title: Development of gait assist method for parkinson's disease patients with FOG in walking

abstract: Parkinson's disease (PD) is progressive degenerative neurological disorder by dopamine depletion. Many PD patients have Freezing of Gait (FOG). During FOG, they have difficulty to start to take a step in several seconds to over ten seconds. FOG leads to fall, and lower Quality of life due to get injured and psychological influence by anxious and fear of falling. For symptomatic relief of FOG in daily life, it is necessary to predict automatically FOG and physical intervention by wearable system without environment arrangement and human assistance. The purpose of this study is to develop a wearable gait assist system that predicts FOG episode and performs physical assistance, and to confirm the feasibility of the system through an experiment. Subjects of the system have enough muscle to walk alone in this study. For this reason, the system is single leg mechanism of exoskeleton type and a power unit is installed in a knee joint. Prediction method for FOG episode is to detect reduction of gait cycle (GC) based on peak-to-peak of toe floor reaction force. For the experiment with an able-bodied participant, the participant performed normal gait and walking with short steps. As a result, the developed system detected all of GC reduction and performed the assistance, and participant's knee joint was flexed. Therefore, we confirmed the feasibility of the developed system with the prediction method through the experiment.


title: C4.5 and imbalanced data sets: investigating the effect of sampling method, probabilistic estimate, and decision tree structure

abstract: Imbalanced data sets are becoming ubiquitous, as many applications have very few instances of the \interesting" or \abnormal" class. Traditional machine learning algorithms can be biased towards majority class due to over-prevalence. It is desired that the interesting (minority) class prediction be improved, even if at the cost of additional majority class errors. In this paper, we study three issues, usually considered separately, concerning decision trees and imbalanced data sets | quality of probabilistic estimates, pruning, and eect of preprocessing the imbalanced data set by over or undersampling methods such that a fairly balanced training set is provided to the decision trees. We consider each issue independently and in conjunction with each other, highlighting the scenarios where one method might be preferred over another for learning decision trees from imbalanced data sets.


title: Single-stage quasi z-source flyback Power Factor Correction converter

abstract: This paper presents a single-stage quasi z-source flyback Power Factor Correction (PFC) AC-DC converter which features high efficiency, unity power factor (PF), and low Total Harmonic Distortion (THD) under wide range input voltage operation. The proposed PFC converter can achieve very low voltage spike on switch by utilizing a common topology, namely quasi z-source topology that has been widely used in many applications. As a result, a low voltage rating Mosfet with low on-resistance R ds(on) can be used to gain a high efficiency. In addition, Continuous Conduction Mode (CCM) operation associated with the variable duty control scheme to regulate the output voltage is also applied to get high PF, low harmonic components in the line current, as well as conduction loss reduction. In this study, the operating principle, circuit analysis, and control method of the proposed PFC converter are provided. Additionally, the design guidelines are also verified by a laboratory prototype with the input voltage of 130–230 V AC, 36 V/180 W output, and 55 kHz switching frequency. Consequently, this topology is suitable for wide input voltage range and low-to-medium power applications where a unity PF, low THD, and high efficiency are required.


title: Packaging of silicon photonics systems

abstract: We address the effects of packaging on performance, reliability and cost of photonic devices. For silicon photonics we address some specific packaging aspects. Finally we propose an approach for integration of photonics and ASICs.


title: The impact of private health insurers on the qualit y of regional health care systems in Russia

abstract: The 1991 law ‘On health insurance for the citizens of the Russian Federation’ established that social health insurance is to be offered by multiple private insu rance companies. The paper is the first econometric analysis measuring the effect of private health insurers on quality related outcomes of social health insurance (SHI) systems in Russian regions. The baseline model intr oduces regional SHI system as a binary variable wit h unity value corresponding to the presence of private heal th insurers as the only agents at the SHI market. T he extended model captures endogeneity by employing an instrumental variable approach. The non-parametric model us es kernel regressions. The results of parametric and kernel regressions reveal that the presence of private insurers is a s ignificant determinant of infant and under-five mortality. The positive impact of private insurers is explained b y regional institutional reforms. The methods of provider reim bursement are related to infant and under-five mort ality, which offers suggestive evidence for enabling insurer com petition through selective contracting with health care providers.


title: Three-Skips CNN for road scene semantic segmentation

abstract: In this paper we propose a deep learning architecture to make the best use of global and local information for pixel-wise semantic segmentation. The architecture of three-skips CNN is built with convolutional layers in VGG16 network and its mirrored convolutional layers. Our architecture aims to road scene understanding. In order to save memory and computational time, we use unpooling layers to map low resolution feature maps to the input resolution. We introduce three skip architectures which combine local information and global information to produce accurate and detailed segmentations. Besides, we present the median balance method to deal with class unbalance problem in road scene datasets. Thorough evaluations on CamVid dataset demonstrate our approach has state-of-the-art performance and less computational time.


title: Aging and autobiographical memory: Dissociating episodic from semantic retrieval.

abstract: Cognitive aging research documents reduced access to contextually specific episodic details in older adults, whereas access to semantic or other nonepisodic information is preserved or facilitated. The present study extended this finding to autobiographical memory by using a new measure; the Autobiographical Interview. Younger and older adults recalled events from 5 life periods. Protocols were scored according to a reliable system for categorizing episodic and nonepisodic information. Whereas younger adults were biased toward episodic details reflecting happenings, locations, perceptions, and thoughts, older adults favored semantic details not connected to a particular time and place. This pattern persisted after additional structured probing for contextual details. The Autobiographical Interview is a useful instrument for quantifying episodic and semantic contributions to personal remote memory. Most research on real-world autobiographical memory in older adults has examined life-period effects on numbers of memories recalled (e.g., Franklin & Holding, 1977; Hyland & Ackerman, 1988; Rubin & Schulkind, 1997b). Relatively little is known about the quality of older adults’ autobiographical recollections in comparison to those of younger adults. Cognitive aging research predicts that the quality of older adults’ autobiographical recollection would differ from that of younger adults, yet the direction of these differences depends on the task. Age-related decline is greatest for explicit, effortful, and unstructured retrieval tasks (Zacks, Hasher, & Li, 2000). Relative to younger adults, older adults are impaired at retrieving episodic contextual details (McIntyre & Craik, 1987; Spencer & Raz, 1995), report a lesser degree of reexperiencing


title: AN OPTIMAL APPROACH FOR THE TASKS ALLOCATION BASED ON THE FUSION OF EC AND ITCC IN THE DISTRIBUTED COMPUTING SYSTEMS

abstract: Distributed computing systems (DCS) are of current interest due to the advancement of microprocessors technology and computers networks. It consists of multiple computing nodes that communicate with each other by message passing mechanism. The advancement of the new technologies in communication and information lead to the development of the Distributed System. The task allocation is an essential phase in the Distributed computing systems. We consider the problem of m tasks and n processors (where m>>n). In this paper a heuristic model is presented, which performs static allocation of a set of "m" tasks of a program to a set of "n" processors (where, m >> n) with the constraints of minimizing Inter Task communication Cost (ITCC) and maximize the overall throughput of the system in such a way that allocated load on all the processors should be balanced. While designing the algorithm the Execution Cost (EC) and ITCC have been taken into consideration.


title: Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels

abstract: Multiple antennas can be used for increasing the amount of diversity or the number of degrees of freedom in wireless communication systems. We propose the point of view that both types of gains can be simultaneously obtained for a given multiple-antenna channel, but there is a fundamental tradeoff between how much of each any coding scheme can get. For the richly scattered Rayleigh-fading channel, we give a simple characterization of the optimal tradeoff curve and use it to evaluate the performance of existing multiple antenna schemes.


title: A multiport isolated DC-DC converter

abstract: This paper presents a multi-port isolated DC-DC converter for DC applications. A three-port structure is presented, characterized with full bidirectional power flow and simple control. Galvanic isolation is achieved by means of a multi-winding medium frequency transformer which is a part of a resonant LLC converter. To provide controllable power exchange with external DC ports, two out of three ports are equipped with additional bidirectional buck/boost stages. They serve to provide active power flow control, while the inner resonant stage provides galvanic isolation, operates in the open-loop and adapts its mode of operation based on the actual power flow. Both switched mode and rea-time hardware-in-the-loop simulations, with control algorithm deployed on the digital signal processor, are used to verify and demonstrate various operational modes.


title: Variational Continual Learning

abstract: This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks. The framework can successfully train both deep discriminative models and deep generative models in complex continual learning settings where existing tasks evolve over time and entirely new tasks emerge. Experimental results show that VCL outperforms state-of-the-art continual learning methods on a variety of tasks, avoiding catastrophic forgetting in a fully automatic way.


title: U-Healthcare System: State-of-the-Art Review and Challenges

abstract: With the increase of an ageing population and chronic diseases, society becomes more health conscious and patients become “health consumers” looking for better health management. People’s perception is shifting towards patient-centered, rather than the classical, hospital–centered health services which has been propelling the evolution of telemedicine research from the classic e-Health to m-Health and now is to ubiquitous healthcare (u-Health). It is expected that mobile & ubiquitous Telemedicine, integrated with Wireless Body Area Network (WBAN), have a great potential in fostering the provision of next-generation u-Health. Despite the recent efforts and achievements, current u-Health proposed solutions still suffer from shortcomings hampering their adoption today. This paper presents a comprehensive review of up-to-date requirements in hardware, communication, and computing for next-generation u-Health systems. It compares new technological and technical trends and discusses how they address expected u-Health requirements. A thorough survey on various worldwide recent system implementations is presented in an attempt to identify shortcomings in state-of-the art solutions. In particular, challenges in WBAN and ubiquitous computing were emphasized. The purpose of this survey is not only to help beginners with a holistic approach toward understanding u-Health systems but also present to researchers new technological trends and design challenges they have to cope with, while designing such systems.


Microstructure and corrosion properties of the laser treated SUPERSTON alloy


title: Mapping conceptual to logical models for ETL processes

abstract: Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In previous line of research, we have presented a conceptual and a logical model for ETL processes. In this paper, we describe the mapping of the conceptual to the logical model. First, we identify how a conceptual entity is mapped to a logical entity. Next, we determine the execution order in the logical workflow using information adapted from the conceptual model. Finally, we provide a methodology for the transition from the conceptual to the logical model.


title: Relay-by-smartphone: realizing multihop device-to-device communications

abstract: The Great East Japan Earthquake and Tsunami drastically changed Japanese society, and the requirements for ICT was completely redefined. After the disaster, it was impossible for disaster victims to utilize their communication devices, such as cellular phones, tablet computers, or laptop computers, to notify their families and friends of their safety and confirm the safety of their loved ones since the communication infrastructures were physically damaged or lacked the energy necessary to operate. Due to this drastic event, we have come to realize the importance of device-to-device communications. With the recent increase in popularity of D2D communications, many research works are focusing their attention on a centralized network operated by network operators and neglect the importance of decentralized infrastructureless multihop communication, which is essential for disaster relief applications. In this article, we propose the concept of multihop D2D communication network systems that are applicable to many different wireless technologies, and clarify requirements along with introducing open issues in such systems. The first generation prototype of relay by smartphone can deliver messages using only users' mobile devices, allowing us to send out emergency messages from disconnected areas as well as information sharing among people gathered in evacuation centers. The success of field experiments demonstrates steady advancement toward realizing user-driven networking powered by communication devices independent of operator networks.


title: A full hybrid electric bike: How to increase human efficiency

abstract: Energy and environmental considerations, new paradigms for urban mobility and transportation increased in the past few years the interest on Light Electric Vehicle (LEV). In that context, Electrically Power Assisted Cycles (EPACs) received a great attention: several efforts have been made in order to improve their performance in terms of autonomy, weight, esthetic and feeling with the driver. In this work, project and realization of a full hybrid electric bike (HEB) are presented. The main idea, borrowed from the more explored 4-wheel world, is to use the possible energy fluxes between cyclist and motor in order to improve the efficiency of the primary engine (the human body) being completely self-sustaining and grid-independent (differently to all the other EPACs). Collected biometric data that lead to the algorithm design are presented in detail focusing particularly on human metabolic efficiency measurement. Finally, an experiment performed in cycling track compares the mechanical and metabolic results between traditional and hybrid electric bike.


title: Blockchain-based publicly verifiable data deletion scheme for cloud storage

abstract: With the rapid development of cloud storage, more and more data owners store their data on the remote cloud, that can reduce data owners overhead because the cloud server maintaining the data for them, e.g., storing, updating and deletion. However, that leads to data deletion becomes a security challenge because the cloud server may not delete the data honestly for financial incentives. Recently, plenty of research works have been done on secure data deletion. However, most of the existing methods can be summarized with the same protocol essentially, which called one-bit-return protocol: the storage server deletes the data and returns a one-bit result. The data owner has to believe the returned result because he cannot verify it. In this paper, we propose a novel blockchain-based data deletion scheme, which can make the deletion operation more transparent. In our scheme, the data owner can verify the deletion result no matter how malevolently the cloud server behaves. Besides, with the application of blockchain, the proposed scheme can achieve public verification without any trusted third party.


title: Augmented Reality in Reality

abstract: In this survey of academic contributions driving augmented reality’s commercial potential and of the industry trends advancing software and hardware developments and emerging applications, the author offers advice on how startups hoping to leverage these advances can compete against senior tech tycoons.


title: Hybrid media consumption: how tweeting during a televised political debate influences the vote decision

abstract: An increasing number of people are using microblogs to broadcast their thoughts in real time as they watch televised political events. Microblogging social network sites (SNSs) such as Twitter generate a parallel stream of information and opinion. It is presumed that the additional content enhances the viewing experience, but our experiment explores the validity of this assumption. We studied how tweeting, or passively observing Twitter during a debate, influenced affect, recall and vote decision. For most measures, participants' average feeling and recall toward the candidates did not depend on Twitter activity, but Twitter activity did matter for vote choice. People who actively tweeted changed their voting choice to reflect the majority sentiment on Twitter. Results are discussed in terms of the possibility that active tweeting leads to greater engagement but that it may also make people more susceptible to social influence.


title: Automatic Inference of Search Patterns for Taint-Style Vulnerabilities

abstract: Taint-style vulnerabilities are a persistent problem in software development, as the recently discovered "Heart bleed" vulnerability strikingly illustrates. In this class of vulnerabilities, attacker-controlled data is passed unsanitized from an input source to a sensitive sink. While simple instances of this vulnerability class can be detected automatically, more subtle defects involving data flow across several functions or project-specific APIs are mainly discovered by manual auditing. Different techniques have been proposed to accelerate this process by searching for typical patterns of vulnerable code. However, all of these approaches require a security expert to manually model and specify appropriate patterns in practice. In this paper, we propose a method for automatically inferring search patterns for taint-style vulnerabilities in C code. Given a security-sensitive sink, such as a memory function, our method automatically identifies corresponding source-sink systems and constructs patterns that model the data flow and sanitization in these systems. The inferred patterns are expressed as traversals in a code property graph and enable efficiently searching for unsanitized data flows -- across several functions as well as with project-specific APIs. We demonstrate the efficacy of this approach in different experiments with 5 open-source projects. The inferred search patterns reduce the amount of code to inspect for finding known vulnerabilities by 94.9% and also enable us to uncover 8 previously unknown vulnerabilities.


title: Machine Learning Approach for Cloud NoSQL Databases Performance Modeling

abstract: Cloud computing is a successful, emerging paradigmthat supports on-demand services with pay-as-you-go model. With the exponential growth of data, NoSQL databases havebeen used to manage data in the cloud. In these newly emergingsettings, mechanisms to guarantee Quality of Service heavilyrelies on performance predictability, i.e., the ability to estimatethe impact of concurrent query execution on the performanceof individual queries in a continuously evolving workload. Thispaper presents a performance modeling approach for NoSQLdatabases in terms of performance metrics which is capableof capturing the non-linear effects caused by concurrency anddistribution aspects. Experimental results confirm that our performancemodeling can accurately predict mean response timemeasurements under a wide range of workload configurations.


title: Municipal solid waste management in Beijing: characteristics and challenges

abstract: An overview of the municipal solid waste (MSW) management in Beijing, a city with a resident population of about 19.61 million in 2010, is presented in the article. Economic development and population growth have resulted in a MSW generation increase from 2.96 million tons in 2000 to 6.20 million tons in 2007, fluctuating to 6.35 million tons in 2010. The components of MSW over the past decade are characterized by increasing food and paper contents, and a decreasing ash content. The percentage of food waste, the main putrescible component, increased steadily from 45.77% in 2002 to 66.98% in 2010. Combustible materials, such as plastic, paper, textile, wood and food waste, accounted for 94.66% of MSW in 2008. There are 15 landfill sites, 2 incinerators and 2 composting plants in Beijing, with a total designed capacity of 15,380 tons/day in 2010. The main waste disposal technology used in Beijing is landfill, which accounts for 92.27% of the total designed capacity in 2008 and 78.54% in 2009. The designed capacity of the existing disposal plants cannot cope with the actual quantity of waste generation, resulting in overloading and premature closure of landfill sites. Solid waste incineration has been given priority in technology development and financial support over other disposal methods.


title: Lip-reading by surveillance cameras

abstract: To increase the safety of citizens a network of surveillance cameras has been installed all over the city. These cameras enable analysis of behavior of people and objects. Aggressive behavior is from nature multimodal. Microphones attached to these cameras are not able to analyze speech in a noisy environments and if the speaker is too far away. Lip-movements of a talking mouth can be recorded and understood under limited conditions. From recent progress in the area of Artificial Intelligence it can be expected that large scale lip-reading will be possible next future. In this paper we report the state of the art of lip-reading for the Dutch language. We present a prototype developed at Delft University of Technology. The model is based on the Active Appearance model and Hidden Markov models. The results of experiments with the lip-reading will be represented too. The system has been successfully applied in trains to detect aggressive acts and violence against people and material.


title: A qualitative study of workplace intercultural communication tensions in dyadic face-to-face and computer-mediated interactions

abstract: We present findings from a qualitative study with 28 participants of the dyadic intercultural communication tensions professionals experience in Face-to-Face (FTF) and Computer-Mediated Communication (CMC) workplace interactions. We identify four categories of intercultural communication tensions that emerged most frequently in our dataset including range of emotional expression, level of formality, "fixed" versus flexible appointments and task versus social-orientation. We discuss how these tensions manifested in FTF and CMC media and unravel the ways media supports or hinders intercultural communication. We present the adaptations participants made to mitigate such tensions and offer implications for design. Our findings demonstrate that the most frequently occurring intercultural communication tensions manifested in both FTF and CMC, regardless of the medium used. This indicates that cultural communication challenges will persist no matter the medium, highlighting the opportunity for technologies to better support workplace intercultural communication.


Security and Privacy for Mobile Health-Care ( mHealth ) Systems


title: Technological Aspects of E-Learning Readiness in Higher Education: A Review of the Literature

abstract: E-learning has become one of the most important technologies of the modern era. E-learning is a learning process which aims to create an interactive learning environment based on the use of computers and the internet. Through e-learning, learners can access resources and information from anywhere and at anytime. Many higher education institutions have expressed an interest in implementing e-learning, and e-learning readiness is a critical aspect in achieving successful implementation. Higher education institutions should therefore assess their readiness before initiating an e-learning project. E-learning readiness involves many components of e-learning, including students, lecturers, technology and the environment, which must be ready in order to formulate a coherent and achievable strategy. One of the aspects of e-learning readiness is technological readiness, which plays an important role in implementing an effective and efficient e-learning project. This paper explores the gaps in the knowledge about the technological aspects of e-learning readiness through the conduct of a literature review. In particular, the review focuses on the models that have been developed to assess e-learning readiness.


title: Impact of microbial transformation of food on health — from fermented foods to fermentation in the gastro-intestinal tract

abstract: Fermentation of food components by microbes occurs both during certain food production processes and in the gastro-intestinal tract. In these processes specific compounds are produced that originate from either biotransformation reactions or biosynthesis, and that can affect the health of the consumer. In this review, we summarize recent advances highlighting the potential to improve the nutritional status of a fermented food by rational choice of food-fermenting microbes. The vast numbers of microbes residing in the human gut, the gut microbiota, also give rise to a broad array of health-active molecules. Diet and functional foods are important modulators of the gut microbiota activity that can be applied to improve host health. A truly multidisciplinary approach is required to increase our understanding of the molecular mechanisms underlying health beneficial effects that arise from the interaction of diet, microbes and the human body.


title: A Holographic Antenna Approach for Surface Wave Control in Microstrip Antenna Applications

abstract: A holographic antenna inspired structure is used to control the surface wave (SW) excited by a microstrip patch antenna. The hologram is designed to support a periodic leaky-wave which radiates at broadside and enhances the radiation of the patch while suppressing the horizontal lobe. In this design, the holographic approach is adapted for patch antenna applications where the SW wavelengths are comparable to the freespace wavelength. This is achieved by introducing dual phase-shifting metallic dipoles with periodic spacings. This paper discusses a simple and intuitive design method for the holographic surface, as well as its integration with the microstrip patch. The initial design concept was developed by assuming small perturbation to the SW, which was subsequently verified through full-wave simulations and prototype measurements. The results verified the improvements in the broadside gain and SW efficiency of the microstrip patch at the cost of increased area.


title: Digitally Assisted RF-Analog Self Interference Cancellation for Wideband Full-Duplex Radios

abstract: This brief presents a new approach to cancelling self-interference in full-duplex radios. By augmenting minimal-complexity analog cancellation hardware using a radio frequency vector multiplier with the flexibility and effectiveness of a digital baseband rational function finite impulse response (FIR) filter, the proposed approach enables excellent cancellation performance over a wide modulation bandwidth. This algorithm is devised by exploiting a simplified baseband equivalent behavioral model of the front-end of the full-duplex radio. This allows the parameters of the rational function FIR filter to be identified linearly using the least-squares estimation. The hardware proof-of-concept prototype, built using off-the-shelf components, demonstrated minimum self-interference cancellations of 50 and 40 dB for digitally modulated test signals with modulation bandwidths of 20 and 40–120 MHz, respectively.


DEEP NEAREST CLASS MEAN CLASSIFIERS


title: Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information

abstract: Anomaly detection is an important problem in building energy management in order to identify energy theft and inefficiencies. However, it is hard to differentiate actual anomalies from the genuine changes in energy consumption due to seasonal variations and changes in personal settings such as holidays. One of the important drawbacks of existing anomaly detection algorithms is that various unknown context variables, such as seasonal variations, can affect the energy consumption of users in ways that appear anomalous to existing time series based anomaly detection algorithms. ::: In this paper, we present a system for monitoring the energy consumption of multiple users within a neighborhood and a novel algorithm for detecting anomalies by combining data from multiple users. For each user, the neighborhood is defined as the set of all other users that have similar characteristics (function, location or demography), and are therefore likely to react and consume energy in the similar way in response to the external conditions. The neighborhood can be predefined based on prior customer information, or can be identified through an analysis of historical energy consumption. The proposed algorithm works as a two-step process. In the first step, the algorithm periodically computes an anomaly score for each user by just considering their own energy consumption and variations in the consumption of the past. In the second step, the anomaly score for a user is adjusted by analyzing the energy consumption data in the neighborhood. The collation of data within the neighborhood allows the proposed algorithm to differentiate between these genuine effects and real anomalous behavior of users. Unlike multivariate time series anomaly detection algorithms, the proposed algorithm can identify specific users that are exhibiting anomalous behavior. The capabilities of the algorithm are demonstrated using several year-long real-world data sets, for commercial as well as residential consumers.


title: Towards a true spherical camera

abstract: We present the design of a spherical imaging system with the following properties: (i) A 4π field of view that enables it ::: to "see" in all directions; (ii) a single center of projection to avoid parallax within the field of view; and (iii) a uniform ::: spatial and angular resolution in order to achieve a uniform sampling of the field of view. Our design consists of a spherical ::: (ball) lens encased within a spherical detector shell. The detector shell has a uniform distribution of sensing elements, ::: but with free space between neighboring elements, thereby making the detector partly transparent to light. We determine ::: the optimal dimensions of the sensing elements and the diameter of the detector shell that produce the most compact ::: point spread function. The image captured with such a camera has minimal blur and can be deblurred using spherical ::: deconvolution. Current solid state technologies do not permit the fabrication of a high resolution spherical detector array. ::: Therefore, in order to verify our design, we have built a prototype spherical camera with a single sensing element, which ::: can scan a spherical image one pixel at a time.


title: PZT-Actuated and -Sensed Resonant Micromirrors with Large Scan Angles Applying Mechanical Leverage Amplification for Biaxial Scanning

abstract: This article presents design, fabrication and characterization of lead zirconate titanate (PZT)-actuated micromirrors, which enable extremely large scan angle of up to 106° and high frequency of 45 kHz simultaneously. Besides the high driving torque delivered by PZT actuators, mechanical leverage amplification has been applied for the micromirrors in this work to reach large displacements consuming low power. Additionally, fracture strength and failure behavior of poly-Si, which is the basic material of the micromirrors, have been studied to optimize the designs and prevent the device from breaking due to high mechanical stress. Since comparing to using biaxial micromirror, realization of biaxial scanning using two independent single-axial micromirrors shows considerable advantages, a setup combining two single-axial micromirrors for biaxial scanning and the results will also be presented in this work. Moreover, integrated piezoelectric position sensors are implemented within the micromirrors, based on which closed-loop control has been developed and studied.


title: Interactive narrative: An intelligent systems approach

abstract: Interactive narrative is a form of digital interactive experience in which users create or influence a dramatic storyline through their actions. The goal of an interactive narrative system is to immerse the user in a virtual world such that he or she believes that they are an integral part of an unfolding story and that their actions can significantly alter the direction and/or outcome of the story.In this article we review the ways in which artificial intelligence can be brought to bear on the creation of interactive narrative systems. We lay out the landscape of about 20 years of interactive narrative research and explore the successes as well as open research questions pertaining to the novel use of computational narrative intelligence in the pursuit of entertainment, education, and training.


title: The Generalized Distributive Law

abstract: We discuss a general message passing algorithm, which we call the generalized distributive law (GDL). The GDL is a synthesis of the work of many authors in information theory, digital communications, signal processing, statistics, and artificial intelligence. It includes as special cases the Baum-Welch algorithm, the fast Fourier transform (FFT) on any finite Abelian group, the Gallager-Tanner-Wiberg decoding algorithm, Viterbi's algorithm, the BCJR algorithm, Pearl's "belief propagation" algorithm, the Shafer-Shenoy probability propagation algorithm, and the turbo decoding algorithm. Although this algorithm is guaranteed to give exact answers only in certain cases (the "junction tree" condition), unfortunately not including the cases of GTW with cycles or turbo decoding, there is much experimental evidence, and a few theorems, suggesting that it often works approximately even when it is not supposed to.


title: Visual Sentiment Analysis for Social Images Using Transfer Learning Approach

abstract: Visual sentiment analysis framework can predictthe sentiment of an image by analyzing the image contents. Nowadays, people are uploading millions of images in socialnetworks such as Twitter, Facebook, Google Plus, and Flickr. These images play a crucial part in expressing emotions of users in online social networks. As a result, image sentiment analysishas become important in the area of online multimedia big dataresearch. Several research works are focusing on analyzing thesentiment of the textual contents. However, little investigationhas been done to develop models that can predict sentiment ofvisual content. In this paper, we propose a novel visual sentimentanalysis framework using transfer learning approach to predictsentiment. We use hyper-parameters learned from a very deepconvolutional neural network to initialize our network modelto prevent overfitting. We conduct extensive experiments on aTwitter image dataset and prove that our model achieves betterperformance than the current state-of-the-art.


title: An LPV Modeling and Identification Approach to Leakage Detection in High Pressure Natural Gas Transportation Networks

abstract: In this paper a new approach to gas leakage detection in high pressure natural gas transportation networks is proposed. The pipeline is modelled as a Linear Parameter Varying (LPV) System driven by the source node massflow with the gas inventory variation in the pipe (linepack variation, proportional to the pressure variation) as the scheduling parameter. The massflow at the offtake node is taken as the system output. The system is identified by the Successive Approximations LPV System Subspace Identification Algorithm which is also described in this paper. The leakage is detected using a Kalman filter where the fault is treated as an augmented state. Given that the gas linepack can be estimated from the massflow balance equation, a differential method is proposed to improve the leakage detector effectiveness. A small section of a gas pipeline crossing Portugal in the direction South to North is used as a case study. LPV models are identified from normal operational data and their accuracy is analyzed. The proposed LPV Kalman filter based methods are compared with a standard mass balance method in a simulated 10% leakage detection scenario. The Differential Kalman Filter method proved to be highly efficient.


title: Breeding habitat preference and nest success of Red-necked Phalaropes on Niglintgak Island, Northwest Territories

abstract: Red-necked Phalaropes (Phalaropus lobatus (L., 1758)) breed throughout arctic and subarctic wetlands. These wetlands provide Red-necked Phalaropes dense graminoid habitat that shelters and conceals nests, and freshwater ponds where phalaropes engage in social interactions and feed on small aquatic invertebrates. We studied breeding habitat preference of Red-necked Phalaropes at multiple scales and determined which, if any, nest-site characteristics influenced hatching success. Red-necked Phalaropes avoided habitat at the meso (home range) scale containing large areas of mud and shrub cover, and selected sites with greater cover of graminoids, aquatic emergents, and open water than that available in the environment. At the micro (nest) scale, phalaropes chose sites dominated by graminoids and water. In 2005, concealed nests experienced higher daily nest survival than exposed nests. In 2006, 40% of nests were destroyed during a summer storm surge and we detected no differences in habitat characteristics bet...


title: Domain adaptation with multiple sources

abstract: This paper presents a theoretical analysis of the problem of domain adaptation with multiple sources. For each source domain, the distribution over the input points as well as a hypothesis with error at most ∊ are given. The problem consists of combining these hypotheses to derive a hypothesis with small error with respect to the target domain. We present several theoretical results relating to this problem. In particular, we prove that standard convex combinations of the source hypotheses may in fact perform very poorly and that, instead, combinations weighted by the source distributions benefit from favorable theoretical guarantees. Our main result shows that, remarkably, for any fixed target function, there exists a distribution weighted combining rule that has a loss of at most ∊ with respect to any target mixture of the source distributions. We further generalize the setting from a single target function to multiple consistent target functions and show the existence of a combining rule with error at most 3∊. Finally, we report empirical results for a multiple source adaptation problem with a real-world dataset.


title: Understanding the Anatomy of Analytics-Based Services – A Taxonomy to Conceptualize the Use of Data and Analytics in Services

abstract: The abundance of data accompanied by advances in analytics technologies increasingly drive companies to introduce analytics-based services, i.e. customer-facing services in which data and analytics help customers make decisions. Despite its growing application in practice, theoretical and conceptual work on analytics-based services is still scarce. In this paper, we develop a taxonomy of analytics-based services unveiling their conceptually grounded and empirically validated characteristics. Applying an established taxonomy building method, we draw upon an analysis of 85 use cases of analytics-based services. The results of an expert evaluation indicate both the usefulness and robustness of our taxonomy. The developed taxonomy of analytics-based services contributes in two ways: First, we add to the descriptive knowledge on this new service type, establish a common language among researchers and equip them with the means to analyze analytics-based services in a structured manner – thus laying the foundation for a deeper theorizing process in the future. Second, we provide a concrete conceptualization of analytics-based services for practitioners for initial guidance in new service development.


title: A monotonic policy optimization algorithm for high-dimensional continuous control problem in 3D MuJoCo

abstract: One challenge in applying reinforcement learning with nonlinear function approximator to high- dimensional continuous control problems is that the update policy produced by the many existed algorithms may fail to improve policy performance or even causes a serious degradation of the policy performance. To address this challenge, this paper proposes a new lower bound on the policy improvement where an average policy divergence on state space is penalized. To the best of our knowledge, this is currently the best result about the lower bound on the policy improvement. Optimizing directly the lower bound on the policy improvement is very difficult, because it demands for high computational overhead. According to the ideal of the trust region policy optimization (TRPO), this paper also presents a monotonic policy optimization algorithm, which is based on the new lower bound on the policy improvement introduced in this paper, it can generate a sequence of monotonically improving policies, and it is suitable for the large-scale continuous control problems. This paper also evaluates and compares the proposed algorithms with some of the existed algorithms on highly challenging robot locomotion tasks.


title: Investigation and analysis of an ultrasonic sensor for specific yield assessment and greenhouse features identification

abstract: The spectrum of an ultrasonic return echo from plants has been shown to contain useful information. The research reported in this paper focused on developing an ultrasonic sensing system and analyzing the ultrasonic classification features that would ultimately be used as the basis for a yield estimation robotic system. An algorithm was also developed for prediction of fruit mass per plant based on the ultrasonic echo return from a plant. The ultrasonic sensor system was tested in lab and pepper greenhouse environments and on single pepper plants, single leaves and fruit. The results showed the potential of ultrasonic sensors for such a robot in classifying plants and greenhouse infrastructures such as walls. It showed the robot’s ability to detect hidden plant rows and fruits as well as making an estimation of the fruit mass in single plants. A multi-linear regression model developed for estimating the energy level was found to be highly significant with R 2 of 0.64 and 0.84 for 28–32 and 20–28 kHz ranges respectively. This estimated model was used to derive a prediction method for fruit mass per plant that yielded an R2 of 0.34.


title: Zero voltage switching full - Bridge converter topology

abstract: This paper describes a version of the Phase Shifted Full Bridge (PSFB) converter topology that achieves Zero Voltage Switching (ZVS) of the primary side controlled switches over a very wide load range, has minimal loss of output voltage due to commutation delay, that reduces the excess current flowing through the transformer and primary switches during the freewheeling period all with minimal additional components, particularly no extra controlled switches, no extra diodes in series with any of the current paths and no extra magnetic components. This topology has been prototyped at the 4kW level with outputs of 48V/80A and 24V/160A. The 48V version has been put into volume production in the form of Argus 4kW switch-mode rectifiers for telecom applications.


title: Internet of Things (IoT) Security: Current Status, Challenges and Countermeasures

abstract: This paper presents a survey and an analysis of the current status and concerns of Internet of things (IoT) security. The IoT framework aspires to connect anyone with anything, anywhere. As opposed to the traditional Internet, in addition to humans, an IoT connects a large number of machines, resourceconstrained devices and sensors using heterogeneous wired and wireless networks. An IoT typically has a three conceptual layers consisting of Perception, Network, and Application layers. This paper describes security issues within and across these layers. Many security principles that should be enforced at each layer are also presented. Previous work specific to enforcing security for each IoT layer and corresponding countermeasures are also discussed. Finally, the paper presents future directions for securing the IoT.


title: Open source as enabler for OPC UA in industrial automation

abstract: As a standardized communication protocol, OPC UA is the main focal point with regard to information exchange in the ongoing initiative Industrie 4.0. But there are also considerations to use it within the Internet of Things. The fact that currently no open reference implementation can be used in research for free represents a major problem in this context. The authors have the opinion that open source software can stabilize the ongoing theoretical work. Recent efforts to develop an open implementation for OPC UA were not able to meet the requirements of practical and industrial automation technology. This issue is addressed by the open62541 project which is presented in this article including an overview of its application fields and main research issues.


title: PVC removal from mixed plastics by triboelectrostatic separation.

abstract: Ever increasing oil price and the constant growth in generation of waste plastics stimulate a research on material separation for recycling of waste plastics. At present, most waste plastics cause serious environmental problems due to the disposal by reclamation and incineration. Particularly, polyvinyl chloride (PVC) materials among waste plastics generates hazardous HCl gas, dioxins containing Cl, and so on, which lead to air pollution and shorten the life of incinerator, and it makes difficultly recycling of other plastics. Therefore, we designed a bench scale triboelectrostatic separator for PVC removal from mixed plastics (polyvinyl chloride/polyethylene terephthalate), and then carried out material separation tests. In triboelectrostatic separation, PVC and PET particles are charged negatively and positively, respectively, due to the difference of the work function of plastics in tribo charger of the fluidized-bed, and are separated by means of splitter through an opposite electric field. In this study, the charge efficiency of PVC and PET was strongly dependent on the tribo charger material (polypropylene), relative humidity (below 30%), air velocity (over 10 m/s), and mixture ratio (PET:PVC=1:1). At the optimum conditions (electrode potential of 20 kV and splitter position of -2 cm), PVC rejection and PET recovery in PET products were 99.60 and 98.10%, respectively, and the reproducibility of optimal test was very good (+/-1%). In addition, as a change of splitter position, we developed the technique to recover high purity PET (over 99.99%) although PET recovery decreases by degrees.


title: Design of fully printable planar chipless RFID transponder with 35-bit data capacity

abstract: A fully printable, passive and planar chipless RFID transponder is presented. The chipless tag encodes data into the spectral signature using a multiresonator. Both amplitude and phase components of the spectral signature are used for data encoding. The tag comprises of a microstrip multiresonating circuit with 35 spiral resonators and two cross-polarized transmitting and receiving circular UWB monopol antennas. The chipless tag has potentials for low cost item tagging such as banknotes and secured documents.


title: Learning Object-Centric Transformation for Video Prediction

abstract: Future frame prediction for video sequences is a challenging task and worth exploring problem in computer vision. Existing methods often learn motion information for the entire image to predict next frames. However, different objects in the same scene often move and deform in different ways intuitively. Considering the human visual system, one often pays attention to the key objects that contain crucial motion signals, rather than compress an entire image into a static representation. Motivated by this property of human perception, in this work, we develop a novel object-centric video prediction model that learns local motion transformation dynamically for key object regions with visual attention. By transforming objects iteratively to the original input frames, next frame can be produced. Specifically, we design an attention module with replaceable strategies to attend to objects in video frames automatically. Our method does not require any annotated data during training procedure. To produce sharp predictions, adversarial training is adopted in our work. We evaluate our model on the Moving MNIST and UCF101 datasets and report competitive results, compared to prior methods. The generated frames demonstrate that our model can characterize motion for different objects and produce plausible future frames.


Syntax-Directed Variational Autoencoder for Molecule Generation


title: Mu suppression as an index of sensorimotor contributions to speech processing: Evidence from continuous EEG signals

abstract: Abstract Mu rhythm suppression is an index of sensorimotor activity during the processing of sensory stimuli. Two present studies investigate the extent to which this measure is sensitive to differences in acoustic processing. In both studies, participants were required to listen to 90 second acoustic stimuli clips with their eyes closed and identify predetermined targets. Experimental conditions were designed to vary the acoustic processing demands. Mu suppression was measured continuously across central electrodes (C3, Cz, and C4). Ten adult females participated in the first study in which the target was a pseudoword presented in three conditions (identification, discrimination, discrimination in noise). Mu suppression was strongest and reached significance relative to baseline only in the discrimination in noise task at C3 (indicative of left hemisphere sensorimotor activity) when measured in a 10–12 Hz bandwidth. Thirteen adult females participated in the second study, which measured mu suppression to acoustic stimuli with ‘segmentation’ (i.e., separating a parsed stimulus into individual components) versus non-segmentation requirements in both speech and tone discrimination conditions. Significantly greater overall suppression to speech relative to tone tasks was found in the 10–12 Hz bandwidth. Further, suppression relative to baseline was significant only at C3 during the speech discrimination with segmentation task. Taken together, findings indicate that mu rhythm suppression in acoustic processing is sensitive to dorsal stream processing. More specifically, it is sensitive to (1) increases in overall processing demands and (2) processing linguistic versus non-linguistic information.


title: Adolescent Internet Use: What We Expect, What Teens Report.

abstract: As adolescent Internet use grew exponentially in the last decade, with it emerged a number of correspondent expectations. Among them were the following: (1) that gender predicts usage, i.e., that boys spend more time online, surfing the web and playing violent games, while girls chat or shop online; (2) that Internet use causes social isolation and depression, especially for teens; and (3) that adolescents use the Internet for anonymous identity experimentation. These expectations were based on research with earlier technologies when the Internet was less diffused in the adolescent population. By means of highly detailed daily reports of adolescents’ home Internet usage and peer-related adjustment, the present research sought to compare these expectations with the actual experiences of early and mid-adolescents in 2000 and 2001. Participants were 261 7th and 10th graders from suburban California public schools who completed four consecutive end-of-day reports on their school-based adjustment and Internet activity (including detailed logs of instant messages). Results challenge prevailing expectations regarding gender, well-being, and identity play. For the most part, adolescent boys’ and girls’ online activities have become more similar than different. On average, boys and girls alike described their online social interaction as (1) occurring in private settings such as e-mail and instant messages, (2) with friends who are also part of their daily, offline lives, and (3) devoted to fairly ordinary yet intimate topics (e.g., friends, gossip). No associations were found between Internet usage and well-being. Online pretending was reported to be motivated by a desire to play a joke on friends more often than to explore a desired or future identity, but participants reported a range of pretending content, contexts, and motives.


title: Autonomous driving at Ulm University: A modular, robust, and sensor-independent fusion approach

abstract: The project “Autonomous Driving” at Ulm University aims at advancing highly-automated driving with close-to-market sensors while ensuring easy exchangeability of the particular components. In this contribution, the experimental vehicle that was realized during the project is presented along with its software modules. To achieve the mentioned goals, a sophisticated fusion approach for robust environment perception is essential. Apart from the necessary motion planning algorithms, this paper thus focuses on the sensor-independent fusion scheme. It allows for an efficient sensor replacement and realizes redundancy by using probabilistic and generic interfaces. Redundancy is ensured by utilizing multiple sensors of different types in crucial modules like grid mapping, localization and tracking. Furthermore, the combination of the module outputs to a consistent environment model is achieved by employing their probabilistic representation. The performance of the vehicle is discussed using the experience from numerous autonomous driving tests on public roads.


title: Progressive Neural Architecture Search

abstract: We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms. Our approach uses a sequential model-based optimization (SMBO) strategy, in which we search for structures in order of increasing complexity, while simultaneously learning a surrogate model to guide the search through structure space. Direct comparison under the same search space shows that our method is up to 5 times more efficient than the RL method of Zoph et al. (2018) in terms of number of models evaluated, and 8 times faster in terms of total compute. The structures we discover in this way achieve state of the art classification accuracies on CIFAR-10 and ImageNet.


title: Magnesium as a biodegradable and bioabsorbable material for medical implants

abstract: For many years, stainless steel, cobalt-chromium, and titanium alloys have been the primary biomaterials used for load-bearing applications. However, as the need for structural materials in temporary implant applications has grown, materials that provide short-term structural support and can be reabsorbed into the body after healing are being sought. Since traditional metallic biomaterials are typically biocompatible but not biodegradable, the potential for magnesium-based alloys in biomedical applications has gained more interest. This paper summarizes the history and current status of magnesium as a bioabsorbable implant material. Also discussed is the development of a magnesium-zinc-calcium alloy that demonstrates promising degradation behavior.


title: Model-guided and inertia-driven directional minimal path for coronary centerline extraction

abstract: Extracting centerlines of coronary arteries is a challenging but important task in clinical applications of cardiac computed tomography angiography (CTA). In this paper, we propose an inertia-driven path tracking method based on the directional minimal path (DMP) to automatically extract the coronary centerlines. In DMP, the path tracing is divided into a number of segments. The proposed method adopts the previous segment in the DMP to form the inertia-driven force after the first segment, and thereby increase the robustness of the DMP. To trace the coronary path deeply and thus to obtain a long centerline, we construct a coronary model combining multiple subjects and extract the longest coronary centerline from a branch with a new end-point determination criterion. We evaluated the performance of this method using the Rotterdam Coronary Artery Algorithm Evaluation Framework. The average inside of our centerline extraction results was 0.38mm and the overlap was 83.7%.


title: Fuzzy logic approach to autonomous car parking using MATLAB

abstract: This paper discusses a system that parks automatically an automobile given certain conditions and making decisions based in fuzzy logic. It proposes three models in cascade in order to achieve a "decision-action" approach, so that the car in question does not need an operator.


Security for volatile FPGAs


title: Reading Between the Lines: Content-Agnostic Detection of Spear-Phishing Emails

abstract: Spear-phishing is an effective attack vector for infiltrating companies and organisations. Based on the multitude of personal information available online, an attacker can craft seemingly legit emails and trick his victims into opening malicious attachments and links. Although anti-spoofing techniques exist, their adoption is still limited and alternative protection approaches are needed. In this paper, we show that a sender leaves content-agnostic traits in the structure of an email. Based on these traits, we develop a method capable of learning profiles for a large set of senders and identifying spoofed emails as deviations thereof. We evaluate our approach on over 700,000 emails from 16,000 senders and demonstrate that it can discriminate thousands of senders, identifying spoofed emails with 90% detection rate and less than 1 false positive in 10,000 emails. Moreover, we show that individual traits are hard to guess and spoofing only succeeds if entire emails of the sender are available to the attacker.


title: iPads in early education: separating assumptions and evidence

abstract: Since their first appearance in 2010, iPads and other comparable tablets have been heralded for their potential to revolutionize education, including that of young children. Like previous multimedia technologies (e.g., whiteboards, Kozma, 1991), iPads are multimodal, allowing users to use texts, pictures, and sounds. In comparison with other, so far available multimedia technologies, iPads have three novel features which have the potential to make a positive difference to early education: iPads are portable and light-weight (unlike netbooks and laptops), they eliminate the need for separate input devices requiring certain levels of dexterity (such as mouse and keyboard) and thirdly, they are specifically designed to accommodate a number of apps, many of which have a child-friendly intuitive design. With several of these apps, iPads provide unprecedented opportunities for children to create their own contents and participate in rich and dynamic learning contexts. ::: ::: Yet, despite the possible benefits, there is an absence of research supporting the enthusiastic claims that iPads will “revolutionize education” (e.g., Ferenstein, 2011). This is due to several reasons but in early education, two prevalent myths concerning new technologies hinder research progress and innovation in practice: technological determinism and the digital/non-digital binary. This article outlines how these technology myths relate to the emerging iPad research in early education of children aged between 2 and 8 years old. After a critical assessment of the assumptions underlying some of the studies, attention is turned to a “second wave” of iPads' research which avoids these conceptual obstacles. Recommendations for future research are provided throughout the article, with the aim of provoking a wider debate regarding some of the identified issues.


title: Task-relevant grasp selection: A joint solution to planning grasps and manipulative motion trajectories

abstract: This paper addresses the problem of jointly planning both grasps and subsequent manipulative actions. Previously, these two problems have typically been studied in isolation, however joint reasoning is essential to enable robots to complete real manipulative tasks. In this paper, the two problems are addressed jointly and a solution that takes both into consideration is proposed. To do so, a manipulation capability index is defined, which is a function of both the task execution waypoints and the object grasping contact points. We build on recent state-of-the-art grasp-learning methods, to show how this index can be combined with a likelihood function computed by a probabilistic model of grasp selection, enabling the planning of grasps which have a high likelihood of being stable, but which also maximise the robot's capability to deliver a desired post-grasp task trajectory. We also show how this paradigm can be extended, from a single arm and hand, to enable efficient grasping and manipulation with a bi-manual robot. We demonstrate the effectiveness of the approach using experiments on a simulated as well as a real robot.


title: Substrate Integrated Magneto-Electric Dipole Antenna for 5G Wi-Fi

abstract: In this communication, a new technique—tapered H-shaped ground, is proposed to reduce the thickness of a magneto-electric (ME) dipole antenna. By employing the proposed technique, the antenna height of the ME dipole antenna is reduced from 0.25 to 0.11 $\lambda_{{\bf 0}}$ (where $\lambda_{{\bf 0}}$ is the wavelength of 5.5 GHz). This new antenna structure can be easily realized by the multi-layer printed circuit board (PCB) technology, which is low cost and easy to fabricate. Measured results show that this antenna has an impedance bandwidth of 18.74% for ${\rm VSWR} \le 2$ (4.98 to 6.01 GHz). Stable radiation patterns with low back radiation and low cross polarization radiation are achieved across the entire operating bandwidth for 5G Wi-Fi.


title: Repetitive high voltage all-solid-state Marx generator for dielectric barrier discharge pulsed plasma

abstract: Dielectric barrier discharge (DBD) is used in plasma technology with wide applications, such as ozone generation, depollution of gas stream and surface treatment of plastics. Commonly used power supplies for DBD generate sinusoidal voltages with frequency up to 100 kHz. Recent research shows that apart from a primary discharge occurring at the rising front or during the pulse top, a secondary discharge occurs at the end of the falling voltage flank when unipolar square pulses are used. This excitation method of DBDs improves energy efficiency significantly compared with sinusoidal excitation. In this paper, an all-solid-state Marx generator that can generate nearly rectangular pulses with a DBD load is proposed. It operates at an output voltage of 30 kV, a repetition frequency of 1 kHz and 1 kV DC input voltage successfully. Both the rise and fall times are less than 200 ns, and a maximum pulse-width of 1 us is obtained. A transformer isolated gate drive circuit is employed. This prototype has been applied in gas treatment research using DBD. Preliminary experimental results show that it is able to deliver almost rectangular voltage pulse to resistive load and capacitive load. When driving a DBD load, it can output large current at the rising of a pulse.


title: DISCUSSION OF FUNCTIONAL DESIGN OPTIONS FOR ONLINE RATING SYSTEMS:

abstract: Ratings are important for building up trust among different parties. Since the arrival of the internet era in the 1990s, countless online rating systems have emerged. For example, Amazon.com established a rating system for books and other products in 1995. Today online rating can be found everywhere, be it e-commerce sites, social networks, and information or recommendation platforms. In most cases, users provide the input to these systems which is then aggregated and directed to appropriate recipients. The increasing relevance of these rating systems forms a new distinct research field with a growing need for research on the design, effects, and validity of rating systems. Hence, we contribute to the body of knowledge by conducting a thorough analysis of the state of the art of online rating systems. We especially focus on the functional design options by analyzing 102 different systems with the help of a criteria catalog of 237 criteria. In this paper, we discuss an excerpt of our findings and present a morphological box that categorizes functional design options for online rating systems.


title: Automated scalable segmentation of neurons from multispectral images

abstract: Reconstruction of neuroanatomy is a fundamental problem in neuroscience. Stochastic expression of colors in individual cells is a promising tool, although its use in the nervous system has been limited due to various sources of variability in expression. Moreover, the intermingled anatomy of neuronal trees is challenging for existing segmentation algorithms. Here, we propose a method to automate the segmentation of neurons in such (potentially pseudo-colored) images. The method uses spatio-color relations between the voxels, generates supervoxels to reduce the problem size by four orders of magnitude before the final segmentation, and is parallelizable over the supervoxels. To quantify performance and gain insight, we generate simulated images, where the noise level and characteristics, the density of expression, and the number of fluorophore types are variable. We also present segmentations of real Brainbow images of the mouse hippocampus, which reveal many of the dendritic segments.


title: A Papier-Mache Approach to Learning 3D Surface Generation

abstract: We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, our new shape generation framework, AtlasNet, comes with significant advantages, such as improved precision and generalization capabilities, and the possibility to generate a shape of arbitrary resolution without memory issues. We demonstrate these benefits and compare to strong baselines on the ShapeNet benchmark for two applications: (i) autoencoding shapes, and (ii) single-view reconstruction from a still image. We also provide results showing its potential for other applications, such as morphing, parametrization, super-resolution, matching, and co-segmentation.


title: Magnetoreception System in Honeybees (Apis mellifera)

abstract: Honeybees (Apis mellifera) undergo iron biomineralization, providing the basis for magnetoreception. We showed earlier the presence of superparamagnetic magnetite in iron granules formed in honeybees, and subscribed to the notion that external magnetic fields may cause expansion or contraction of the superparamagnetic particles in an orientation-specific manner, relaying the signal via cytoskeleton (Hsu and Li 1994). In this study, we established a size-density purification procedure, with which quantitative amount of iron granules was obtained from honey bee trophocytes and characterized; the density of iron granules was determined to be 1.25 g/cm3. While we confirmed the presence of superparamagnetic magnetite in the iron granules, we observed changes in the size of the magnetic granules in the trophycytes upon applying additional magnetic field to the cells. A concomitant release of calcium ion was observed by confocal microscope. This size fluctuation triggered the increase of intracellular Ca+2 , which was inhibited by colchicines and latrunculin B, known to be blockers for microtubule and microfilament syntheses, respectively. The associated cytoskeleton may thus relay the magnetosignal, initiating a neural response. A model for the mechanism of magnetoreception in honeybees is proposed, which may be applicable to most, if not all, magnetotactic organisms.


Semantic Cyberthreat Modelling


title: Human-robot collaboration in cellular manufacturing: Design and development

abstract: The challenge of this work is to study the design and development of human-robot collaboration (HRC) in cellular manufacturing. Based on the concept of human collaborative design, four main design factors are being identified and developed in an active HRC prototype production cell for cable harness assembly. Human collaborative design aims to optimize the system design for the advantage of collaboration between human and robots based on human considerations. Task modeling approach is developed to study and analyze the task in order to identify the collaboration tasks to develop the collaboration planning. In the collaboration safety development, five safety designs, cover both hardware and control design, are proposed and developed in the prototype system. Risk assessment is conducted to verify the safety design. Two main experiments were conducted as preliminary study to investigate mental workload in HRC. A multimodal information support system is developed in the study of man-machine interface in this work to provide a comprehensive human-robot interface to facilitate human operator. The system performance evaluation had proven the improvement of prototype production cell with HRC design for cellular manufacturing.


title: eb 2 00 4 Fujita ’ s very ampleness conjecture for singular toric varieties

abstract: We present a self-contained combinatorial approach to Fujita’s conjectures in the toric case. Our main new result is a generalization of Fujita’s very ampleness conjecture for toric varieties with arbitrary singularities. In an appendix, we use similar methods to give a new proof of an analogous toric generalization of Fujita’s freeness conjecture due to Fujino.


title: The population posterior and Bayesian modeling on streams

abstract: Many modern data analysis problems involve inferences from streaming data. However, streaming data is not easily amenable to the standard probabilistic modeling approaches, which require conditioning on finite data. We develop population variational Bayes, a new approach for using Bayesian modeling to analyze streams of data. It approximates a new type of distribution, the population posterior, which combines the notion of a population distribution of the data with Bayesian inference in a probabilistic model. We develop the population posterior for latent Dirichlet allocation and Dirichlet process mixtures. We study our method with several large-scale data sets.


title: Feature-oriented programming and the AHEAD tool suite

abstract: Feature oriented programming (FOP) is the study of feature mod ularity and its use in program synthesis. AHEAD is a theory of FOP that is based on a fundamental concept of generative programming that functions map programs. This enables the design of programs to be expressed compo sitionally as algebraic expressions, which are suited for automated analysis, manipulation, and program synthesis. This paper is a tutorial on FOP and AHEAD. We review AHEAD's theory and the tool set that implements it.


title: Performance of Non-Orthogonal Multiple Access (NOMA) in mmWave wireless communications for 5G networks

abstract: Among the key technologies that have been identified as capacity boosters for fifth generation - 5G - mobile networks, are millimeter wave (mmWave) transmissions and non-orthogonal multiple access (NOMA). The large amount of spectrum available at mmWave frequencies combined with a more effective use of available resources, helps improving the overall capacity. NOMA, unlike orthogonal multiple access (OMA) methods, allows sharing the same frequency resources at the same time, by implementing adaptive power allocation. In this paper we present a performance analysis of NOMA in mmWave cells, using OMA as a benchmark. The results show that up to 70% channel capacity gain can be achieved when using NOMA instead of OMA. Most of the NOMA studies to the date focus mainly on the capacity gain; therefore we also present an analysis of the required signal-to-interference-plus-noise ratio (SINR) in NOMA, needed to achieve a target block error rate (BLER). The results show that an average SINR penalty of 12 dB is present when choosing NOMA over OMA.


title: A comparative study on multi-person tracking using overlapping cameras

abstract: We present a comparative study for tracking multiple persons using cameras with overlapping views. The evaluated methods consist of two batch mode trackers (Berclaz et al, 2011, Ben-Shitrit et al, 2011) and one recursive tracker (Liem and Gavrila, 2011), which integrate appearance cues and temporal information differently. We also added our own improved version of the recursive tracker. Furthermore, we investigate the effect of the type of background estimation (static vs. adaptive) on tracking performance. Experiments are performed on two novel and challenging multi-person surveillance data sets (indoor, outdoor), made public to facilitate benchmarking. We show that our adaptation of the recursive method outperforms the other stand-alone trackers.


title: Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network

abstract: Accurate prediction of diabetes is an important issue in health prognostics. However, data overfitting degrades the prediction accuracy in diabetes prognosis. In this paper, a reliable prediction system for the disease of diabetes is presented using a dropout method to address the overfitting issue. In the proposed method, deep learning neural network is employed where fully connected layers are followed by dropout layers. The proposed neural network outperforms other state-of-art methods in better prediction scores for the Pima Indians Diabetes Data Set.


title: Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data

abstract: Medical image data is naturally distributed among clinical institutions. This partitioning, combined with security and privacy restrictions on medical data, imposes limitations on machine learning algorithms in clinical applications, especially for small and newly established institutions. We present InsuLearn: an intuitive and robust open-source (open-source code available at: https://github.com/DistributedML/InsuLearn) platform designed to facilitate distributed learning (classification and regression) on medical image data, while preserving data security and privacy. InsuLearn is built on ensemble learning, in which statistical models are developed at each institution independently and combined at secure coordinator nodes. InsuLearn protocols are designed such that the liveness of the system is guaranteed as institutions join and leave the network. Coordination is implemented as a cluster of replicated state machines, making it tolerant to individual node failures. We demonstrate that InsuLearn successfully integrates accurate models for horizontally partitioned data while preserving privacy.


title: Hierarchical Trajectory Planning of an Autonomous Car Based on the Integration of a Sampling and an Optimization Method

abstract: This paper presents a hierarchical trajectory planning based on the integration of a sampling and an optimization method for urban autonomous driving. To manage a complex driving environment, the upper behavioral trajectory planner searches the macro-scale trajectory to determine the behavior of an autonomous car by using environment models, such as traffic control device and objects. This planner infers reasonable behavior and provides it to the motion trajectory planner. For planning the behavioral trajectory, the sampling-based approach is used due to its advantage of a free-form cost function for discrete models of the driving environments and simplification of the searching area. The lower motion trajectory planner determines the micro-scale trajectory based on the results of the upper trajectory planning with the environment model. The lower planner strongly considers vehicle dynamics within the planned behavior of the behavioral trajectory. Therefore, the planning space of the lower planner can be limited, allowing for improvement of the efficiency of the numerical optimization of the lower planner to find the best trajectory. For the motion trajectory planning, the numerical optimization is applied due to its advantages of a mathematical model for the continuous elements of the driving environments and low computation to converge minima in the convex function. The proposed algorithms of the sampling-based behavioral and optimization-based motion trajectory were evaluated through experiments in various scenarios of an urban area.


title: Speech Recognition using MFCC

abstract: This paper describes an approach of speech recognition by using the Mel-Scale Frequency Cepstral Coefficients (MFCC) extracted from speech signal of spoken words. Principal Component Analysis is employed as the supplement in feature dimensional reduction state, prior to training and testing speech samples via Maximum Likelihood Classifier (ML) and Support Vector Machine (SVM). Based on experimental database of total 40 times of speaking words collected under acoustically controlled room, the sixteen-ordered MFCC extracts have shown the improvement in recognition rates significantly when training the SVM with more MFCC samples by randomly selected from database, compared with the ML. Keywords—Speech Signal, MFCC, SVM, ML I. INTRODUCTION PEECH recognition is the process of automatically recognizing the spoken words of person based on information in speech signal. Recognition technique makes it possible to the speaker's voice to be used in verifying their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information service, voice mail, security control for the confidential information areas, and remote access to computers. The acoustical parameters of spoken signal used in recognition tasks have been popularly studied and investigated, and being able to be categorized into two types of processing domain: First group is spectral based parameters and another is dynamic time series. The most popular spectral based parameter used in recognition approach is the Mel Frequency Cepstral Coefficients called MFCC (2,3). Due to its advantage of less complexity in implementation of feature extraction algorithm, only sixteen coefficients of MFCC corresponding to the Mel scale frequencies of speech Cepstrum are extracted from spoken word samples in database. All extracted MFCC samples are then statistically analyzed for principal components, at least two dimensions minimally required in further recognition performance evaluation.


title: Source normalized indicators of citation impact: an overview of different approaches and an empirical comparison

abstract: Different scientific fields have different citation practices. Citation-based bibliometric indicators need to normalize for such differences between fields in order to allow for meaningful between-field comparisons of citation impact. Traditionally, normalization for field differences has usually been done based on a field classification system. In this approach, each publication belongs to one or more fields and the citation impact of a publication is calculated relative to the other publications in the same field. Recently, the idea of source normalization was introduced, which offers an alternative approach to normalize for field differences. In this approach, normalization is done by looking at the referencing behavior of citing publications or citing journals. In this paper, we provide an overview of a number of source normalization approaches and we empirically compare these approaches with a traditional normalization approach based on a field classification system. We also pay attention to the issue of the selection of the journals to be included in a normalization for field differences. Our analysis indicates a number of problems of the traditional classification-system-based normalization approach, suggesting that source normalization approaches may yield more accurate results.


title: Reconsidering Network Effect Theory

abstract: According to Network Effect literature network externalities lead to market failure due to Pareto-inferior coordination results. We show that the assumptions and simplifications implicitly used for modeling standardization processes fail to explain the real-world variety of diffusion courses in today’s dynamic IT markets and derive requirements for a more general model of network effects. We argue that Agent-based Computational Economics provides a solid basis for meeting these requirements by integrating evolutionary models from Game Theory and Institutional Economics.


title: Work in progress: Teaching computational thinking in middle and high school

abstract: Computational thinking (CT) does not require profound knowledge of computer science (CS). Vice versa, it helps students organize and build up new knowledge around the core CS concepts they encounter on the daily basis. Furthermore, CT provides a set of problem-solving skills and enhances analytical abilities that are crucial for the young generation to succeed in modern world pervaded with technology. Teaching the principles of computing to students with non-computing tracks of study provides them with a competitive advantage and breaks down stereotypes around CS. Our approach is to introduce a topic related to world issues and students' interests while concealing its relation to CS until students' interest and attention are captured. The approach takes advantage of pattern-matching abilities that are naturally wired in our brain to improve understanding and make the obtained knowledge persist in memory. The paper discusses some of the modules developed as part of the Inspire-CT project that can be delivered in one or part of a class period at high school or middle school.


title: Is There a Common Summary Statistical Process for Representing the Mean and Variance? A Study Using Illustrations of Familiar Items

abstract: A number of studies revealed that our visual system can extract different types of summary statistics, such as the mean and variance, from sets of items. Although the extraction of such summary sta...


Foreign Body Granulomas after the Use of Dermal Fillers: Pathophysiology, Clinical Appearance, Histologic Features, and Treatment


Habit formation


title: On drone ground penetrating radar for landmine detection

abstract: Ground Penetrating Radar (GPR) system mounted on drone for landmine detection is presented. Classical methods for landmine detections are not time efficient and dangerous. With proposed solution a drone could autonomously scan desired area without contacting the investigated ground. For this purpose, a sub-nanosecond monocyclic pulse generator and Ultra-Wideband (UWB) antenna that is suitable for air coupled GPR system was developed. The preliminary laboratory experimental results show first success by object detection and are good basic for further development.


title: Instagram use and young women’s body image concerns and self-objectification: Testing mediational pathways:

abstract: This study examined the relationship between Instagram use (overall, as well as specifically viewing fitspiration images) and body image concerns and self-objectification among women between the ag...


title: Web spam identification through language model analysis

abstract: This paper applies a language model approach to different sources of information extracted from a Web page, in order to provide high quality indicators in the detection of Web Spam. Two pages linked by a hyperlink should be topically related, even though this were a weak contextual relation. For this reason we have analysed different sources of information of a Web page that belongs to the context of a link and we have applied Kullback-Leibler divergence on them for characterising the relationship between two linked pages. Moreover, we combine some of these sources of information in order to obtain richer language models. Given the different nature of internal and external links, in our study we also distinguished these types of links getting a significant improvement in classification tasks. The result is a system that improves the detection of Web Spam on two large and public datasets such as WEBSPAM-UK2006 and WEBSPAM-UK2007.


title: Energy storage and its use with intermittent renewable energy

abstract: A simple probabilistic method has been developed to predict the ability of energy storage to increase the penetration of intermittent embedded renewable generation (ERG) on weak electricity grids and to enhance the value of the electricity generated by time-shifting delivery to the network. This paper focuses on the connection of wind generators at locations where the level of ERG would be limited by the voltage rise. Short-term storage, covering less than 1 h, offers only a small increase in the amount of electricity that can be absorbed by the network. Storage over periods of up to one day delivers greater energy benefits, but is significantly more expensive. Different feasible electricity storage technologies are compared for their operational suitability over different time scales. The value of storage in relation to power rating and energy capacity has been investigated so as to facilitate appropriate sizing.


title: Soft tissue contouring using autologous tissue for hemifacial atrophy

abstract: Numerous methods for the restoration of facial symmetry in patients with hemifacial atrophy have been described. Most recently, the use of autologous tissue for augmentation has been employed. Experience gained from the use of free-tissue transplantation with microvascular anastomosis and injection of liposuctioned fat are presented in this paper. In 19 patients, reconstruction was performed using microsurgical tissue transfer, while in 3 patients, autologous fat transplantation alone was used. Indications, operative techniques together with the advantages as well as disadvantages associated with each procedure are described.


title: Ubii: Physical World Interaction Through Augmented Reality

abstract: We describe a new set of interaction techniques that allow users to interact with physical objects through augmented reality (AR). Previously, to operate a smart device, physical touch is generally needed and a graphical interface is normally involved. These become limitations and prevent the user from operating a device out of reach or operating multiple devices at once. Ubii (Ubiquitous interface and interaction) is an integrated interface system that connects a network of smart devices together, and allows users to interact with the physical objects using hand gestures. The user wears a smart glass which displays the user interface in an augmented reality view. Hand gestures are captured by the smart glass, and upon recognizing the right gesture input, Ubii will communicate with the connected smart devices to complete the designated operations. Ubii supports common inter-device operations such as file transfer, printing, projecting, as well as device pairing. To improve the overall performance of the system, we implement computation offloading to perform the image processing computation. Our user test shows that Ubii is easy to use and more intuitive than traditional user interfaces. Ubii shortens the operation time on various tasks involving operating physical devices. The novel interaction paradigm attains a seamless interaction between the physical and digital worlds.


title: Bayesian robot system identification with input and output noise ✩

abstract: For complex robots such as humanoids, model-based control is highly beneficial for accurate tracking while keeping negative feedback gains low for compliance. However, in such multi degree-of-freedom lightweight systems, conventional identification of rigid body dynamics models using CAD data and actuator models is inaccurate due to unknown nonlinear robot dynamic effects. An alternative method is data-driven parameter estimation, but significant noise in measured and inferred variables affects it adversely. Moreover, standard estimation procedures may give physically inconsistent results due to unmodeled nonlinearities or insufficiently rich data. This paper addresses these problems, proposing a Bayesian system identification technique for linear or piecewise linear systems. Inspired by Factor Analysis regression, we develop a computationally efficient variational Bayesian regression algorithm that is robust to ill-conditioned data, automatically detects relevant features, and identifies input and output noise. We evaluate our approach on rigid body parameter estimation for various robotic systems, achieving an error of up to three times lower than other state-of-the-art machine learning methods.


title: Key Data Quality Issues for Enterprise Asset Management in Engineering Organisations

abstract: Data Quality (DQ) is a critical issue for effective asset management. DQ problems can result in severe negative consequences for an organisation. Several research studies have indicated that most organizations have DQ problems. In response to these problems, organisations have developed various policies (e.g. fair use of data, information privacy, etc). However, it is often difficult to implement these policies at the tacit and operational level, especially identifying the areas or problems to focus on. Through a case study with two state-wide Australian utility companies, this paper tries to present the DQ issues which emerged from a number of asset management processes. The findings are thought to be valuable for practitioners, especially policy implementers, who want to align policy with real-world business.


title: Temporal Segmentation of Egocentric Videos to Highlight Personal Locations of Interest

abstract: With the increasing availability of wearable cameras, the acquisition of egocentric videos is becoming common in many scenarios. However, the absence of explicit structure in such videos (e.g., video chapters) makes their exploitation difficult. We propose to segment unstructured egocentric videos to highlight the presence of personal locations of interest specified by the end-user. Given the large variability of the visual content acquired by such devices, it is necessary to design explicit rejection mechanisms able to detect negatives (i.e., frames not related to any considered location) learning only from positive ones at training time. To challenge the problem, we collected a dataset of egocentric videos containing 10 personal locations of interest. We propose a method to segment egocentric videos performing discrimination among the personal locations of interest, rejection of negative frames, and enforcing temporal coherence between neighboring predictions.


title: Citation based plagiarism detection: a new approach to identify plagiarized work language independently

abstract: This paper describes a new approach towards detecting plagiarism and scientific documents that have been read but not cited. In contrast to existing approaches, which analyze documents' words but ignore their citations, this approach is based on citation analysis and allows duplicate and plagiarism detection even if a document has been paraphrased or translated, since the relative position of citations remains similar. Although this approach allows in many cases the detection of plagiarized work that could not be detected automatically with the traditional approaches, it should be considered as an extension rather than a substitute. Whereas the known text analysis methods can detect copied or, to a certain degree, modified passages, the proposed approach requires longer passages with at least two citations in order to create a digital fingerprint.


title: Balancing platform control and external contribution in third-party development: the boundary resources model

abstract: Prior research documents the significance of using platform boundary resources e.g. application programming interfaces for cultivating platform ecosystems through third-party development. However, there are few, if any, theoretical accounts of this relationship. To this end, this paper proposes a theoretical model that centres on two drivers behind boundary resources design and use - resourcing and securing - and how these drivers interact in third-party development. We apply the model to a detailed case study of Apple's iPhone platform. Our application of the model not only serves as an illustration of its plausibility but also generates insights about the conflicting goals of third-party development: the maintenance of platform control and the transfer of design capability to third-party developers. We generate four specialised constructs for understanding the actions taken by stakeholders in third-party development: self-resourcing, regulation-based securing, diversity resourcing and sovereignty securing. Our research extends and complements existing platform literature and contributes new knowledge about an alternative form of system development.


title: An improved Prewitt algorithm for edge detection based on noised image

abstract: In this paper, an improved Prewitt algorithm for edge detection is proposed for the reason that the traditional Prewitt edge detection algorithm is sensitive to the noise. The traditional Prewitt edge detection operator only has two templates with horizontal and vertical directions. While the edge is in a plurality of directions, so operator with eight templates of different directions is put forward and it can detect more edges. In order to improve the capability of resisting noise, this paper put forward three improvements. First of all, the mean value rather than the maximum value of the gradient magnitude of the eight directions is used as the final gradient magnitude. Secondly, OTSU automatic threshold is used to set the gradient magnitude threshold. Again, an 8-neighborhood template is proposed to remove the isolated single pixel noise. The experimental results show that the improved algorithm improves the anti noise performance greatly, and detects the edges of the random noised image effectively.


title: CalibMe: Fast and Unsupervised Eye Tracker Calibration for Gaze-Based Pervasive Human-Computer Interaction

abstract: As devices around us become smart, our gaze is poised to become the next frontier of human-computer interaction (HCI). State-of-the-art mobile eye tracker systems typically rely on eye-model-based gaze estimation approaches, which do not require a calibration. However, such approaches require specialized hardware (e.g., multiple cameras and glint points), can be significantly affected by glasses, and, thus, are not fit for ubiquitous gaze-based HCI. In contrast, regression-based gaze estimations are straightforward approaches requiring solely one eye and one scene camera but necessitate a calibration. Therefore, a fast and accurate calibration is a key development to enable ubiquitous gaze-based HCI. In this paper, we introduce CalibMe, a novel method that exploits collection markers (automatically detected fiducial markers) to allow eye tracker users to gather a large array of calibration points, remove outliers, and automatically reserve evaluation points in a fast and unsupervised manner. The proposed approach is evaluated against a nine-point calibration method, which is typically used due to its relatively short calibration time and adequate accuracy. CalibMe reached a mean angular error of 0.59 (0=0.23) in contrast to 0.82 (0=0.15) for a nine-point calibration, attesting for the efficacy of the method. Moreover, users are able to calibrate the eye tracker anywhere and independently in - 10 s using a cellphone to display the collection marker.