method and designed from this equivalent circuit method that is finding the optimal coupling coefficient k. Also the operational characteristic of this proposed topology was verified through PSIM program and experimental waveform.


title: Controlled Positioning of Cells in Biomaterials—Approaches Towards 3D Tissue Printing

abstract: Current tissue engineering techniques have various drawbacks: they often incorporate uncontrolled and imprecise scaffold geometries, whereas the current conventional cell seeding techniques result mostly in random cell placement rather than uniform cell distribution. For the successful reconstruction of deficient tissue, new material engineering approaches have to be considered to overcome current limitations. An emerging method to produce complex biological products including cells or extracellular matrices in a controlled manner is a process called bioprinting or biofabrication, which effectively uses principles of rapid prototyping combined with cell-loaded biomaterials, typically hydrogels. 3D tissue printing is an approach to manufacture functional tissue layer-by-layer that could be transplanted in vivo after production. This method is especially advantageous for stem cells since a controlled environment can be created to influence cell growth and differentiation. Using printed tissue for biotechnological and pharmacological needs like in vitro drug-testing may lead to a revolution in the pharmaceutical industry since animal models could be partially replaced by biofabricated tissues mimicking human physiology and pathology. This would not only be a major advancement concerning rising ethical issues but would also have a measureable impact on economical aspects in this industry of today, where animal studies are very labor-intensive and therefore costly. In this review, current controlled material and cell positioning techniques are introduced highlighting approaches towards 3D tissue printing.


Visual SLAM Tutorial : Bundle Adjustment


title: The “organic ” path to obesity? Organic claims influence calorie judgments and exercise recommendations

abstract: Labeling a food as "organic" entails a claim about its production but is silent on its calorie content. Nevertheless, people infer that organic cookies are lower in calories and can be eaten more often than conventional cookies (Study 1). These inferences are observed even when the nutrition label conveys identical calorie content and are more pronounced among perceivers high on pro-environmentalism. Moreover, when evaluating a person with a weight-loss goal, forgoing exercise is deemed more acceptable when the person has just chosen organic rather than conventional dessert (Study 2). These results reflect an "organic/natural"-"healthy" association that is capable of biasing everyday judgments about diet and exercise.


Demonstration of the Nonfeasibility of Fully Automatic High Quality Translation


title: Summarizing Answers in Non-Factoid Community Question-Answering

abstract: We aim at summarizing answers in community question-answering (CQA). While most previous work focuses on factoid question-answering, we focus on the non-factoid question-answering. Unlike factoid CQA, non-factoid question-answering usually requires passages as answers. The shortness, sparsity and diversity of answers form interesting challenges for summarization. To tackle these challenges, we propose a sparse coding-based summarization strategy that includes three core ingredients: short document expansion, sentence vectorization, and a sparse-coding optimization framework. Specifically, we extend each answer in a question-answering thread to a more comprehensive representation via entity linking and sentence ranking strategies. From answers extended in this manner, each sentence is represented as a feature vector trained from a short text convolutional neural network model. We then use these sentence representations to estimate the saliency of candidate sentences via a sparse-coding framework that jointly considers candidate sentences and Wikipedia sentences as reconstruction items. Given the saliency vectors for all candidate sentences, we extract sentences to generate an answer summary based on a maximal marginal relevance algorithm. Experimental results on a benchmark data collection confirm the effectiveness of our proposed method in answer summarization of non-factoid CQA, and moreover, its significant improvement compared to state-of-the-art baselines in terms of ROUGE metrics.


title: Design and development of a tilt-wing UAV

abstract: In this paper, the mechanical and aerodynamic design, carbon composite production, hierarchical control system design and vertical flight tests of a new unmanned aerial vehicle (UAV), which is capable of VTOL (vertical takeoff and landing) like a helicopter and long range horizontal flight like an airplane, are presented. Real flight tests show that the aerial vehicle can successfully operate in VTOL mode. Kalman filtering is employed to obtain accurate roll and pitch angle estimations.


title: Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey

abstract: The rapid development of digital imaging and computer vision has increased the potential of using the image processing technologies in ophthalmology. Image processing systems are used in standard clinical practices with the development of medical diagnostic systems. The retinal images provide vital information about the health of the sensory part of the visual system. Retinal diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, Stargardt’s disease, and retinopathy of prematurity, can lead to blindness manifest as artifacts in the retinal image. An automated system can be used for offering standardized large-scale screening at a lower cost, which may reduce human errors, provide services to remote areas, as well as free from observer bias and fatigue. Treatment for retinal diseases is available; the challenge lies in finding a cost-effective approach with high sensitivity and specificity that can be applied to large populations in a timely manner to identify those who are at risk at the early stages of the disease. The progress of the glaucoma disease is very often quiet in the early stages. The number of people affected has been increasing and patients are seldom aware of the disease, which can cause delay in the treatment. A review of how computer-aided approaches may be applied in the diagnosis and staging of glaucoma is discussed here. The current status of the computer technology is reviewed, covering localization and segmentation of the optic nerve head, pixel level glaucomatic changes, diagonosis using 3-D data sets, and artificial neural networks for detecting the progression of the glaucoma disease.


title: Efficient algorithms for mining outliers from large data sets

abstract: In this paper, we propose a novel formulation for distance-based outliers that is based on the distance of a point from its kth nearest neighbor. We rank each point on the basis of its distance to its kth nearest neighbor and declare the top n points in this ranking to be outliers. In addition to developing relatively straightforward solutions to finding such outliers based on the classical nested-loop join and index join algorithms, we develop a highly efficient partition-based algorithm for mining outliers. This algorithm first partitions the input data set into disjoint subsets, and then prunes entire partitions as soon as it is determined that they cannot contain outliers. This results in substantial savings in computation. We present the results of an extensive experimental study on real-life and synthetic data sets. The results from a real-life NBA database highlight and reveal several expected and unexpected aspects of the database. The results from a study on synthetic data sets demonstrate that the partition-based algorithm scales well with respect to both data set size and data set dimensionality.


An English Grammar Checker As A Writing Aid For Students Of English As A Second Language


title: Capacity and power allocation for fading MIMO channels with channel estimation error

abstract: In this correspondence, we investigate the effect of channel estimation error on the capacity of multiple-input-multiple-output (MIMO) fading channels. We study lower and upper bounds of mutual information under channel estimation error, and show that the two bounds are tight for Gaussian inputs. Assuming Gaussian inputs we also derive tight lower bounds of ergodic and outage capacities and optimal transmitter power allocation strategies that achieve the bounds under perfect feedback. For the ergodic capacity, the optimal strategy is a modified waterfilling over the spatial (antenna) and temporal (fading) domains. This strategy is close to optimum under small feedback delays, but when the delay is large, equal powers should be allocated across spatial dimensions. For the outage capacity, the optimal scheme is a spatial waterfilling and temporal truncated channel inversion. Numerical results show that some capacity gain is obtained by spatial power allocation. Temporal power adaptation, on the other hand, gives negligible gain in terms of ergodic capacity, but greatly enhances outage performance.


title: Utilizing Wikipedia knowledge in open directory project-based text classification

abstract: Traditional Open Directory Project (ODP)-based text classification methods use bag-of-words approach, which only utilizes single words in ODP documents and ignores important types of semantic information such as phrases and related terms. In this paper, we propose a method for enriching the semantic information in ODP documents by utilizing Wikipedia knowledge. First, we construct a phrase dictionary based on Wikipedia and search for Wikipedia phrases in ODP documents. Second, we select the most likely relevant Wikipedia articles and relevant hyperlinks for Wikipedia phrases in ODP documents. Finally, we add Wikipedia phrases and relevant hyperlinks to ODP documents to enrich the semantic information. Our evaluation results verify the efficacy of the proposed methodology.


title: Safe kernel extensions without run-time checking

abstract: Abstract : This paper describes a mechanism by which an operating system kernel can determine with certainty that it is safe to execute a binary supplied by an untrusted source. The kernel first defines a safety policy and makes it public. Then, using this policy, an application can provide binaries in a special form called proof-carrying code, or simply PCC. Each PCC binary contains, in addition to the native code, a formal proof that the code obeys the safety policy. The kernel can easily validate the proof without using cryptography and without consulting any external trusted entities. If the validation succeeds, the code is guaranteed to respect the safety policy without relying on run-time checks. The main practical difficulty of PCC is in generating the safety proofs. In order to gain some preliminary experience with this, we have written several network packet filters in hand-tuned DEC Alpha assembly language, and then generated PCC binaries for them using a special prototype assembler. The PCC binaries can be executed with no run-time over-head, beyond a one-time cost of 1 to 3 milliseconds for validating the enclosed proofs. The net result is that our packet filters are formally guaranteed to be safe and are faster than packet filters created using Berkeley Packet Filters, Software Fault Isolation, or safe languages such as Modula-3.


title: BitIodine: Extracting Intelligence from the Bitcoin Network

abstract: Bitcoin, the famous peer-to-peer, decentralized electronic currency system, allows users to benefit from pseudonymity, by generating an arbitrary number of aliases (or addresses) to move funds. However, the complete history of all transactions ever performed, called “blockchain”, is public and replicated on each node. The data it contains is difficult to analyze manually, but can yield a high number of relevant information. In this paper we present a modular framework, BitIodine, which parses the blockchain, clusters addresses that are likely to belong to a same user or group of users, classifies such users and labels them, and finally visualizes complex information extracted from the Bitcoin network. BitIodine labels users semi-automatically with information on their identity and actions which is automatically scraped from openly available information sources. BitIodine also supports manual investigation by finding paths and reverse paths between addresses or users. We tested BitIodine on several real-world use cases, identified an address likely to belong to the encrypted Silk Road cold wallet, or investigated the CryptoLocker ransomware and accurately quantified the number of ransoms paid, as well as information about the victims. We release a prototype of BitIodine as a library for building Bitcoin forensic analysis tools.


title: Matching with PROSAC - progressive sample consensus

abstract: A new robust matching method is proposed. The progressive sample consensus (PROSAC) algorithm exploits the linear ordering defined on the set of correspondences by a similarity function used in establishing tentative correspondences. Unlike RANSAC, which treats all correspondences equally and draws random samples uniformly from the full set, PROSAC samples are drawn from progressively larger sets of top-ranked correspondences. Under the mild assumption that the similarity measure predicts correctness of a match better than random guessing, we show that PROSAC achieves large computational savings. Experiments demonstrate it is often significantly faster (up to more than hundred times) than RANSAC. For the derived size of the sampled set of correspondences as a function of the number of samples already drawn, PROSAC converges towards RANSAC in the worst case. The power of the method is demonstrated on wide-baseline matching problems.


Techniques Trie Memory


title: Person Search in Videos with One Portrait Through Visual and Temporal Links

abstract: In real-world applications, e.g. law enforcement and video retrieval, one often needs to search a certain person in long videos with just one portrait. This is much more challenging than the conventional settings for person re-identification, as the search may need to be carried out in the environments different from where the portrait was taken. In this paper, we aim to tackle this challenge and propose a novel framework, which takes into account the identity invariance along a tracklet, thus allowing person identities to be propagated via both the visual and the temporal links. We also develop a novel scheme called Progressive Propagation via Competitive Consensus, which significantly improves the reliability of the propagation process. To promote the study of person search, we construct a large-scale benchmark, which contains 127K manually annotated tracklets from 192 movies. Experiments show that our approach remarkably outperforms mainstream person re-id methods, raising the mAP from 42.16% to 62.27%.


Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories


title: Development of hair-washing robot equipped with scrubbing fingers

abstract: We have developed the world's first hair-washing robot equipped with scrubbing fingers. This robot assists in washing hair in hospitals or care facilities, and it eases the burden of healthcare professionals and care workers while it brings a higher quality of life to patients and others in need of nursing care. It features the mechanical and control technologies needed for touching someone gently on the head. These include, for instance, the self-aligning mechanism and the cylindrical rack mechanism of the robot's end effector, the parallel link mechanism of its pressing arm, and the compliance control for providing an appropriate sense of pressure.


title: Design of a GaN-Based, Inductor-less, Wireless Power Transfer System at 40.68 MHz

abstract: Wireless power transfer systems have attracted tremendous attention over the past decade. The design of most small size, low power, WPT systems; however, utilizes ferrite to shield and redirect the magnetic fields. This add to the weight and bulk of the system. In this paper, a small and extremely lightweight, PCB based, proof-of-concept WPT system is demonstrated. The circuit operates at 40.68 MHz and is capable of 20 W of output power. It utilizes no discrete inductors other than the WPT coils themselves and weights only 7 grams. The following paper shows the design methodology and the inductor elimination technique used to achieve such design.


title: Analytical and Simulation study for LoRa Modulation

abstract: In this paper, the performance of Long Range (LoRa) Internet of Things (IoT) technology is investigated. By considering Chirp Spread Spectrum (CSS) technique of LoRa, an approximation of the Bit Error Rate (BER) is presented and evaluated through intensive simulations. Unlike previous works which present the BER of LoRa in terms of the ratio of energy ber bit to noise ratio only without any proofing, our presented work expresses BER in terms of LoRa's modulation patterns such as the spreading factor, the code rate, the symbol frequency and the SNR. Numerical results are carried out in order to investigate the LoRa performance and to illustrate the accuracy of the new BER expression.


title: Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning

abstract: The mutual information is a core statistical quantity that has applications in all areas of machine learning, whether this is in training of density models over multiple data modalities, in maximising the efficiency of noisy transmission channels, or when learning behaviour policies for exploration by artificial agents. Most learning algorithms that involve optimisation of the mutual information rely on the Blahut-Arimoto algorithm --- an enumerative algorithm with exponential complexity that is not suitable for modern machine learning applications. This paper provides a new approach for scalable optimisation of the mutual information by merging techniques from variational inference and deep learning. We develop our approach by focusing on the problem of intrinsically-motivated learning, where the mutual information forms the definition of a well-known internal drive known as empowerment. Using a variational lower bound on the mutual information, combined with convolutional networks for handling visual input streams, we develop a stochastic optimisation algorithm that allows for scalable information maximisation and empowerment-based reasoning directly from pixels to actions.


title: UPTIME: Ubiquitous pedestrian tracking using mobile phones

abstract: The mission of tracking a pedestrian is valuable for many applications including walking distance estimation for the purpose of pervasive healthcare, museum and shopping mall guides, and locating emergency responders. In this paper, we show how accurate and ubiquitous tracking of a pedestrian can be performed using only the inertial sensors embedded in his/her mobile phone. Our work depends on performing dead reckoning to track the user's movement. The main challenge that needs to be addressed is handling the noise of the low cost low quality inertial sensors in cell phones. Our proposed system combines two novel contributions: a novel step count estimation technique and a gait-based accurate variable step size detection algorithm. The step count estimation technique is based on a lightweight finite state machine approach that leverages orientation-independent features. In order to capture the varying stride length of the user, based on his changing gait, we employ a multi-class hierarchical Support Vector Machine classifier. Combining the estimated number of steps with the an accurate estimate of the individual stride length, we achieve ubiquitous and accurate tracking of a person in indoor environments. We implement our system on different Android-based phones and compare it to the state-of-the-art techniques in indoor and outdoor testbeds with arbitrary phone orientation. Our results in two different testbeds show that we can provide an accurate step count estimation with an error of 5.72%. In addition, our gait type classifier has an accuracy of 97.74%. This leads to a combined tracking error of 6.9% while depending only on the inertial sensors and turning off the GPS sensor completely. This highlights the ability of the system to provide ubiquitous, accurate, and energy efficient tracking.


title: An Empirical Analysis of Linkability in the Monero Blockchain

abstract: Monero is a privacy-centric cryptocurrency that allows users to obscure their transaction graph by including chaff coins, called"mixins,"along with the actual coins they spend. In this report, we empirically evaluate two weak- nesses in Monero's mixin sampling strategy. First, about 62% of transaction inputs with one or more mixins are vulnerable to"chain-reaction"analysis -- that is, the real input can be deduced by elimination, e.g. because the mixins they include are spent by 0-mixin transactions. Sec- ond, Monero mixins are sampled in such a way that the mixins can be easily distinguished from the real coins by their age distribution; in short, the real input is usually the"newest"input. We estimate that this heuristic can be used to guess the real input with 80% accuracy over all transactions with 1 or more mixins. Our analysis uses only public blockchain data, in contrast to earlier attacks requiring active participation in the network. While the first weakness primarily affects Monero transactions made by older software versions (i.e., prior to RingCT), the second weakness is applicable to the newest versions as well. We propose and evaluate a countermeasure de- rived from blockchain data that can improve the privacy of future transactions.


title: Development of 1-MW Inductive Power Transfer System for a High-Speed Train

abstract: Design and fabrication of a 1-MW inductive power transfer (IPT) system that supplies power to the vehicle in real time without any battery charge is proposed for a high-speed train. The IPT system consists of a 1-MW resonant inverter, a 128-m transmitter, four pickups, including rectifiers, and a wireless feedback network to maintain a constant output voltage of the pickups. The operating frequency of the system is 60 kHz to achieve efficient power transfer with a large air gap. The measured efficiency of the IPT system at the 818-kW output power of the pickups for the 5-cm air gap is 82.7%. The electromagnetic field and the induced voltage at the rail are also measured for safety evaluation. The fabricated IPT system was adapted to the high-speed train, and the train successfully accelerates to a speed of 10 km/h according to startup procedures.


title: An Energy Optimization Model (EOM) to reduce mobile service providers network costs: A multi-objective optimization approach

abstract: This paper presents an Energy Optimization Model (EOM) for Mobile Service Providers (MSP) that enables the optimization of power efficiency and the integration of optimum renewable and clean energy sources into the mobile network. The model approach drives both Operational Expenditure (OPEX) and Capital Expenditure (CAPEX) reduction via re-engineering power provisioning and site solutions. The features for the selection of Base Transceiver Station (BTS) type based on low power consumption are discussed within the context of establishing a direct impact on the site construction, power source type and dimensioning, and hence, the network cost structure. The K-mean clustering algorithm is used to cluster sites based on those features. HOMER® software was used to optimize the solution within each cluster of sites. This follows a multi-objective optimization function with power saving and CO 2 emission as the dominant target factors and the cost, OPEX, Operation & Maintenance (O&M) as constraints. We focus on the lowest power optimization in this paper. Network energy optimization for between clusters (intra-cluster) is performed. Both Traffic and power profile data from Zain-Sudan MSP during the two month period from May-to-July 2013, is obtained and used as input to the EOM model. The optimum parameters for the set of solutions are then determined for deployment under budget & cost constraints. Renewable energy power generation profile for solar and wind from Laqawa site in the South-West of Sudan is used. Results indicate the effectiveness of the EOM model in finding optimum solutions per cluster of sites while facilitating for multi-objective optimization formulation across geographical regions and site types.


title: Ensemble learning based urban traffic state prediction for coupling traffic network with large scale data

abstract: Current urban transportation system applications often deal with the scenarios of large scale multiple-layer road networks, with the requirement of qualified overall prediction accuracy. However, the common related studies mostly focus on the prediction of individual road segments or intersections. In this paper, we propose a generic method for large scale coupling urban traffic to forecast the future states of the overall network. In the preliminary stage, the features are extracted using unsupervised method. Subsequently, an ensemble learning via matching the historical data and the current information are implemented to predict the traffic state transition. The data set for experiment is composed of multiple layers of road network in Shanghai, which are collected by loop detectors every two minutes. We verify the effectiveness of the developed method by evaluating the experimental prediction performance using the actual data. The results also indicate that adding the interactive features of the coupling road network to the forecasting algorithm can generate superior prediction accuracy comparing with the independent prediction method.


title: Detecting Opinions Using Deep Syntactic Analysis

abstract: In this paper, we present an opinion detection system built on top of a robust syntactic pars-er. The goal of this system is to extract opi-nions associated with products but also with characteristics of these products, i.e. to per-form feature-based opinion extraction. To car-ry out this task, and following a rget corpus tastudy, the robust syntactic parser is enriched by associa ting polarities to pertinent lexical elements and by developing generic rules to extract relations of opinions t ogether with their polarity, i.e. positive or negative. These rela-tions are used to feed an opinion representa-tion model. A first evaluation shows very en-couraging results, but numerous perspectives and developments remain to be inve stigated.


title: Application of Rogowski Coils used for Protective Relaying Purposes

abstract: This paper describes current technology and application for Rogowski coils, which serve in the role of current transformers for measuring power system currents for protective relaying or other measurement purposes. Rogowski coils have low-energy outputs. They offer a number of application advantages including compact size, light weight, and linearity (do not saturate like conventional iron-core current transformers). A forthcoming Guide PC37.235 from the IEEE PSRC will give detailed information and help for users


title: Gender identification using a general audio classifier

abstract: In the context of content-based multimedia indexing gender identification using speech signal is an important task. Existing techniques are dependent on the quality of the speech signal making them unsuitable for the video indexing problems. In this paper we introduce a novel gender identification approach based on a general audio classifier. The audio classifier models the audio signal by the first order spectrum's statistics in 1s windows and uses a set of neural networks as classifiers. The presented technique shows robustness to adverse audio compression and it is language independent. We show how practical considerations about the speech in audio-visual data, such as the continuity of speech, can further improve the classification results which attain 92%.


title: Six-Phase Fractional-Slot-per-Pole-per-Phase Permanent-Magnet Machines With Low Space Harmonics for Electric Vehicle Application

abstract: This paper discusses the development of new winding configuration for six-phase permanent-magnet (PM) machines with 18 slots and 8 poles, which eliminates and/or reduces undesirable space harmonics in the stator magnetomotive force. The proposed configuration improves power/torque density and efficiency with a reduction in eddy-current losses in the rotor permanent magnets and copper losses in end windings. To improve drive train availability for applications in electric vehicles (EVs), this paper proposes the design of a six-phase PM machine as two independent three-phase windings. A number of possible phase shifts between two sets of three-phase windings due to their slot-pole combination and winding configuration are investigated, and the optimum phase shift is selected by analyzing the harmonic distributions and their effect on machine performance, including the rotor eddy-current losses. The machine design is optimized for a given set of specifications for EVs, under electrical, thermal and volumetric constraints, and demonstrated by the experimental measurements on a prototype machine.


title: Soft Actuator Mimicking Human Esophageal Peristalsis for a Swallowing Robot

abstract: Provision of modified foods and drinks is one of the approaches for dysphagia management, which is based on the assumption that food with proper texture and rheological properties will allow dysphagia patients to swallow safely and maintain adequate nutrition. However, lack of information about the in vivo swallowing process and its interaction with food flow has obstructed the effective management of dysphagia. In the esophageal swallowing stage, masticated food is transported through the esophagus to the stomach by a peristaltic mechanism, which is generated by sequential contraction and relaxation of esophageal muscles. Inspired by this behavior, a soft actuator is proposed to provide a nonrisk environment aiming to facilitate investigations of the most effective properties of food for the management of the swallowing disorders. The wave-like motion is first specified according to the in vivo measurement of human esophageal peristalsis. Finite-element analysis simulations are carried out to aid the structure design before prototype manufacture. Constructed by casting silicon rubber in a three-dimensional (3-D) printed customized mold, the novel actuator has soft structure resembling its human counterpart, which has a flexible muscular structure. Multiple layers of inflatable chambers are embedded and distributed along the axis of a food passage regularly, which locates at the center of the actuator. The actuator is capable of generating a peristaltic wave and pushing a bolus along the passage. The closure of the tube and the velocity of the propagation wave are going to be adjusted to achieve the trajectories recorded experimentally, by regulating the compressed air pressure pumped into chambers actively.


title: A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification

abstract: In this study, a multi-task deep neural network is proposed for skin lesion analysis. The proposed multi-task learning model solves different tasks (e.g., lesion segmentation and two independent binary lesion classifications) at the same time by exploiting commonalities and differences across tasks. This results in improved learning efficiency and potential prediction accuracy for the task-specific models, when compared to training the individual models separately. The proposed multi-task deep learning model is trained and evaluated on the dermoscopic image sets from the International Skin Imaging Collaboration (ISIC) 2017 Challenge - Skin Lesion Analysis towards Melanoma Detection, which consists of 2000 training samples and 150 evaluation samples. The experimental results show that the proposed multi-task deep learning model achieves promising performances on skin lesion segmentation and classification. The average value of Jaccard index for lesion segmentation is 0.724, while the average values of area under the receiver operating characteristic curve (AUC) on two individual lesion classifications are 0.880 and 0.972, respectively.


title: Smart home automation system using Bluetooth technology

abstract: In this paper a low cost and user friendly remote controlled home automation system is presented using Arduino board, Bluetooth module, smartphone, ultrasonic sensor and moisture sensor. A smartphone application is used in the suggested system which allows the users to control up to 18 devices including home appliances and sensors using Bluetooth technology. Nowadays, most of conventional home automation systems are designed for special purposes while proposed system is a general purpose home automation system. Which can easily be implement in existing home. The suggested system has more features than conventional home automation systems such as an ultrasonic sensor is used for water level detection and soil moisture sensor is use for automatic plant irrigation system. This paper also describes the hardware and software architecture of system, future work and scope. The proposed prototype of home automation system is implemented and tested on hardware and it gave the exact and expected results.


Realization with CCG


title: Physicochemical and Microbiological Properties of Synbiotic Yogurt Made with Probiotic Yeast Saccharomyces boulardii in Combination with Inulin

abstract: Saccharomyces boulardii is a unique species of yeast previously characterized as a probiotic strain (CNCM I-745) among a few probiotic yeasts reported to date. Inulin is one of the most common prebiotics that exhibit twisted hydrocolloidal properties in dairy products. The present study was designed to develop a synbiotic yogurt by incorporation of S. boulardii and inulin at 1%, 1.5%, and 2% (w/v), comparing with the probiotic and control plain yogurts. Microrheological, microstructural, microbiological, sensory properties, and volatile compounds of the yogurt samples were evaluated. Microrheological analysis showed that addition of inulin to yogurt slightly reduced the values of G′ and G″, while solid–liquid balance (SLB) values confirmed more solid properties of the synbiotic yogurt (0.582~0.595) than the plain yogurt (0.503~0.518). A total of 18 volatile compounds were identified in the synbiotic yogurt, while only five and six compounds were identified in plain and probiotic yogurts, respectively. Physiochemical parameters such as pH, acidity, and protein content were in the normal range (as with the control), while fat content in the synbiotic yogurt decreased significantly. Addition of 1% inulin not only reduced syneresis but also maintained viability of S. boulardii after 28 days of storage. Microstructural and microrheological studies confirmed the dense, compressed, homogeneous structure of the synbiotic yogurt. Thus, addition of inulin improved the textural and sensory properties of the synbiotic yogurt, as well as survival of S. boulardii with viable count above 6.0 log CFU/g in yogurt, as generally required for probiotics. Therefore, novel synbiotic yogurt with desirable quality was developed as an effective carrier for delivery of the probiotic yeast exerting its beneficial health effects.


title: An LTCC Coupled Resonator Decoupling Network for Two Antennas

abstract: An integrated low-temperature co-fired ceramic (LTCC) coupled resonator decoupling network (CRDN) for two coupled antennas is proposed in this paper. By virtue of LTCC technology, a second-order CRDN is realized by two tightly coupled lumped-element resonators in a volume of ${\hbox{3.2}}\times {\hbox{2.5}}\times {\hbox{1.2}}\ {\hbox{mm}}^{3}$ . Theoretical analysis has revealed that a wide range of mutual admittance of a CRDN can be realized by choosing appropriate I/O coupling elements while the coupled resonators can be consolidated in an LTCC device. This “one-fit-all” feature allows for decoupling a wide range of coupled antennas with different form factors for a given frequency band. To prove the concept, two LTCC CRDNs for decoupling different forms of antennas working in the 2.45-GHz band are designed, fabricated, and tested. Measurement results have demonstrated that significant improvement in isolation can be achieved within a wide operating frequency range in all cases. The presented design theory and circuit topology are general and can be applied to both symmetric and asymmetric two-element antenna arrays.


title: Quantized Phonon Spectrum of Single-Wall Carbon Nanotubes

abstract: The electronic spectra of carbon nanotubes and other nanoscale systems are quantized because of their small radii. Similar quantization in the phonon spectra has been difficult to observe because of the far smaller energy scale. We probed this regime by measuring the temperature-dependent specific heat of purified single-wall nanotubes. The data show direct evidence of one-dimensional quantized phonon subbands. Above 4 kelvin, they are in excellent agreement with model calculations of individual nanotubes and differ markedly from the specific heat of two-dimensional graphene or three-dimensional graphite. Detailed modeling yields an energy of 4.3 millielectron volts for the lowest quantized phonon subband and a tube-tube (or “lattice”) Debye energy of 1.1 millielectron volts, implying a small intertube coupling in bundles.


title: In search of memory: the emergence of a new science of mind

abstract: Memory binds our mental life together. We are who we are in large part because of what we learn and remember. But how does the brain create memories? Nobel Prize winner Eric R. Kandel intertwines the intellectual history of the powerful new science of the mind-a combination of cognitive psychology, neuroscience, and molecular biology-with his own personal quest to understand memory. A deft mixture of memoir and history, modern biology and behavior, In Search of Memory brings readers from Kandel's childhood in Nazi-occupied Vienna to the forefront of one of the great scientific endeavors of the twentieth century: the search for the biological basis of memory.


title: Automated enhancement and detection of stripe defects in large circular weft knitted fabrics

abstract: Stripes are periodic defects that are difficult to detect during production even by experienced human inspectors. Therefore, we introduce an image processing method for automatically detecting stripe defects in circularly knitted fabric. We show how a barely visible defect can be optically enhanced to improve manual assessment as well as how descriptor-based image processing and machine learning can be used to allow automated stripe detection. Image enhancement is performed by applying gabor and matched filters to histogram-equalized fabric images. Subsequently, we extract image information with different descriptors (LBP, GLCM, HOG) and feed these into random forest and SVM classifiers. The full pipeline is validated by training and testing it on three sets of fabric produced with different knitting machines and parameter settings. Results show that the proposed enhancement combined with a statistics-based descriptor such as GLCM or HOG allows to train both tested classifiers with good classification rates of up to 98.9%.


title: Domain analysis of 3 Keto Acyl-CoA synthase for structural variations in Vitis vinifera and Oryza brachyantha using comparative modelling

abstract: The long chain fatty acids incorporated into plant lipids are derived from the iterative addition of C2 units which is provided by malonyl-CoA to an acyl-CoA after interactions with 3-ketoacyl-CoA synthase (KCS), found in several plants. This study provides functional characterization of three 3 ketoacyl CoA synthase like proteins in Vitis vinifera (one) and Oryza brachyantha (two proteins). Sequence analysis reveals that protein of Oryza brachyantha shows 96% similarity to a hypothetical protein in Sorghum bicolor; total 11 homologs were predicted in Sorghum bicolor. Conserved domain prediction confirm the presence of FAE1/Type III polyketide synthase-like protein, Thiolase-like, subgroup; Thiolase-like and 3-Oxoacyl-ACP synthase III, C-terminal and chalcone synthase like domain but very long chain 3-keto acyl CoA domain is absent. All three proteins were found to have Chalcone and stilbene synthases C terminal domain which is similar to domain of thiolase and β keto acyl synthase. Its N terminal domain is absent in J3M9Z7 protein of Oryza brachyantha and F6HH63 protein of Vitis vinifera. Differences in N-terminal domain is responsible for distinguish activity. The J3MF16 protein of Oryza brachyantha contains N terminal domain and C terminal domain and characterized using annotation of these domains. Domains Gcs (streptomyces coelicolor) and Chalcone-stilbene synthases (KAS) in 2-pyrone synthase (Gerbera hybrid) and chalcone synthase 2 (Medicago sativa) were found to be present in three proteins. This similarity points toward anthocyanin biosynthetic process. Similarity to chalcone synthase 2 reveals its possible role in Naringenine and Chalcone synthase like activity. In 3 keto acyl CoA synthase of Oryza brachyantha. Active site residues C-240, H-407, N-447 are present in J3MF16 protein that are common in these three protein at different positions. Structural variations among dimer interface, product binding site, malonyl-CoA binding sites, were predicted in localized combination of conserved residues.


title: A formal model of computer architectures for digital system design environments

abstract: A new and powerful model of computer architectures for machine description is presented. This model is capable of representing a machine across the abstraction levels ranging from the exo-architecture to the gate level. The goal is to establish a formal framework for the construction of a new hardware description language that will be useful for a large class of retargetable design-automation systems. >


title: An improved procedure in detecting the stability robustness of systems with uncertain delay

abstract: A very efficient and reliable shortcut is explained here on the procedure, which resolves the stability robustness of LTI dynamics against an uncertain delay. This procedure reduces the numerical complexity of the solution by an order of magnitude. This note is in response to a number of user requests on the practicality of the mentioned methodology.


title: Association of African-American Ethnic Background With Survival in Men With Metastatic Prostate Cancer

abstract: Background: African-American men have earlier onset of prostate cancer, higher prostate-specific antigen (PSA) levels, more advanced stage at diagnosis, and higher mortality than white men. It is not known whether the poorer survival of African-American men with prostate cancer reflects their later stage at diagnosis or differences in the basic biology of their disease. To evaluate this question, we examined outcomes of African-American and white men with metastatic prostate cancer in the context of a randomized clinical trial. Methods: Southwest Oncology Group Study 8894 was a randomized phase III trial that compared orchiectomy with or without flutamide in men with metastatic prostate cancer. Using data from 288 African-American and 975 white men in the trial, we conducted a proportional hazards regression analysis to determine if ethnicity was an independent predictor of survival. All statistical tests were two-sided. Results: African-American men were more likely than white men to have extensive disease and bone pain and had poorer performance status, younger age at study entry, higher Gleason score, and higher PSA levels. After adjustment for these prognostic variables, the hazard ratio (HR) for all-cause mortality for African-American men relative to white men was 1.23 (P = .018). Further adjustment for initial quality-of-life assessments also resulted in higher HRs associated with African-American ethnicity relative to white ethnicity (HR = 1.39; P = .007). Conclusions: African-American men with metastatic prostate cancer have a statistically significantly worse prognosis than white men that cannot be explained by the prognostic variables explored in this study. These data should give increased impetus for efforts to detect the disease early in African-American men and for the development of more effective therapies based on potential biologic differences in this ethnic group.


title: Design of integrated planar Marchand balun using physical transformer model

abstract: A simple and effective technique for designing integrated planar Marchand balun is presented in this paper. The approach uses the physical transformer model to replace the lossy coupled transmission lines in a conventional Marchand balun design. As a demonstration and validation of the design approach, a Marchand balun using silicon-based integrated passive device (IPD) technology is carried out at a center frequency of 2.45 GHz. The measured results show low insertion loss and high balance property over a wide bandwidth for the implemented Marchand balun. Comparison among modeled, EM simulated and measured results shows good agreement.


title: Solid state transformer application to grid connected photovoltaic inverters

abstract: In the paper, an architecture, including a solid state transformer (SST) which is different from the conventional style is proposed The photovoltaic system with SST consists of two power stages. A dual half bridge (DHB) converter constitutes the first power stage, which aims to achieve the maximum power point tracking (MPPT) functions. A grid connected inverter is used to stabilize the DC bus voltage and make the output current sinusoidal. The control technique is presented and validated by simulation implemented on a photovoltaic system with SST. The simulation results prove the effectiveness of the proposed photovoltaic system and the proposed control technique.


title: Worldview transformation and the development of social consciousness

abstract: In this paper, we examine how increasing understanding and explicit awareness of social consciousness can develop through trans- formations in worldview. Based on a model that emerged from a series of qualitative and quantitative studies on worldview transformation, we identify five developmental levels of social consciousness: embedded, self-reflexive, engaged, collaborative, and resonant. As a person's worldview transforms, awareness can expand to include each of these levels, leading to enhanced prosocial experiences and behaviours. Increased social consciousness can in turn stimulate further transform- ations in worldview. We then consider an educational curriculum to facilitate the understanding of worldview and the cultivation of social consciousness as core capacities for twenty-first century students and global citizens.


title: Medical students' personality characteristics and academic performance: A five-factor model perspective.

abstract: Objectives This study investigates: (1) which personality traits are typical of medical students as compared to other students, and (2) which personality traits predict medical student performance in pre-clinical years. Design This paper reports a cross-sectional inventory study of students in nine academic majors and a prospective longitudinal study of one cohort of medical students assessed by inventory during their first preclinical year and by university examination at the end of each pre-clinical year. Subjects and methods In 1997, a combined total of 785 students entered medical studies courses in five Flemish universities. Of these, 631 (80AE4%) completed the NEO-PI-R (i.e. a measure of the Five-Factor Model of Personality). This was also completed by 914 Year 1 students of seven other academic majors at Ghent University. Year end scores for medical students were obtained for 607 students in Year 1, for 413 in Year 2, and for 341 in Year 3. Results Medical studies falls into the group of majors where students score highest on extraversion and agreeableness. Conscientiousness (i.e. self-achievement and self-discipline) significantly predicts final scores in each pre-clinical year. Medical students who score low on conscientiousness and high on gregariousness and excitement-seeking are significantly less likely to sit examinations successfully. Conclusions The higher scores for extraversion and agreeableness, two dimensions defining the interpersonal dynamic, may be beneficial for doctors’ collaboration and communication skills in future professional practice. Because conscientiousness affects examination results and can be reliably assessed at the start of a medical study career, personality assessment may be a useful tool in student counselling and guidance.


title: Decreased gut microbiota diversity, delayed Bacteroidetes colonisation and reduced Th1 responses in infants delivered by caesarean section.

abstract: OBJECTIVE ::: The early intestinal microbiota exerts important stimuli for immune development, and a reduced microbial exposure as well as caesarean section (CS) has been associated with the development of allergic disease. Here we address how microbiota development in infants is affected by mode of delivery, and relate differences in colonisation patterns to the maturation of a balanced Th1/Th2 immune response. ::: ::: ::: DESIGN ::: The postnatal intestinal colonisation pattern was investigated in 24 infants, born vaginally (15) or by CS (nine). The intestinal microbiota were characterised using pyrosequencing of 16S rRNA genes at 1 week and 1, 3, 6, 12 and 24 months after birth. Venous blood levels of Th1- and Th2-associated chemokines were measured at 6, 12 and 24 months. ::: ::: ::: RESULTS ::: Infants born through CS had lower total microbiota diversity during the first 2 years of life. CS delivered infants also had a lower abundance and diversity of the Bacteroidetes phylum and were less often colonised with the Bacteroidetes phylum. Infants born through CS had significantly lower levels of the Th1-associated chemokines CXCL10 and CXCL11 in blood. ::: ::: ::: CONCLUSIONS ::: CS was associated with a lower total microbial diversity, delayed colonisation of the Bacteroidetes phylum and reduced Th1 responses during the first 2 years of life.


title: Development of high frequency circuit model for oil-immersed power transformers and its application for lightning surge analysis

abstract: The lightning impulse withstand voltage for an oil-immersed power transformer is determined by the value of the lightning surge overvoltage generated at the transformer terminal. This overvoltage value has been conventionally obtained through lightning surge analysis using the electromagnetic transients program (EMTP), where the transformer is often simulated by a single lumped capacitance. However, since high frequency surge overvoltages ranging from several kHz to several MHz are generated in an actual system, a transformer circuit model capable of simulating the range up to this high frequency must be developed for further accurate analysis. In this paper, a high frequency circuit model for an oil-immersed transformer was developed and its validity was verified through comparison with the measurement results on the model winding actually produced. Consequently, it emerged that a high frequency model with three serially connected LC parallel circuits could adequately simulate the impedance characteristics of the winding up to a high frequency range of several MHz. Following lightning surge analysis for a 500 kV substation using this high frequency model, the peak value of the waveform was evaluated as lower than that simulated by conventional lumped capacitance even though the front rising was steeper. This phenomenon can be explained by the charging process of the capacitance circuit inside the transformer. Furthermore, the waveform analyzed by each model was converted into an equivalent standard lightning impulse waveform and the respective peak values were compared. As a result, the peak value obtained by the lumped capacitance simulation was evaluated as relatively higher under the present analysis conditions.


title: Constructing a Language From Scratch: Combining Bottom–Up and Top–Down Learning Processes in a Computational Model of Language Acquisition

abstract: We present a computational model that allows us to study the interplay of different processes involved in first language acquisition. We build on the assumption that language acquisition is usage-driven and assume that there are different processes in language acquisition operating at different levels. Bottom-up processing allows a learner to identify regularities in the linguistic input received, while top–down processing exploits prior experience and previous knowledge to guide choices made during bottom–up processing. To shed light on the interplay between top–down and bottom–up processing in language acquisition, we present a computational model of language acquisition that is based on bootstrapping mechanisms and is usage-based in that it relies on discovered regularities to segment speech into word-like units. Based on this initial segmentation, our model induces a construction grammar that in turn acts as a top–down prior that guides the segmentation of new sentences into words. We spell out in detail these processes and their interplay, showing that top–down processing increases both understanding performance and segmentation accuracy. Our model thus contributes to a better understanding of the interplay between bottom–up and top–down processes in first language acquisition and thus to a better understanding of the mechanisms and architecture involved in language acquisition.


title: Design And Implementation Of Anthropomorphic Robotic Arm

abstract: The report focuses on the design and demonstration of an anthropomorphic robotic arm with seven degrees of freedom using readily available low-cost components to perform different real time human hand applications. The robotic arm consists of a shoulder, elbow, wrist and a five-finger gripper. It can perform different gripping actions, such as lateral, spherical, cylindrical and tip-holding gripping actions; each finger has three movable links. The actuator used for the robotic arm is a high torque dc servo motor and the five-finger gripper consists of five cables placed like tendons in the human arm. Implementation is done using a human hand glove which senses the motion from sensor technology to produce a proportional analog voltage, digitized via the microcontroller Atmel ATmega32. The microcontroller then through the processed signal controls the mechanical structure that is the robotic arm.


title: Risk Factors for Smartphone Addiction in Korean Adolescents: Smartphone Use Patterns

abstract: With widespread use of the smartphone, clinical evidence for smartphone addiction remains unclear. Against this background, we analyzed the effect of smartphone use patterns on smartphone addiction in Korean adolescents. A total of 370 middle school students participated. The severity of smartphone addiction was measured through clinical interviews and the Korean Smartphone Addiction Proneness Scale. As a result, 50 (13.5%) were in the smartphone addiction group and 320 (86.5%) were in the healthy group. To investigate the effect of smartphone use patterns on smartphone addiction, we performed self-report questionnaires that assessed the following items: smartphone functions mostly used, purpose of use, problematic use, and parental attitude regarding smartphone use. For smartphone functions mostly used, the addiction group showed significantly higher scores in "Online chat." For the purpose of use, the addiction group showed significantly higher "habitual use," "pleasure," "communication," "games," "stress relief," "ubiquitous trait," and "not to be left out." For problematic use, the addiction group showed significantly higher scores on "preoccupation," "tolerance," "lack of control," "withdrawal," "mood modification," "conflict," "lies," "excessive use," and "loss of interest." For parental attitude regarding children's smartphone use, the addiction group showed significantly higher scores in "parental punishment." Binary logistic regression analysis indicated that "female," "use for learning," "use for ubiquitous trait," "preoccupation," and "conflict" were significantly correlated with smartphone addiction. This study demonstrated that the risk factors for smartphone addiction were being female, preoccupation, conflict, and use for ubiquitous trait; the protective factor was use for learning. Future studies will be required to reveal the additional clinical evidence of the disease entity for smartphone addiction.


title: Efficient transfer entropy analysis of non-stationary neural time series

abstract: Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions. To obtain these necessary observations, available estimators typically assume stationarity of processes to allow pooling of observations over time. This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary. As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations. Such an ensemble of realizations is often readily available in neuroscience experiments in the form of experimental trials. Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series. We present an efficient implementation of the approach that is suitable for the increased computational demand of the ensemble method's practical application. In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method for transfer entropy estimation. We test the performance and robustness of our implementation on data from numerical simulations of stochastic processes. We also demonstrate the applicability of the ensemble method to magnetoencephalographic data. While we mainly evaluate the proposed method for neuroscience data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and artificial systems.


title: Alumina-zirconia composites functionalized with laminin-1 and laminin-5 for dentistry: effect of protein adsorption on cellular response.

abstract: The present paper describes a study on laminin interaction with the surface of two alumina–zirconia composites with different percentages of ZrO2, both with submicrometric grain size. As major molecules within the basement membrane (BM), laminins are important protein fragments for epithelial cell adhesion and migration. On the other hand, alumina–zirconia composites are very attractive materials for dental applications due to their esthetic and mechanical properties. X-Ray photoelectron spectroscopy and atomic force microscopy were used to study the adsorption of two types of laminin, laminin-1 (Ln-1) and laminin-5 (Ln-5), onto the ceramics surfaces. The in vitro cell response was determined by intracellular phosphorylation of major kinases. Ceramics samples functionalized with laminins showed better cellular activation than untreated specimens; furthermore, cellular activation was found to be greater for the composite with higher percentage in zirconia when functionalized with Ln-5, whereas the adsorption of Ln-1 resulted in a greater activation for the alumina-rich oxide.


title: Digital reading: An overview

abstract: Purpose: Digital reading is an important research topic in contemporary information science research. This paper aims to provide a snapshot of major studies on digital reading over the past few years. Design/methodology/approach: This paper begins by introducing the background in digital reading, then outlines major research findings. Findings: The paper demonstrates the growth of interest in information science and other disciplines in digital reading behavior. Five areas are highlighted: Digital reading behavior, print vs. digital, preference for reading medium, multi-tasking and learning, and technological advancement and traditional attachment. Research limitations: Only major studies in the North American and European countries are covered. Practical implications: Understanding reading behavior in the digital environment would help develop more effective reading devices and empower readers in the online environment. Originality/value: The paper represents a first attempt to compare, evaluate, and synthesize recent studies on digital reading. Implications for the changes in reading behavior are discussed, and directions for future research are suggested.


title: High-speed medium voltage (MV) drive applications enabled by series connection of 1.7 kV SiC MOSFET devices

abstract: The medium voltage (MV) high-speed drives are required for traction, wind energy, marine, aerospace, oil, and gas compressors applications. The MV converter must be able to switch at higher switching frequencies (> 5 kHz) to generate higher fundamental frequency AC input voltages for the motor (≥ 500 Hz) and thereby achieving high speed at the motor output (≥ 15000 rpm). This paper presents the series connection of 1.7 kV SiC MOSFET devices to enable simple two level MV converter for high-speed drive and grid connected applications. Experimental switching characterization of a phase leg (one pole of a three-phase converter) with the series connection of four devices per arm (or eight per leg) has been presented. It also presents the experimental results of a DC-AC half-bridge inverter with four devices in series per arm at 3kV dc bus, at different fundamental frequencies. It also includes the experimental endurance test results of phase-leg (pole) in a DC-DC converter at 100 kW, 3kV dc bus. Furthermore, it also presents the performance evaluation of a 3-phase voltage source inverter (VSI) efficiency using four series connected devices for 3.6 kV dc bus, 2.1 kV AC(L-L), 720 kW output power and its comparison with single 6.5 kV Si IGBT.


title: Intelligent agent based hotel search & booking system

abstract: M-commerce (mobile commerce) is the buying and selling of goods and services through wireless handheld devices such as cellular telephone and personal digital assistants PDA. M-commerce provides lot of services like Mobile ticketing, Mobile banking, Mobile location based services, Mobile auctions, Mobile purchasing and so on. This represents an incredible opportunity to enable mobile devices, as a universal device for mobile commerce applications. For such applications, we normally want to choose the best hotel in prime locations, with modern facilities, clean environment and affordable rates. This can be time consuming and sometimes costly when doing this on our own or using human agents. So based on this we here propose "Intelligent Agent Based Hotel Search and Booking System". This system here would use an intelligent agent (instead of the human agent) to perform similar search and booking activities that can improve the speed of the search and reduce cost significantly. So in summary we propose developing a an agent that will move from hotel to hotel from the mobile devices like Smart phones by collecting details on the list of available facilities, price, customer experience, transportation etc and forward-feeding them back to the user's mobile phone. The implementation will be carried using JADE-LEAP agent development kit


title: Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis

abstract: Sensor data fusion technology is widely employed in fault diagnosis. The information in a sensor data fusion system is characterized by not only fuzziness, but also partial reliability. Uncertain information of sensors, including randomness, fuzziness, etc., has been extensively studied recently. However, the reliability of a sensor is often overlooked or cannot be analyzed adequately. A Z-number, Z = (A, B), can represent the fuzziness and the reliability of information simultaneously, where the first component A represents a fuzzy restriction on the values of uncertain variables and the second component B is a measure of the reliability of A. In order to model and process the uncertainties in a sensor data fusion system reasonably, in this paper, a novel method combining the Z-number and Dempster–Shafer (D-S) evidence theory is proposed, where the Z-number is used to model the fuzziness and reliability of the sensor data and the D-S evidence theory is used to fuse the uncertain information of Z-numbers. The main advantages of the proposed method are that it provides a more robust measure of reliability to the sensor data, and the complementary information of multi-sensors reduces the uncertainty of the fault recognition, thus enhancing the reliability of fault detection.


title: DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks

abstract: In this work, we present a compact, modular framework for constructing new recurrent neural architectures. Our basic module is a new generic unit, the Transition Based Recurrent Unit (TBRU). In addition to hidden layer activations, TBRUs have discrete state dynamics that allow network connections to be built dynamically as a function of intermediate activations. By connecting multiple TBRUs, we can extend and combine commonly used architectures such as sequence-to-sequence, attention mechanisms, and recursive tree-structured models. A TBRU can also serve as both an {\em encoder} for downstream tasks and as a {\em decoder} for its own task simultaneously, resulting in more accurate multi-task learning. We call our approach Dynamic Recurrent Acyclic Graphical Neural Networks, or DRAGNN. We show that DRAGNN is significantly more accurate and efficient than seq2seq with attention for syntactic dependency parsing and yields more accurate multi-task learning for extractive summarization tasks.


title: A novel fuzzy logic approach to contrast enhancement

abstract: Abstract Contrast enhancement is one of the most important issues of image processing, pattern recognition and computer vision. The commonly used techniques for contrast enhancement fall into two categories: (1) indirect methods of contrast enhancement and (2) direct methods of contrast enhancement. Indirect approaches mainly modify histogram by assigning new values to the original intensity levels. Histogram specification and histogram equalization are two popular indirect contrast enhancement methods. However, histogram modification technique only stretches the global distribution of the intensity. The basic idea of direct contrast enhancement methods is to establish a criterion of contrast measurement and to enhance the image by improving the contrast measure. The contrast can be measured globally and locally. It is more reasonable to define a local contrast when an image contains textual information. Fuzzy logic has been found many applications in image processing, pattern recognition, etc. Fuzzy set theory is a useful tool for handling the uncertainty in the images associated with vagueness and/or imprecision. In this paper, we propose a novel adaptive direct fuzzy contrast enhancement method based on the fuzzy entropy principle and fuzzy set theory. We have conducted experiments on many images. The experimental results demonstrate that the proposed algorithm is very effective in contrast enhancement as well as in preventing over-enhancement.


Trust enhanced technology acceptance model - consumer acceptance of mobile payment solutions, The Stockholm Mobility Roundtable 2003


title: Discriminative Optimization: Theory and Applications to Computer Vision Problems

abstract: Many computer vision problems are formulated as the optimization of a cost function. This approach faces two main challenges: designing a cost function with a local optimum at an acceptable solution, and developing an efficient numerical method to search for this optimum. While designing such functions is feasible in the noiseless case, the stability and location of local optima are mostly unknown under noise, occlusion, or missing data. In practice, this can result in undesirable local optima or not having a local optimum in the expected place. On the other hand, numerical optimization algorithms in high-dimensional spaces are typically local and often rely on expensive first or second order information to guide the search. To overcome these limitations, we propose Discriminative Optimization (DO), a method that learns search directions from data without the need of a cost function. DO explicitly learns a sequence of updates in the search space that leads to stationary points that correspond to the desired solutions. We provide a formal analysis of DO and illustrate its benefits in the problem of 3D registration, camera pose estimation, and image denoising. We show that DO outperformed or matched state-of-the-art algorithms in terms of accuracy, robustness, and computational efficiency.


title: Plant classification system for crop /weed discrimination without segmentation

abstract: This paper proposes a machine vision approach for plant classification without segmentation and its application in agriculture. Our system can discriminate crop and weed plants growing in commercial fields where crop and weed grow close together and handles overlap between plants. Automated crop / weed discrimination enables weed control strategies with specific treatment of weeds to save cost and mitigate environmental impact.


title: The Personal Interaction Panel - a Two-Handed Interface for Augmented Reality

abstract: This paper describes the introduction of a new interaction paradigm to augmented reality applications. The everyday tool handling experience of working with pen and notebooks is extended to create a three dimensional two-handed interface, that supports easy-to-understand manipulation tasks in augmented and virtual environments. In the design step we take advantage from the freedom, given by our very low demands on hardware and augment form and functionality to this device. On the basis of examples from object manipulation, augmented research environments and scientific visualization we show the generality of applicability. Although being in the first stages implementation, we consider the wide spectrum of suitability for different purposes.


title: WebPatrol: automated collection and replay of web-based malware scenarios

abstract: Traditional remote-server-exploiting malware is quickly evolving and adapting to the new web-centric computing paradigm. By leveraging the large population of (insecure) web sites and exploiting the vulnerabilities at client-side modern (complex) browsers (and their extensions), web-based malware becomes one of the most severe and common infection vectors nowadays. While traditional malware collection and analysis are mainly focusing on binaries, it is important to develop new techniques and tools for collecting and analyzing web-based malware, which should include a complete web-based malicious logic to reflect the dynamic, distributed, multi-step, and multi-path web infection trails, instead of just the binaries executed at end hosts. This paper is a first attempt in this direction to automatically collect web-based malware scenarios (including complete web infection trails) to enable fine-grained analysis. Based on the collections, we provide the capability for offline "live" replay, i.e., an end user (e.g., an analyst) can faithfully experience the original infection trail based on her current client environment, even when the original malicious web pages are not available or already cleaned. Our evaluation shows that WebPatrol can collect/cover much more complete infection trails than state-of-the-art honeypot systems such as PHoneyC [11] and Capture-HPC [1]. We also provide several case studies on the analysis of web-based malware scenarios we have collected from a large national education and research network, which contains around 35,000 web sites.


title: Stepping out of the box: towards analytics outside the learning management system

abstract: Most of the current learning analytic techniques have as starting point the data recorded by Learning Management Systems (LMS) about the interactions of the students with the platform and among themselves. But there is a tendency on students to rely less on the functionality offered by the LMS and use more applications that are freely available on the net. This situation is magnified in studies in which students need to interact with a set of tools that are easily installed on their personal computers. This paper shows an approach using Virtual Machines by which a set of events occurring outside of the LMS are recorded and sent to a central server in a scalable and unobtrusive manner.


title: Motor activation in literal and non-literal sentences: does time matter?

abstract: Despite the impressive amount of evidence showing involvement of the sensorimotor systems in language processing, important questions remain unsolved among which the relationship between non literal uses of language and sensorimotor activation. The literature did not yet provide a univocal answer on whether the comprehension of non literal, abstract motion sentences engages the same neural networks recruited for literal sentences. A previous TMS study using the same experimental materials of the present study showed activation for literal, fictive and metaphoric motion sentences but not for idiomatic ones. To evaluate whether this may depend on insufficient time for elaborating the idiomatic meaning, we conducted a behavioural experiment that used a sensibility judgment task performed by pressing a button either with a hand finger or with a foot. Motor activation is known to be sensitive to the action-congruency of the effector used for responding. Therefore, all other things being equal, significant differences between response emitted with an action-congruent or incongruent effector (foot vs. hand) may be attributed to motor activation. Foot-related action verbs were embedded in sentences conveying literal motion, fictive motion, metaphoric motion or idiomatic motion. Mental sentences were employed as a control condition. Foot responses were significantly faster than finger responses but only in literal motion sentences. We hypothesize that motor activation may arise in early phases of comprehension processes (i.e. upon reading the verb) for then decaying as a function of the strength of the semantic motion component of the verb.


title: Low Power Wide Area Networks (LPWANs) for Internet of Things (IoT) Applications: Research Challenges and Future Trends

abstract: Internet of things (IoT) changes significantly the requirements for connectivity, mainly with regards to long battery life, low device cost, low deployment cost, extended coverage and support for a massive number of devices. Driven from these requirements, several different cellular and non-cellular low power wide area network (LPWAN) solutions are emerging and competing for IoT business and the overall connectivity market. Motivated by this, in this paper, we review and compare the design specifications of different LPWAN solutions, as well as, we discuss their suitability for different IoT applications. Finally, we present the challenges, future trends, and potential research directions for LPWANs.


title: ubiGaze: ubiquitous augmented reality messaging using gaze gestures

abstract: We describe ubiGaze, a novel wearable ubiquitous method to augment any real-world object with invisible messages through gaze gestures that lock the message into the object. This enables a context and location dependent messaging service, which users can utilize discreetly and effortlessly. Further, gaze gestures can be used as an authentication method, even when the augmented object is publicly known. We developed a prototype using two wearable devices: a Pupil eye tracker equipped with a scene camera and a Sony Smartwatch 3. The eye tracker follows the users' gaze, the scene camera captures distinct features from the selected real-world object, and the smartwatch provides both input and output modalities for selecting and displaying messages. We describe the concept, design, and implementation of our real-world system. Finally, we discuss research implications and address future work.


Cosette: An Automated Prover for SQL


title: Scale Inside-Out: Rapid Mitigation of Cloud DDoS Attacks

abstract: The distributed denial of service (DDoS) attacks in cloud computing requires quick absorption of attack data. DDoS attack mitigation is usually achieved by dynamically scaling the cloud resources so as to quickly identify the onslaught features to combat the attack. The resource scaling comes with an additional cost which may prove to be a huge disruptive cost in the cases of longer, sophisticated, and repetitive attacks. In this work, we address an important problem, whether the resource scaling during attack, always result in rapid DDoS mitigation? For this purpose, we conduct real-time DDoS attack experiments to study the attack absorption and attack mitigation for various target services in the presence of dynamic cloud resource scaling. We found that the activities such as attack absorption which provide timely attack data input to attack analytics, are adversely compromised by the heavy resource usage generated by the attack. We show that the operating system level local resource contention, if reduced during attacks, can expedite the overall attack mitigation. The attack mitigation would otherwise not be completed by the dynamic scaling of resources alone. We conceived a novel relation which terms “Resource Utilization Factor” for each incoming request as the major component in forming the resource contention. To overcome these issues, we propose a new “Scale Inside-out” approach which during attacks, reduces the “Resource Utilization Factor” to a minimal value for quick absorption of the attack. The proposed approach sacrifices victim service resources and provides those resources to mitigation service in addition to other co-located services to ensure resource availability during the attack. Experimental evaluation shows up to 95 percent reduction in total attack downtime of the victim service in addition to considerable improvement in attack detection time, service reporting time, and downtime of co-located services.


title: Identification of Fake Notes and Denomination Recognition

abstract: Currency recognition technology focuses on searching and retrieving visible and hidden tags in monetary standards for a systematic classification. The recognition of the monetary standards and the conversion mechanism is implemented in order to decrease the human power to perceive the measure of the currency's currency value and to convert it to other standards without human supervision. The software interface suggested here could be utilized for various monetary standards (four standards are utilized in our project). Habitually, bank notes are either blurred or damaged; many have complicated recommendation to increase security. It makes the errand of perceiving the monetary standards troublesome. So, for this aspiration, it is predominant to choose the right functions and the right algorithm. The fundamental prerequisites for an algorithm that can be considered as practicable are simplicity, less complexity, high speed and efficiency.