title: Primary melanoma of the vagina. A clinical case

abstract: Primary melanoma of the vagina is a rare neoplasm that appears in the 6th and 7th decades of life. It has a poor prognosis, for which there is no consensus regarding treatment; indeed, the literature describes a number of therapeutic options. This paper describes a patient with vaginal melanoma treated by local excision and immunotherapy.


title: Letter to Neuroscience INCREASED SOCIAL FEAR AND DECREASED FEAR OF OBJECTS IN MONKEYS WITH NEONATAL AMYGDALA LESIONS

abstract: Abstract The amygdala has been implicated in the mediation of emotional and species-specific social behavior ( Kling et al., 1970 , Kling and Brothers, 1992 , Kluver and Bucy, 1939 , Rosvold et al., 1954 ). Humans with bilateral amygdala damage are impaired in judging negative emotion in facial expressions and making accurate judgements of trustworthiness ( Adolphs et al., 1998 , Adolphs et al., 1994 ). Amygdala dysfunction has also been implicated in human disorders ranging from social anxiety ( Birbaumer et al., 1998 ) to depression ( Drevets, 2000 ) to autism ( Bachevalier, 1994 , Baron-Cohen et al., 2000 , Bauman and Kemper, 1993 ). We produced selective amygdala lesions in 2-week-old macaque monkeys who were returned to their mothers for rearing. At 6–8 months of age, the lesioned animals demonstrated less fear of novel objects such as rubber snakes than age-matched controls. However, they displayed substantially more fear behavior than controls during dyadic social interactions. These results suggest that neonatal amygdala lesions dissociate a system that mediates social fear from one that mediates fear of inanimate objects. Furthermore, much of the age-appropriate repertoire of social behavior was present in amygdala-lesioned infants indicating that these lesions do not produce autistic-like behavior in monkeys. Finally, amygdala lesions early in development have different effects on social behavior than lesions produced in adulthood.


title: Compact and Low-Profile Textile EBG-Based Antenna for Wearable Medical Applications

abstract: A compact wearable antenna with a novel miniaturized electromagnetic bandgap (EBG) structure at 2.4 GHz for medical application is presented in this letter. The design demonstrates a robust, compact, and low-profile solution to meet the requirements of wearable applications. The EBG structure reduces the back radiation and the impact of frequency detuning due to the high losses of human body. In addition, the structure improves the front-to-back ratio (FBR) by 15.5 dB. The proposed compact antenna with dimensions of ${\text{46}}\,\times \,{\text{46}}\,\times \,{\text{2.4 mm}}^{3}$ yields an impedance bandwidth of 27% (2.17–2.83 GHz), with a gain enhancement of 7.8 dBi and more than 95% reduction in the specific absorption rate. Therefore, the antenna is a promising candidate for integration into wearable devices applied in various domains, specifically biomedical technology.


title: Sustainable supply chain management: a modeling perspective

abstract: Nowadays, the integration of sustainability into supply chain management (SCM) is a key issue for ensuring corporate competitiveness in face of dynamic ecological and social environments. This paper reviews 185 journal publications of the last 20 years that formalize issues related to sustainable supply chain management (SSCM) in quantitative models. In a content analysis, modeling and SCM characteristics as well as sustainability and SSCM constructs are elaborated. The models are assessed numerically by counting frequencies of occurrence and by clustering the paper sample according to selected characteristics. The findings indicate that SSCM models predominantly focus on deterministic approaches and the integration of environmental aspects of sustainability while neglecting stochastic modeling techniques and the consideration of social factors. By now, comprehensive modeling approaches are most often employed on intra-organizational levels whereas broader application areas are assessed by less complex models. The integration of pressures and incentives of external stakeholders or the formalization of sustainable supplier management and sustainability risks are identified as future research perspectives. Furthermore, the interrelationships between the triple bottom line dimensions are to be scrutinized in greater detail in order to avoid focused optimization of selected sustainability criteria. Seven modeling guidelines are derived from the reviewed literature to facilitate future model-based SSCM research. Copyright Springer Science+Business Media New York 2015


title: Loper: A quadruped-hybrid stair climbing robot

abstract: The purpose of this paper is to describe the Loper, a multi-purpose robotic platform under development at the University of Minnesota's Center for Distributed Robotics. Leper's unique Tri-lobe wheel design and highly compliant chassis make the platform especially suited for overcoming many of the challenges associated with search operations in urban settings. The mechanically simple design and use of commercially available components make Loper easily maintainable. The platform also features long operational time, onboard sensor processing, dedicated motion control, and four reconfigurable sensor bays.


title: Decision support system for servitization of industrial SMEs: a modelling and simulation approach

abstract: Industry servitisation, i.e. the manufacturing industry transition towards the integration of services, provides for strong opportunities of new business model development and implementation. However, managing the industrial transformation to such business models means decision-makers are confronted with high decisional complexity. Such dramatic changes affect most enterprise performance drivers, and the interactions of the multiple business and industrial factors influencing the overall company performance make it very difficult for managers to specify consistent and efficient industrial configurations for their business processes. This context explains a concrete need for developing servitisation-dedicated decision support systems (DSS). The objective of the decision aid developed in this paper is to provide operational support which integrates the specific business and industrial features of so-called ‘product–service systems’ in order to study the interactions of different types of performance factors...


title: Do Deep Nets Really Need to be Deep? *** Draft for NIPS 2014 (not camera ready copy) ***

abstract: Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. In this paper we empirically demonstrate that shallow feed-forward nets can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Moreover, in some cases the shallow nets can learn these deep functions using the same number of parameters as the original deep models. On the TIMIT phoneme recognition and CIFAR-10 image recognition tasks, shallow nets can be trained that perform similarly to complex, well-engineered, deeper convolutional models.


title: Hybrid Evolutionary Algorithms for Graph Coloring

abstract: A recent and very promising approach for combinatorial optimization is to embed local search into the framework of evolutionary algorithms. In this paper, we present such hybrid algorithms for the graph coloring problem. These algorithms combine a new class of highly specialized crossover operators and a well-known tabu search algorithm. Experiments of such a hybrid algorithm are carried out on large DIMACS Challenge benchmark graphs. Results prove very competitive with and even better than those of state-of-the-art algorithms. Analysis of the behavior of the algorithm sheds light on ways to further improvement.


title: Optimal Performance of Epidermal Antennas for UHF Radio Frequency Identification and Sensing

abstract: Skin-mounted electronics is the new frontier for unobtrusive body-centric monitoring systems. In designing the wireless devices to be placed in direct contact with the human skin, the presence of the lossy body cannot be ignored because of strong electromagnetic interactions. In this paper, the performance of epidermal antennas, for application to radio frequency identification (RFID) links in the UHF band, was investigated by means of numerical simulations and laboratory tests on fabricated prototypes. The analysis demonstrates the existence of an optimal size of the antennas (from 3 to 6 cm for loops and from 6 to 15 cm for dipoles) and of upper bounds in the achievable radiation gain (less than −10 dB in the case of 0.5 mm thick application substrates) as a consequence of the balance between the two opposing mechanisms of radiation and loss. This behavior, which is controlled by the hosting medium, does not depend on the antenna shape, even if the loop layout permits considerably minimizing the device size. Even the conductivity of the antenna trace plays only a second-order role; low-cost inkjet printable paints with conductivity higher than 104 S/m are suitable to provide radiation performance comparable with the performance of copper-made antennas. Starting from the investigation of the above cited physical phenomena, including the effect of common classes of suitable substrate membranes, guidelines are finally derived for the optimal design of real RFID epidermal antennas.


title: Deep Learning for Epidemiological Predictions

abstract: Predicting new and urgent trends in epidemiological data is an important problem for public health, and has attracted increasing attention in the data mining and machine learning communities. The temporal nature of epidemiology data and the need for real-time prediction by the system makes the problem residing in the category of time-series forecasting or prediction. While traditional autoregressive (AR) methods and Gaussian Process Regression (GPR) have been actively studied for solving this problem, deep learning techniques have not been explored in this domain. In this paper, we develop a deep learning framework, for the first time, to predict epidemiology profiles in the time-series perspective. We adopt Recurrent Neural Networks (RNNs) to capture the long-term correlation in the data and Convolutional Neural Networks (CNNs) to fuse information from data of different sources. A residual structure is also applied to prevent overfitting issues in the training process. We compared our model with the most widely used AR models on USA and Japan datasets. Our approach provides consistently better results than these baseline methods.


title: Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network

abstract: Many cluster-based routing techniques for Wireless Sensor Networks (WSNs) have been proposed in the literature. However, most of the proposed protocols emphasized on the Cluster Head (CH) selection ignoring how the CHs will send the aggregated data back to the Base Station (BS). Furthermore, they tend to use non-realistic parameters and assumptions. Such examples include the use of infinite transmission range and location awareness. They also used an energy model that is fundamentally flawed for modelling radio power consumption in sensor networks. In this paper, two Linear Programming (LP) formulations to the problems of clustering and routing are presented followed by two proposed algorithms for the same based on Particle Swarm Optimization (PSO). The clustering algorithm finds the optimal set of CHs that maximize the energy efficiency, cluster quality and network coverage. The routing algorithm is developed with a novel particle encoding scheme and fitness function to find the optimal routing tree that connects these CHs to the BS. These two algorithms are then combined into a two-tier protocol to provide a complete and practical clustering model. The effect of using a realistic network and energy consumption model in cluster-based communication for WSN will be investigated. Extensive simulations on 50 homogeneous and heterogeneous WSN models are evaluated and compared against well-known cluster-based sensor network protocols. The results demonstrate that the proposed protocol performs better than such protocols in terms of various performance metrics such as scalability, Packet Delivery Rate (PDR) at the CHs and delivery of total data packets to the BS. HighlightsTwo LP formulations to the problems of clustering and routing in Wireless Sensor Networks are presented.PSO-based clustering protocol with a trade-off between energy efficiency, network coverage and data transmission reliability.PSO-based routing protocol with a novel particle encoding scheme for complete routing tree solution.Investigate the result of using realistic network settings.


title: Robotic assistants in therapy and education of children with autism: can a small humanoid robot help encourage social interaction skills?

abstract: This article presents a longitudinal study with four children with autism, who were exposed to a humanoid robot over a period of several months. The longitudinal approach allowed the children time to explore the space of robot–human, as well as human–human interaction. Based on the video material documenting the interactions, a quantitative and qualitative analysis was conducted. The quantitative analysis showed an increase in duration of pre-defined behaviours towards the later trials. A qualitative analysis of the video data, observing the children’s activities in their interactional context, revealed further aspects of social interaction skills (imitation, turn-taking and role-switch) and communicative competence that the children showed. The results clearly demonstrate the need for, and benefits of, long-term studies in order to reveal the full potential of robots in the therapy and education of children with autism.


title: Strategic performance measurement in a healthcare organisation: A multiple criteria approach based on balanced scorecard

abstract: The Balanced Scorecard (BSC) methodology focuses on major critical issues of modern business organisations: the effective measurement of corporate performance and the evaluation of the successful implementation of corporate strategy. Despite the increased adoption of the BSC methodology by numerous business organisations during the last decade, limited case studies concern non-profit organisations (e.g. public sector, educational institutions, healthcare organisations, etc.). The main aim of this study is to present the development of a performance measurement system for public health care organisations, in the context of BSC methodology. The proposed approach considers the distinguished characteristics of the aforementioned sector (e.g. lack of competition, social character of organisations, etc.). The proposed measurement system contains the most important financial performance indicators, as well as non-financial performance indicators that are able to examine the quality of the provided services, the satisfaction of internal and external customers, the self-improvement system of the organisation and the ability of the organisation to adapt and change. These indicators play the role of Key Performance Indicators (KPIs), in the context of BSC methodology. The presented analysis is based on a MCDA approach, where the UTASTAR method is used in order to aggregate the marginal performance of KPIs. This approach is able to take into account the preferences of the management of the organisation regarding the achievement of the defined strategic objectives. The main results of the proposed approach refer to the evaluation of the overall scores for each one of the main dimensions of the BSC methodology (i.e. financial, customer, internal business process, and innovation-learning). These results are able to help the organisation to evaluate and revise its strategy, and generally to adopt modern management approaches in every day practise.


title: Bayesian methods for neural networks and related models

abstract: Models such as feed-forward neural networks and certain other structures investigated in the computer science literature are not amenable to closed-form Bayesian analysis. The paper reviews the various approaches taken to overcome this difficulty, involving the use of Gaussian approximations, Markov chain Monte Carlo simulation routines and a class of non-Gaussian but "deterministic" approximations called variational approximations.


title: Light Sheet Fluorescence Microscopy of Plant Roots Growing on the Surface of a Gel

abstract: One of the key questions in understanding plant development is how single cells behave in a larger context of the tissue. Therefore, it requires the observation of the whole organ with a high spatial- as well as temporal resolution over prolonged periods of time, which may cause photo-toxic effects. This protocol shows a plant sample preparation method for light-sheet microscopy, which is characterized by mounting the plant vertically on the surface of a gel. The plant is mounted in such a way that the roots are submerged in a liquid medium while the leaves remain in the air. In order to ensure photosynthetic activity of the plant, a custom-made lighting system illuminates the leaves. To keep the roots in darkness the water surface is covered with sheets of black plastic foil. This method allows long-term imaging of plant organ development in standardized conditions.


title: Friction Compensation of DC Motors for Precise Motion Control Using Disturbance Observer

abstract: Friction compensation is often neglected in DC servo control systems where precision is not a main concern. However, friction compensation is necessary to achieve precision of a motion control system. In this research work frictional components have been estimated using constant angular velocity motion test and by the use of the disturbance observer. Disturbance observer based friction compensator has been Modeled and developed model was implemented in hardware setup and evaluated using simulation and practical results.


WikiCoref: An English Coreference-annotated Corpus of Wikipedia Articles.


title: Security metrics for source code structures

abstract: Software security metrics are measurements to assess security related imperfections (or perfections) introduced during software development. A number of security metrics have been proposed. However, all the perspectives of a software system have not been provided specific attention. While most security metrics evaluate software from a system-level perspective, it can also be useful to analyze defects at a lower level, i.e., at the source code level. To address this issue, we propose some code-level security metrics which can be used to suggest the level of security of a code segment. We provide guidelines about where and how these metrics can be used to improve source code structures. We have also conducted two case studies to demonstrate the applicability of the proposed metrics.


title: Tunable Dual-Band Filter and Diplexer Based on Folded Open Loop Ring Resonators

abstract: Tunable dual-band filter and diplexer based on folded open loop ring resonators (OLRRs) are proposed in this brief. Two pairs of OLRRs of different center frequencies are utilized to couple with microstrip transmission lines in these two devices, and there is no extra matching network in the diplexer structure. Maximal magnetic coupling and high isolation can be obtained by placing the resonators at proper positions along the feeding lines. The passband/channel frequencies of the dual-band filter/diplexer can be tuned independently by varying dc bias voltages applied to the varactors loaded at one end of the resonators. In addition, a broad harmonic suppression can be obtained by choosing appropriate lumped elements adding to the OLRRs. The performances of the proposed devices can be verified by the simulated and measured results.


title: Usefulness, localizability, humanness, and language-benefit: additional evaluation criteria for natural language dialogue systems

abstract: Human---computer dialogue systems interact with human users using natural language. We used the ALICE/AIML chatbot architecture as a platform to develop a range of chatbots covering different languages, genres, text-types, and user-groups, to illustrate qualitative aspects of natural language dialogue system evaluation. We present some of the different evaluation techniques used in natural language dialogue systems, including black box and glass box, comparative, quantitative, and qualitative evaluation. Four aspects of NLP dialogue system evaluation are often overlooked: "usefulness" in terms of a user's qualitative needs, "localizability" to new genres and languages, "humanness" or "naturalness" compared to human---human dialogues, and "language benefit" compared to alternative interfaces. We illustrated these aspects with respect to our work on machine-learnt chatbot dialogue systems; we believe these aspects are worthwhile in impressing potential new users and customers.


title: The effects of Social Networking Site (SNS) use on college students' friendship and well-being

abstract: "Social" type SNS use was positively related to users' subjective well-being."Entertainment" SNS use was not related to users' subjective well-being.Online self-disclosure was positively related to friendship quality."Social" SNS use was inversely related to online self-disclosure. The present study was conducted with two goals in mind: (1) to examine the influence of using different types of SNS use on users' well-being, and (2) to examine the mediating roles of online self-disclosure and friendship quality in the relationship between types of SNS use and well-being. Participants were from two large 4-year undergraduate universities in Southwestern China. The study was conducted during Spring semester, 2013, using advertisements that described the nature of the research and indicated that compensation for participation was ? 10 (about $1.5 U.S.). Of the 402 students approached, 337 completed the survey (i.e., response rate was 83.83%). Structural equation modeling showed that "social" type SNS use was positively related to users' well-being, whereas "entertainment" type SNS use was not. In addition, online self-disclosure was a significant predictor of users' friendship quality. However, there was an inverse relationship between "social" SNS use and online self-disclosure, and no relationship between friendship quality based on SNS use and well-being. It should be noted that generalizations of our findings should be made cautiously. The cross-section design and self-reported usage of SNS would also be limitations. Experimental and longitudinal studies should be conducted to provide stronger evidence of causal relations among variables examined in this study.


The stasis and flow of behavior: toward a theory of goals.


title: Context- and Content-aware Embeddings for Query Rewriting in Sponsored Search

abstract: Search engines represent one of the most popular web services, visited by more than 85% of internet users on a daily basis. Advertisers are interested in making use of this vast business potential, as very clear intent signal communicated through the issued query allows effective targeting of users. This idea is embodied in a sponsored search model, where each advertiser maintains a list of keywords they deem indicative of increased user response rate with regards to their business. According to this targeting model, when a query is issued all advertisers with a matching keyword are entered into an auction according to the amount they bid for the query, and the winner gets to show their ad. One of the main challenges is the fact that a query may not match many keywords, resulting in lower auction value, lower ad quality, and lost revenue for advertisers and publishers. Possible solution is to expand a query into a set of related queries and use them to increase the number of matched ads, called query rewriting. To this end, we propose rewriting method based on a novel query embedding algorithm, which jointly models query content as well as its context within a search session. As a result, queries with similar content and context are mapped into vectors close in the embedding space, which allows expansion of a query via simple K-nearest neighbor search in the projected space. The method was trained on more than 12 billion sessions, one of the largest corpuses reported thus far, and evaluated on both public TREC data set and in-house sponsored search data set. The results show the proposed approach significantly outperformed existing state-of-the-art, strongly indicating its benefits and the monetization potential.


title: Fast Separation of Reflection Components using a Specularity-Invariant Image Representation

abstract: In this paper, we propose a fast method for separating reflection components using a single color image. We first propose a specular-free two-band image that is a specularity-invariant color image representation. Reflection components separation is achieved by comparing local ratios at each pixel and making those ratios equal in an iterative framework. The proposed method is very fast and shows reasonable results for textured indoor/outdoor images.


title: Transistor matching in analog CMOS applications

abstract: This paper gives an overview of MOSFET mismatch effects that form a performance/yield limitation for many designs. After a general description of (mis)matching, a comparison over past and future process generations is presented. The application of the matching model in CAD and analog circuit design is discussed. Mismatch effects gain importance as critical dimensions and CMOS power supply voltages decrease.


title: Industrial Collaborative Robot Design: a Guideline for Future Design Activity

abstract: This study explores the rapidly expanding Collaborative Robot market and the recent literature in social robotics. An attempt is made to interpret both resources to define macro-parameter trends that could be useful guidelines for robot designing. The market shows a trend towards anthropomorphism in collaborative robot design, though some studies suggest that functionality of robots (not sociality) should always be explicit and evident in their design. An unknown area of convergence between these two trends is hypothesised; here hides the most anthropomorphic design accepted by human collaborators.


title: Robust Deep Reinforcement Learning with Adversarial Attacks

abstract: This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter uncertainties with the help of these attacks. We show that even a naively engineered attack successfully degrades the performance of DRL algorithm. We further improve the attack using gradient information of an engineered loss function which leads to further degradation in performance. These attacks are then leveraged during training to improve the robustness of RL within robust control framework. We show that this adversarial training of DRL algorithms like Deep Double Q learning and Deep Deterministic Policy Gradients leads to significant increase in robustness to parameter variations for RL benchmarks such as Cart-pole, Mountain Car, Hopper and Half Cheetah environment.


title: Detecting Online Harassment in Social Networks

abstract: Online Harassment is the process of sending messages for example in Social Networks to cause psychological harm to a victim. In this paper, we propose a pattern-based approach to detect such messages. Since user generated texts contain noisy language we perform a normalization step first to transform the words into their canonical forms. Additionally, we introduce a person identification module that marks phrases which relate to a person. Our results show that these preprocessing steps increase the classification performance. The pattern-based classifier uses the information provided by the preprocessing steps to detect patterns that connect a person to profane words. This technique achieves a substantial improvement compared to existing approaches. Finally, we discuss the portability of our approach to Social Networks and its possible contribution to tackle the abuse of such applications for the distribution of Online Harassment.


title: Unrestricted Adversarial Examples

abstract: We introduce a two-player contest for evaluating the safety and robustness of machine learning systems, with a large prize pool. Unlike most prior work in ML robustness, which studies norm-constrained adversaries, we shift our focus to unconstrained adversaries. Defenders submit machine learning models, and try to achieve high accuracy and coverage on non-adversarial data while making no confident mistakes on adversarial inputs. Attackers try to subvert defenses by finding arbitrary unambiguous inputs where the model assigns an incorrect label with high confidence. We propose a simple unambiguous dataset ("bird-or- bicycle") to use as part of this contest. We hope this contest will help to more comprehensively evaluate the worst-case adversarial risk of machine learning models.


title: Factors That Influence the Efficiency of a Propeller-Type Tribocharging Device for Granular Plastics

abstract: This work is focused on the experimental study of a new propeller-type tribocharging device specifically designed to operate in conjunction with an electrostatic separator for mixtures of granular insulating materials. The device consists of two coaxial aluminum propellers rotating in a polyvinyl chloride (PVC) pipe; it has two control variables: the material feed-rate and the rotation speed of the propellers. Virtual instrumentation using LABVIEW software is used for measuring the mass and the electric charge of the tribocharged samples of four types of polymers: polypropylene (PP); high impact polystyrene (HIPS); high-density polyethylene (HDPE); and PVC. In accordance with the sign and the magnitude of the charge/mass ratio, the four polymers are arranged in a triboelectric series, starting from the negative polarity: PVC—HDPE—PP—HIPS. The effectiveness of the tribocharging process is validated by using a free fall electrostatic separator. The best results (purities and recoveries higher than 90%) are obtained in the case of PVC/HIPS granular mixtures.


title: Model-Free Deep Inverse Reinforcement Learning by Logistic Regression

abstract: This paper proposes model-free deep inverse reinforcement learning to find nonlinear reward function structures. We formulate inverse reinforcement learning as a problem of density ratio estimation, and show that the log of the ratio between an optimal state transition and a baseline one is given by a part of reward and the difference of the value functions under the framework of linearly solvable Markov decision processes. The logarithm of density ratio is efficiently calculated by binomial logistic regression, of which the classifier is constructed by the reward and state value function. The classifier tries to discriminate between samples drawn from the optimal state transition probability and those from the baseline one. Then, the estimated state value function is used to initialize the part of the deep neural networks for forward reinforcement learning. The proposed deep forward and inverse reinforcement learning is applied into two benchmark games: Atari 2600 and Reversi. Simulation results show that our method reaches the best performance substantially faster than the standard combination of forward and inverse reinforcement learning as well as behavior cloning.


title: The Continuing Debate over Depreciation, Capital and Income

abstract: Richard P. Brief, The Continuing Debate Over Depreciation, Capital and Income (New York: Garland Publishing, Inc., 1993, 287 pp., $54). This book is a 30-year anthology of selected writings by the author delineated into six sections, or themes. The common threads tying the themes together are the concepts of estimation and measurement in accounting and the impact of their errors. Much of the debate contained in this book is documented to date back at least 100 years. The first theme highlights the theoretical foundation of measurement and uncertainty which has plagued the profession since its founding over a century ago. This foundation, called "first principles," sets the rhetorical stage for the remaining sections of the book. Beginning with Dicksee's pedagogical trademark, the agency of a ship in which the life of the entity is one shipping venture, the concepts of depreciation and profit determination are explained with emphasis on the inaccuracy (error) of interim reporting (i.e., any reporting period less than the life of the enterprise). Brief refers to the old adage "history repeats itself" when discussing controversies surrounding full disclosure, alternative accounting choices, and auditor responsibility. These unresolved controversies were as relevant a century ago as they are today. According to Brief, these controversies persist "because profit calculations involve uncertainty about the future" [p. 25]. Moreover, he posits that uncertainties cannot be mitigated through regulation. The second theme deals directly with the book's title: The Continuing Debate Over Depreciation, Capital and Income. A discussion of depreciation theory of 100 years ago is presented as well as evidence of internal economic and external institutional forces affecting depreciation methods. Brief shows that the argument of valuing assets at cost, replacement value, market value, etc., were alive 100 years ago and remain unresolved today. The choice of depreciation methods is analytically shown to affect the growth in financial capital and ultimately to impact capital and income measurement. A corollary issue is that depreciation is an allocation problem, not a valuation problem. Given that depreciation is an allocation problem, the third theme addresses the theory of cost allocation under uncertainty. Each succeeding article in this section builds on the former. Using statistical properties, Brief begins with a simple least squares model to estimate proper cost allocations by minimizing squared measurement errors. The two succeeding articles build on this concept to develop more sophisticated models. An applied concept of measurement error is first introduced in this section. The fourth theme, interest rate approximations, at first seems incongruent with the flow of the book. However, interest rate approximations appear to be the first formal attempts at estimations for business purposes; and the Rule of 69 [p. 182], an actuarial approximation of the number of periods in which a sum will double at a given interest rate, was first articulated using a depreciation problem. The fifth theme is comprised of financial reporting practices, past and present, and the intentional and unintentional errors contained in them. …


title: Social Media Mining: Prediction of Box Office Revenue

abstract: In recent years, social media has played a huge role in how we share and communicate our thoughts and opinions. This information can very valuable for companies and governments as it can be used to analyze public mood and opinion which is a very powerful tool. In this paper, we present a system that mines social media content from a platform such as Twitter for predicting future outcomes. Specifically, it uses chatter from Twitter to predict box office revenue of movies by extracting features such as tweets and their sentiments. Then, by using these features, our system constructs a polynomial regression model for predicting box office revenue. Experimental results show the effectiveness of our system in mining social media and predicting box office revenue.


title: Large Scale Dense Visual Inertial SLAM

abstract: In this paper we present a novel large scale SLAM system that combines dense stereo vision with inertial tracking. The system divides space into a grid and efficiently allocates GPU memory only when there is surface information within a grid cell. A rolling grid approach allows the system to work for large scale outdoor SLAM. A dense visual inertial dense tracking pipeline incrementally localizes stereo cameras against the scene. The proposed system is tested with both a simulated data set and several real-life data in different lighting (illumination changes), motion (slow and fast), and weather (snow, sunny) conditions. Compared to structured light-RGBD systems the proposed system works indoors and outdoors and over large scales beyond single rooms or desktop scenes. Crucially, the system is able to leverage inertial measurements for robust tracking when visual measurements do not suffice. Results demonstrate effective operation with simulated and real data, and both indoors and outdoors under varying lighting conditions.


title: Multi-robot repeated area coverage

abstract: We address the problem of repeated coverage of a target area, of any polygonal shape, by a team of robots having a limited visual range. Three distributed Cluster-based algorithms, and a method called Cyclic Coverage are introduced for the problem. The goal is to evaluate the performance of the repeated coverage algorithms under the effects of the variables: Environment Representation, and the Robots' Visual Range. A comprehensive set of performance metrics are considered, including the distance the robots travel, the frequency of visiting points in the target area, and the degree of balance in workload distribution among the robots. The Cyclic Coverage approach, used as a benchmark to compare the algorithms, produces optimal or near-optimal solutions for the single robot case under some criteria. The results can be used as a framework for choosing an appropriate combination of repeated coverage algorithm, environment representation, and the robots' visual range based on the particular scenario and the metric to be optimized.


title: High-efficiency microwave reflector antennas - A review

abstract: The paper provides a review of current research on microwave reflector antennas with particular regard to those antennas which produce pencil-beam radiation patterns. After a preliminary examination of microwave antennas of different types attention is focused on circularly symmetric antennas with axially symmetric feed systems. This class of antenna accounts for the largest number of applications which include microwave point-to-point communication, satellite communication, and radio astronomy. The choice of optimum feed is considered in more detail and it is demonstrated that metallic feeds with corrugated walls represent the best choice in most cases. Brief reference is also made to dielectric cone feeds which may find a place in future systems. The design of dual-reflector systems is considered in depth and different computer optimisation techniques are reviewed. These include the latest methods of diffraction optimization in which more than one of the antenna specifications is involved. An example is given where efficiency and VSWR are simultaneously optimized.


title: SIW Slotted Waveguide Array With Pillbox Transition for Mechanical Beam Scanning

abstract: A dual-layer pillbox antenna is proposed for mechanical beam-scanning applications at millimeter-wave frequencies. It consists of three main subsystems (multislot quasi-optical transition, input part, and radiating part). The input and radiating parts are placed in two different layers, and the quasi-optical transition behaves as a 180° E-plane coupler-type bent. The latter is made of an integrated parabolic reflector and several coupling slots etched in the metal layer located between the two substrates hosting the input and radiating parts. A physical optics analysis of the transition is proposed and compared to full-wave simulations. The radiating part is a planar slotted waveguide array. The input part is an H-plane integrated sectoral horn placed in the focal plane of the parabola and moved along a straight line to obtain the beam scanning. For the horn aligned with the parabola axis, the radiation patterns present half-power beamwidth equal to 6° and 15° in E- and H-plane, respectively. The scan range is ±35°. This mechanical scanning approach represents an attractive solution for those applications where a continuous steering of the main beam is required.


title: Grid-Based Colocation Mining Algorithms on GPU for Big Spatial Event Data: A Summary of Results

abstract: This paper investigates the colocation pattern mining problem for big spatial event data. Colocation patterns refer to subsets of spatial features whose instances are frequently located together. The problem is important in many applications such as analyzing relationships of crimes or disease with various environmental factors, but is computationally challenging due to a large number of instances, the potentially exponential number of candidate patterns, and high computational cost in generating pattern instances. Existing colocation mining algorithms (e.g., Apriori algorithm, multi-resolution filter, partial join and joinless approaches) are mostly sequential, and thus can be insufficient for big spatial event data. Recently, parallel colocation mining algorithms have been developed based on the Map-reduce framework. However, these algorithms need a large number of nodes to scale up, which is economically expensive, and their reducer nodes have a bottleneck of aggregating all instances of the same colocation patterns. Another work proposes a parallel colocation mining algorithm on GPU based on the iCPI tree and the joinless approach, but assumes that the number of neighbors for each instance is within a small constant, and thus may be inefficient when instances are dense and unevenly distributed. To address these limitations, we propose a grid-based GPU colocation mining algorithm that includes a novel cell aggregate based upper bound filter, and two refinement algorithms. We prove the correctness and completeness of proposed GPU algorithms. Preliminary results on both real world data and synthetic data show that proposed GPU algorithms are promising with over 30 times speedup on up to millions of instances.


title: THE HARDWARE PERFORMANCE OF AUTHENTICATED ENCRYPTION MODES

abstract: Authenticated encryption has long been a vital operation in cryptography by its ability to provide confidentiality, integrity and authenticity at the same time. Its use has progressed in parallel with the worldwide use of Internet Protocol (IP), which has led to development of several new schemes as well as improved versions of existing ones. There have already been studies investigating software performance of various schemes. However, performance of authenticated encryption schemes on hardware has been left as an open question. We study the comprehensive evaluation of hardware performance of the most commonly used authenticated encryption modes CCM, GCM, OCB3 and EAX. These modes are block cipher based with additional authentication data (AAD). In order to make our evaluation fair, we have implemented each scheme with AES block cipher algorithm. In our evaluation, we targeted ASIC platforms and used 45 nm generic NANGATE Open Cell Library for syntheses. In each design, we have targeted minimizing the time-area product while maximizing the throughput. In the results, area, speed, time-area product, throughput, and power figures are presented for each scheme. Finally, we provide an unbiased discussion on the impact of the structure and complexity of each scheme on hardware implementation, together with recommendations on hardware-friendly authenticated encryption scheme design.


title: Multi-objective trajectory optimization for space manipulator with multi-constraints

abstract: In order to improve the motion performance of space manipulators, multiple objectives and constraints are required in operations on orbit. Based on non-dominated sorting genetic algorithm (NSGA-II), a method is proposed to achieve multi-objective trajectory optimization with multi-constraints. Considering the maximum speed and acceleration at the end-effector as decision variables, quantities of operation time and energy cost are treated as optimal goals. Moreover, multi-constraints of manipulator and the environment are introduced during the trajectory optimization. As a result, equations of multi-objective trajectory optimization are established. Then the Pareto optimal set of multi-objective optimization problem (MOP) is calculated, and the validity of the algorithm is verified with a seven degree-of freedom (DOF) space manipulator.


title: FPNN: Field Probing Neural Networks for 3D Data

abstract: Building discriminative representations for 3D data has been an important task in computer graphics and computer vision research. Convolutional Neural Networks (CNNs) have shown to operate on 2D images with great success for a variety of tasks. Lifting convolution operators to 3D (3DCNNs) seems like a plausible and promising next step. Unfortunately, the computational complexity of 3D CNNs grows cubically with respect to voxel resolution. Moreover, since most 3D geometry representations are boundary based, occupied regions do not increase proportionately with the size of the discretization, resulting in wasted computation. In this work, we represent 3D spaces as volumetric fields, and propose a novel design that employs field probing filters to efficiently extract features from them. Each field probing filter is a set of probing points -- sensors that perceive the space. Our learning algorithm optimizes not only the weights associated with the probing points, but also their locations, which deforms the shape of the probing filters and adaptively distributes them in 3D space. The optimized probing points sense the 3D space "intelligently", rather than operating blindly over the entire domain. We show that field probing is significantly more efficient than 3DCNNs, while providing state-of-the-art performance, on classification tasks for 3D object recognition benchmark datasets.


The nature of the Heusler alloys


abstract: 1 Sia Siew Kien, Michael Rosemann and Phillip Yetton are the accepting senior editors for this article. 2 This research was partly funded by an Australian Research Council Discovery grant. The authors are grateful to the interviewees, whose willingness to share their valuable insights and experiences made this study possible, and to the senior editors and reviewers for their very helpful feedback and advice throughout the review process. 3 All quotes in this article are from employees of “RetailCo,” the subject of this case study. The names of the organization and its business divisions have been anonymized. 4 A digital business platform is “an integrated set of electronic business processes and the technologies, applications and data supporting those processes” Weill, P. and Ross, J. W. IT Savvy: What Top Executives Must Know to Go from Pain to Gain, Harvard Business School Publishing, 2009, p. 4; for more on digitized platforms, see pp. 67-87 of this publication. How an Australian Retailer Enabled Business Transformation Through Enterprise Architecture


title: Fractional-order controller design and digital implementation using FOMCON toolbox for MATLAB

abstract: In this paper, we present the suite of tools of the FOMCON (“Fractional-order Modeling and Control”) toolbox for MATLAB that are used to carry out fractional-order PID controller design and hardware realization. An overview of the toolbox, its structure and particular modules, is presented with appropriate comments. We use a laboratory object designed to conduct temperature control experiments to illustrate the methods employed in FOMCON to derive suitable parameters for the controller and arrive at a digital implementation thereof on an 8-bit AVR microprocessor. The laboratory object is working under a real-time simulation platform with Simulink, Real-Time Windows Target toolbox and necessary drivers as its software backbone. Experimental results are provided which support the effectiveness of the proposed software solution.


title: ABSTRACT CP-Miner: A Tool for Finding Copy-paste and Related Bugs in Operating System Code

abstract: Copy-pasted code is very common in large software because programmers prefer reusing code via copy-paste in order to reduce programming effort. Recent studies show that copy-paste is prone to introducing bugs and a significant portion of operating system bugs concentrate in copy-pasted code. Unfortunately, it is challenging to efficiently identify copy-pasted code in large software. Existing copy-paste detection tools are either not scalable to large software, or cannot handle small modifications in copy-pasted code. Furthermore, few tools are available to detect copy-paste related bugs. ::: ::: In this paper we propose a tool, CP-Miner, that uses data mining techniques to efficiently identify copy-pasted code in large software including operating systems, and detects copy-paste related bugs. Specifically, it takes less than 20 minutes for CP-Miner to identify 190,000 copy-pasted segments in Linux and 150,000 in FreeBSD. Moreover, CP-Miner has detected 28 copy-paste related bugs in the latest version of Linux and 23 in FreeBSD. In addition, we analyze some interesting characteristics of copy-paste in Linux and FreeBSD, including the distribution of copy-pasted code across different length, granularity, modules, degrees of modification, and various software versions.


title: Three-Dimensional-Printed Sustainable Biochar-Recycled PET Composites

abstract: Recently, the demand for high-performance, 3D-printable polymer composites has grown exponentially. The objective of this work is to develop a novel low cost and sustainable biochar-recycled poly(ethylene terephthalate) (PET) composite with improved mechanical and thermal performance. Biochar was derived from the pyrolysis of packaging waste at a high temperature and autogenic pressure. The typical temperature and pressure of the reaction are ∼1100 °C and 150 bar. Biochar was ground and sieved to below 100 μm, and it was melt compounded with recycled poly(ethylene terephthalate) derived from postconsumer PET bottles (Aquafina). Biochar/PET composite filaments of 1.75 mm were produced using a melt extruder. The filaments were used to 3D print tensile, DMA, and TMA sample coupons using a Hyrel 30 M printer. Biochar was analyzed for its textural properties, thermal stability, and surface morphology. The as-prepared polymer composite was studied for its thermal, mechanical, and dynamic mechanical properties. ...


title: Automatic Generation of Multiple Choice Questions from Slide Content using Linked Data

abstract: Assessment plays an important role in the process of learning. Multiple choice questions (MCQs) are suitable candidates to fulfill this role. We present an approach for automatically generating MCQs based on the content presented in slides. It extracts named entities from slides, and queries a knowledge base to create different varieties of MCQs and appropriate answer options. Users can choose between different levels of difficulty for the generated questions and answer options. The approach can be easily extended to generate other varieties of MCQs. Results from a user study confirm the applicability and appropriateness of the approach.


title: SFS: random write considered harmful in solid state drives

abstract: Over the last decade we have witnessed the relentless technological improvement in flash-based solid-state drives (SSDs) and they have many advantages over hard disk drives (HDDs) as a secondary storage such as performance and power consumption. However, the random write performance in SSDs still remains as a concern. Even in modern SSDs, the disparity between random and sequential write bandwidth is more than tenfold. Moreover, random writes can shorten the limited lifespan of SSDs because they incur more NAND block erases per write. ::: ::: In order to overcome these problems due to random writes, in this paper, we propose a new file system for SSDs, SFS. First, SFS exploits the maximum write bandwidth of SSD by taking a log-structured approach. SFS transforms all random writes at file system level to sequential ones at SSD level. Second, SFS takes a new data grouping strategy on writing, instead of the existing data separation strategy on segment cleaning. It puts the data blocks with similar update likelihood into the same segment. This minimizes the inevitable segment cleaning overhead in any log-structured file system by allowing the segments to form a sharp bimodal distribution of segment utilization. ::: ::: We have implemented a prototype SFS by modifying Linux-based NILFS2 and compared it with three state-of-the-art file systems using several realistic workloads. SFS outperforms the traditional LFS by up to 2.5 times in terms of throughput. Additionally, in comparison to modern file systems such as ext4 and btrfs, it drastically reduces the block erase count inside the SSD by up to 7.5 times.


title: REACHING OUT: INVOLVING USERS IN INNOVATION TASKS THROUGH SOCIAL MEDIA

abstract: Integrating social media into the innovation process can open up the potential for organizations to utilize the collective creativity of consumers from all over the world. The research in this paper sets out to identify how social media can facilitate innovation. By taking a Design Science Research approach this research presents the Social Media Innovation Method for matching innovation tasks with social media characteristics. This supports the selection of best suitable social media and can help organizations to achieve their innovation goals. At the core of the method is the honeycomb model which describes seven social media characteristics on three dimensions: audience, content and time. The method has been evaluated by using an approach called scenario walkthrough that is applied in a real-life spatial planning project. This research concludes that there is no one-size-fits-all answer to the question how social media can be of value for the innovation process. However, organizations that want to know how it can benefit their own innovation process can use the Social Media Innovation Method presented in this research as a way to provide an answer to that question, uniquely tailored to each innovation task for which social media is to be used.


title: Improvement of power density spoke type Permanent Magnet Generator

abstract: This paper discusses on the improvement of power density of hollow rotor compared to conventional spoke type Permanent Magnet Generator (PMG). The objective of this research is to propose a rotor structure that could improve the power density by maximizing the flux linkage of the machine. In this research the Finite Element Analysis (FEA) is used to simulate the characteristics of the hollow rotor spoke type PMG at various speed. The result shows that the hollow-rotor topology has higher back electromagnetive force and output voltage compared to conventional spoke type PMG. In addition, the hollow-rotor spoke type motor has higher output power compared to conventional spoke type PMG. In summary, this paper provides an overview of the performance of the hollow-rotor spoke type topology that contributes high power density.


title: Hypertool: A Programming Aid for Message-Passing Systems

abstract: Programming assistance, automation concepts, and their application to a message-passing system program development tool called Hypertool are discussed. Hypertool performs scheduling and handles the communication primitive insertion automatically, thereby increasing productivity and eliminating synchronization errors. Two algorithms, based on the critical-path method, are presented for scheduling processes statically. Hypertool also generates the performance estimates and other program quality measures to help programmers improve their algorithms and programs. >


title: Integrated task and motion planning in belief space

abstract: We describe an integrated strategy for planning, perception, state estimation and action in complex mobile manipulation domains based on planning in the belief space of probability distributions over states using hierarchical goal regression (pre-image back-chaining). We develop a vocabulary of logical expressions that describe sets of belief states, which are goals and subgoals in the planning process. We show that a relatively small set of symbolic operators can give rise to task-oriented perception in support of the manipulation goals. An implementation of this method is demonstrated in simulation and on a real PR2 robot, showing robust, flexible solution of mobile manipulation problems with multiple objects and substantial uncertainty.


title: Periscope: a system for adaptive 3D visualization of search results

abstract: A system for efficient 3D visualization of Web search results is presented. The system, called Periscope1, uses a novel approach for adaptive and customizable visualization of complex data. The whole process is divided into a number of interactive steps. At each step, the system can automatically choose the best method of presenting search results. The user can also select a specific presentation method to focus on certain properties of the result obtained. After analyzing the current search result, the user can narrow or broaden the search query and repeat the procedure.


title: Ontologies used in robotics: A survey with an outlook for automated driving

abstract: Full autonomy of road vehicles is a major goal of the automotive industry. To reach such high autonomy it is necessary to provide an accurate and comprehensible situation description for the environment and the vehicle itself. A consistent depiction is essential to facilitate data exchange and communication between internal modules, e.g. collision check and environment model, as well as communication with further information sources like traffic participants vehicle to vehicle (V2V) or the infrastructure, e.g. smart traffic lights, road signs or radio traffic service (V2I). One necessary tool to create such a model could be an ontology which represents the given information and its dependencies. Initially this work provides a summary of given approaches in the literature for use of ontologies in robotics in general. Therefore approaches are stated sorted by application and task. Further approaches with focus on autonomous robots and in particular on autonomous vehicles are listed and described. Finally we give an outlook for further research topics in the domain of ontologies.


title: Micro patterns in Java code

abstract: Micro patterns are similar to design patterns, except that micro patterns are stand at a lower, closer to the implementation, level of abstraction. Micro patterns are also unique in that they are mechanically recognizable, since each such pattern can be expressed as a formal condition on the structure of a class.This paper presents a catalog of 27 micro-patterns defined on Java classes and interfaces. The catalog captures a wide spectrum of common programming practices, including a particular and (intentionally restricted) use of inheritance, immutability, data management and wrapping, restricted creation, and emulation of procedural-, modular-, and even functional- programming paradigms with object oriented constructs. Together, the patterns present a set of prototypes after which a large portion of all Java classes and interfaces are modeled. We provide empirical indication that this portion is as high as 75%.A statistical analysis of occurrences of micro patterns in a large software corpus, spanning some 70,000 Java classes drawn from a rich set of application domains, shows, with high confidence level that the use of these patterns is not random. These results indicate consciousness and discernible design decisions, which are sustained in the software evolution. With high confidence level, we can also show that the use of these patterns is tied to the specification, or the purpose, that the software realizes.The traceability, abundance and the statistical significance of micro pattern occurrence raise the hope of using the classification of software into these patterns for a more founded appreciation of its design and code quality.


title: ALGORITHMIC METHODS FOR SEGMENTATION OF TIME SERIES: AN OVERVIEW

abstract: Adaptive and innovative application of classical data mining principles and techniques in time series analysis has resulted in development of a concept known as time series data mining. Since the time series are present in all areas of business and scientific research, attractiveness of mining of time series datasets should not be seen only in the context of the research challenges in the scientific community, but also in terms of usefulness of the research results, as a support to the process of business decision-making. A fundamental component in the mining process of time series data is time series segmentation. As a data mining research problem, segmentation is focused on the discovery of rules in movements of observed phenomena in a form of interpretable, novel, and useful temporal patterns. In this Paper, a comprehensive review of the conceptual determinations, including the elements of comparative analysis, of the most commonly used algorithms for segmentation of time series, is being considered.


title: MAMT : an environment for modeling and implementing mobile agents

abstract: This paper presents an approach to model and to implement mobile agents. This approach is materialized by a UML profile, called MA-UML for modeling mobile agents, and a software development environment that assists the specification, design and implementation stages of the agent system development lifecycle, called MAMT. The MAMT environment provides support for modeling multi-agent systems by using the MA-UML profile.


title: Stacking-Based Visualization of Trajectory Attribute Data

abstract: Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well.


IACR Cryptology ePrint Archive 2013 1 Analysis of the Rainbow Tradeoff Algorithm Used in Practice


title: Developing a Leadership Identity: A Grounded Theory

abstract: This grounded theory study on developing a leadership identity revealed a 6-stage developmental process. The thirteen diverse students in this study described their leadership identity as moving from a leader-centric view to one that embraced leadership as a collaborative, relational process. Developing a leadership identity was connected to the categories of developmental influences, developing self, group influences, students' changing view of self with others, and students' broadening view of leadership. A conceptual model illustrating the grounded theory of developing a leadership identity is presented.


title: Web Penetration Testing using Nessus and Metasploit Tool

abstract: Web Penetration Testing is a tool that is being used widely to see how the website reacts when an vulnerability attack is done. Now days many ethical hackers use web penetration tool to predict the vulnerabilities of the website. We have done a survey of some of the web penetration tools that are available and then we have proposed a architecture using nesus and metasploit tool to do scan vulnerabilities of an website.


title: Brain anatomy differences in childhood stuttering

abstract: Abstract Stuttering is a developmental speech disorder that occurs in 5% of children with spontaneous remission in approximately 70% of cases. Previous imaging studies in adults with persistent stuttering found left white matter deficiencies and reversed right–left asymmetries compared to fluent controls. We hypothesized that similar differences might be present indicating brain development differences in children at risk of stuttering. Optimized voxel-based morphometry compared gray matter volume (GMV) and diffusion tensor imaging measured fractional anisotropy (FA) in white matter tracts in 3 groups: children with persistent stuttering, children recovered from stuttering, and fluent peers. Both the persistent stuttering and recovered groups had reduced GMV from normal in speech-relevant regions: the left inferior frontal gyrus and bilateral temporal regions. Reduced FA was found in the left white matter tracts underlying the motor regions for face and larynx in the persistent stuttering group. Contrary to previous findings in adults who stutter, no increases were found in the right hemisphere speech regions in stuttering or recovered children and no differences in right–left asymmetries. Instead, a risk for childhood stuttering was associated with deficiencies in left gray matter volume while reduced white matter integrity in the left hemisphere speech system was associated with persistent stuttering. Anatomical increases in right hemisphere structures previously found in adults who stutter may have resulted from a lifetime of stuttering. These findings point to the importance of considering the role of neuroplasticity during development when studying persistent forms of developmental disorders in adults.


title: The software architecture of the Berkeley UAV Platform

abstract: This paper details the software architecture of the Berkeley Unmanned Aerial Vehicle (UAV) Platform. Developed over the course of three years, this platform has successfully demonstrated autonomous vision-based navigation and obstacle avoidance. A software architecture has been developed to allow for collaborative control concepts to be examined. This modular architecture has been shown to be effective for use in allowing a team of UAVs to collaboratively perform a set of missions. The performance of the architecture was demonstrated using 3 UAVs to perform autonomous collaborative patrolling and vision-based navigation.


Meets NOMA : Non-Orthogonal Multiple Access for 5 G Enabled Vehicular Networks


IMU-based Joint Angle Measurement Made Practical


title: A review on real time data stream classification and adapting to various concept drift scenarios

abstract: Data streams are viewed as a sequence of relational tuples (e.g., sensor readings,call records, web page visits) that continuously arrive at time-varying and possibly unbound streams. These data streams are potentially huge in size and thus it is impossible to process many data mining techniques and approaches. Classification techniques fail to successfully process data streams because of two factors: their overwhelming volume and their distinctive feature known as concept drift. Concept drift is a term used to describe changes in the learned structure that occur over time. The occurance of concept drift leads to a drastic drop in classification accuracy. The recognition of concept drift in data streams has led to sliding-window approaches also different approaches to mining data streams with concept drift include instance selection methods, drift detection, ensemble classifiers, option trees and using Hoeffding boundaries to estimate classifier performance. This paper describes the various types of concept drifts that affect the data examples and discusses various approaches in order to handle concept drift scenarios. The aim of this paper is to review and compare single classifier and ensemble approaches to data stream mining respectively.


title: Estimation of Temporal Gait Events from a Single Accelerometer Through the Scale-Space Filtering Idea

abstract: The purpose of this paper is to develop an accelerometry system capable of performing gait event demarcation and calculation of temporal parameters using a single waist-mounted device. Particularly, a mobile phone positioned over the L2 vertebra is used to acquire trunk accelerations during walking. Signals from the acceleration magnitude and the vertical acceleration are smoothed through different filters. Cut-off points between filtered signals as a result of convolving with varying levels of Gaussian filters and other robust features against temporal variation and noise are used to identify peaks that correspond to gait events. Five pre-frail older adults and five young healthy adults were recruited in an experiment. Cadence, step/stride time, step/stride CV, step asymmetry and percentages of the stance/swing and single/double support phases, among the two groups of different mobility were quantified by the system.


title: Artistic Multiprojection Rendering

abstract: In composing hand-drawn images of 3D scenes, artists often alter the projection for each object in the scene independently, thereby generating multiprojection images. We present a tool for creating such multiprojection images and animations, consisting of two parts: a multiprojection rendering algorithm and an interactive interface for attaching local cameras to the scene geometry. We describe a new set of techniques for resolving visibility between geometry rendered with different local cameras. We also develop several camera constraints that are useful when initially setting local camera parameters and when animating the scene. We demonstrate applications of our methods for generating a variety of artistic effects in still images and in animations.


Open domain real-time question answering based on asynchronous multiperspective context-driven retrieval and neural paraphrasing


title: StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views

abstract: Urban forms at human-scale, i.e., urban environments that individuals can sense (e.g., sight, smell, and touch) in their daily lives, can provide unprecedented insights on a variety of applications, such as urban planning and environment auditing. The analysis of urban forms can help planners develop high-quality urban spaces through evidence-based design. However, such analysis is complex because of the involvement of spatial, multi-scale (i.e., city, region, and street), and multivariate (e.g., greenery and sky ratios) natures of urban forms. In addition, current methods either lack quantitative measurements or are limited to a small area. The primary contribution of this work is the design of StreetVizor, an interactive visual analytics system that helps planners leverage their domain knowledge in exploring human-scale urban forms based on street view images. Our system presents two-stage visual exploration: 1) an AOI Explorer for the visual comparison of spatial distributions and quantitative measurements in two areas-of-interest (AOIs) at city- and region-scales; 2) and a Street Explorer with a novel parallel coordinate plot for the exploration of the fine-grained details of the urban forms at the street-scale. We integrate visualization techniques with machine learning models to facilitate the detection of street view patterns. We illustrate the applicability of our approach with case studies on the real-world datasets of four cities, i.e., Hong Kong, Singapore, Greater London and New York City. Interviews with domain experts demonstrate the effectiveness of our system in facilitating various analytical tasks.


title: Convergent Actor-Critic Algorithms Under Off-Policy Training and Function Approximation

abstract: We present the first class of policy-gradient algorithms that work with both state-value and policy function-approximation, and are guaranteed to converge under off-policy training. Our solution targets problems in reinforcement learning where the action representation adds to the-curse-of-dimensionality; that is, with continuous or large action sets, thus making it infeasible to estimate state-action value functions (Q functions). Using state-value functions helps to lift the curse and as a result naturally turn our policy-gradient solution into classical Actor-Critic architecture whose Actor uses state-value function for the update. Our algorithms, Gradient Actor-Critic and Emphatic Actor-Critic, are derived based on the exact gradient of averaged state-value function objective and thus are guaranteed to converge to its optimal solution, while maintaining all the desirable properties of classical Actor-Critic methods with no additional hyper-parameters. To our knowledge, this is the first time that convergent off-policy learning methods have been extended to classical Actor-Critic methods with function approximation.


title: A continuous time linear adaptive source localization algorithm, robust to persistent drift

abstract: The problem of source localization has assumed increased importance in recent years. In this paper, we formulate a continuous time adaptive localization algorithm, that permits a mobile agent to estimate the location of a stationary source, using only the measured distance from the source. The algorithm is shown to be exponentially asymptotically stable, under a persistent excitation condition that has an appealing interpretation. We quantify the fact that exponential asymptotic stability endows the algorithm with the ability to track slow, bounded but potentially persistent and nontrivial drift in the source.


title: ALMANAC: Internet of Things for Smart Cities

abstract: Smart cities advocate future environments where sensor pervasiveness, data delivery and exchange, and information mash-up enable better support of every aspect of (social) life in human settlements. As this vision matures, evolves and is shaped against several application scenarios, and adoption perspectives, a common need for scalable, pervasive, flexible and replicable infrastructures emerges. Such a need is currently fostering new design efforts to grant performance, reuse and interoperability while avoiding knowledge silos typical of early efforts on similar top is, e.g. Automation in buildings and homes. This paper introduces a federated smart city platform (SCP) developed in the context of the ALMANAC FP7 EU project and discusses lessons learned during the first experimental application of the platform to a smart waste management scenario in a medium-sized, European city. The ALMANAC SCP aims to integrate Internet of Things (IoT), capillary networks and metro access networks to offer smart services to the citizens, and thus enable Smart City processes. The key element of the SCP is a middleware supporting semantic interoperability of heterogeneous resources, devices, services and data management. The platform is built upon a dynamic federation of private and public networks, while supporting end-to-end security and privacy. Furthermore, it also enables the integration of services that, although being natively external to the platform itself, allow enriching the set of data and information used by the Smart City applications supported.


title: Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance

abstract: At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model's behavior, precision to how accurate humans are in those predictions, and effort is either the up-front effort required in interpreting the model, or the effort required to make predictions about a model's behavior. ::: In this work, we propose anchor-LIME (aLIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear. We compare aLIME to linear LIME with simulated experiments, and demonstrate the flexibility of aLIME with qualitative examples from a variety of domains and tasks.


title: Design and measurement of array antennas for 77GHz automotive radar application

abstract: Array antennas for 77GHz automotive radar application are designed and measured. Linear series-fed patch array (SFPA) antenna is designed for transmitters of middle range radar (MRR) and all the receivers. A planar SFPA based on the linear one and substrate integrated waveguide (SIW) feeding network is proposed for transmitter of long range radar (LRR), which can decline the radiation from feeding network itself. The array antennas are fabricated, both the performances with and without radome of these array antennas are measured. Good agreement between simulation and measurement has been achieved. They can be good candidates for 77GHz automotive application.


title: Improving the performance of motor-impaired users with automatically-generated, ability-based interfaces

abstract: We evaluate two systems for automatically generating personalized interfaces adapted to the individual motor capabilities of users with motor impairments. The first system, SUPPLE, adapts to users' capabilities indirectly by first using the ARNAULD preference elicitation engine to model a user's preferences regarding how he or she likes the interfaces to be created. The second system, SUPPLE++, models a user's motor abilities directly from a set of one-time motor performance tests. In a study comparing these approaches to baseline interfaces, participants with motor impairments were 26.4% faster using ability-based user interfaces generated by SUPPLE++. They also made 73% fewer errors, strongly preferred those interfaces to the manufacturers' defaults, and found them more efficient, easier to use, and much less physically tiring. These findings indicate that rather than requiring some users with motor impairments to adapt themselves to software using separate assistive technologies, software can now adapt itself to the capabilities of its users.


title: A 48-MHz Differential Crystal Oscillator With 168-fs Jitter in 28-nm CMOS

abstract: A 168-fs rms jitter, 48-MHz differential crystal oscillator based on a new active inductor biasing circuit achieves differential operation with low noise, power, and area overhead. This architecture has two significant advantages compared to the single-ended crystal oscillators that are normally used: 1) this circuit rejects power supply noise and interference which leads to lower jitter and 2) the crystal oscillator-induced spurious tones are smaller and therefore less detrimental to sensitive blocks (e.g., LNA) in RF radios. This paper theoretically analyzes the differential crystal oscillator and provides detailed design considerations. A prototype requires half the power and 80% less area than previously reported differential crystal oscillators. Implemented in a 28-nm LP CMOS process, it draws 1.5 mA from a 1-V supply and occupies 0.013 mm2.


title: Particular object retrieval with integral max-pooling of CNN activations

abstract: Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations. Yet such models are not compatible with geometry-aware re-ranking methods and still outperformed, on some particular object retrieval benchmarks, by traditional image search systems relying on precise descriptor matching, geometric re-ranking, or query expansion. This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN. We build compact feature vectors that encode several image regions without the need to feed multiple inputs to the network. Furthermore, we extend integral images to handle max-pooling on convolutional layer activations, allowing us to efficiently localize matching objects. The resulting bounding box is finally used for image re-ranking. As a result, this paper significantly improves existing CNN-based recognition pipeline: We report for the first time results competing with traditional methods on the challenging Oxford5k and Paris6k datasets.


title: Adaptive signal processing techniques for extracting abdominal fetal electrocardiogram

abstract: The extraction of the Fetal Electrocardiogram (fECG) from the composite Electrocardiogram (ECG) signal obtained from the abdominal lead is discussed. The main point of this paper is to introduce some of the most used Least Mean Squares (LMS) based Finite Impulse Response (FIR) Adaptive Filters and to determine which of them are the most effective under varying circumstances. Experimental results suggest the ideal combination of the chosen settings for these functions. Results of fECG extraction are assessed by Percentage Root-Mean-Square Difference (PRD), input and output Signal to Noise Ratios (SNRs), and Root Mean Square Error (RMSE). Based on simulations conclusions, optimal convergence constant value and filter order were empirically determined. Setting the optimal value of the convergence constant and filter order of adaptive algorithm can be considered a contribution to original results. These results can be used on real records fECG, where it is difficult to determine because of the missing reference.


title: Tightly-Coupled Model Aided Visual-Inertial Fusion for Quadrotor Micro Air Vehicles

abstract: The main contribution of this paper is a tightly-coupled visual-inertial fusion algorithm for simultaneous localisation and mapping (SLAM) for a quadrotor micro aerial vehicle (MAV). Proposed algorithm is based on an extended Kalman filter that uses a platform specific dynamic model to integrate information from an inertial measurement unit (IMU) and a monocular camera on board the MAV. MAV dynamic model exploits the unique characteristics of the quadrotor, making it possible to generate relatively accurate motion predictions. This, together with an undelayed feature initialisation strategy based on inverse depth parametrisation enables more effective feature tracking and reliable visual SLAM with a small number of features even during rapid manoeuvres. Experimental results are presented to demonstrate the effectiveness of the proposed algorithm.


title: Driver fatigue detection system

abstract: The research aims to detect the onset of drowsiness in drivers, while the vehicle is in motion. Detection is done by continuously looking out for symptoms of drowsiness, while considering both physiological and physical signs. Physiological factors include core body temperature and pulse rate. Both of these parameters will decrease during the onset of drowsiness. These are monitored by using somatic sensors. Physical cues including yawning, drooping eyelids, closed eyes and increased blink durations. Once the system detects that the driver is drowsy by using a combination of these factors, it alerts the driver across multiple stages depending on the severity of the symptoms. The system also becomes attuned to the driver's unique characteristics over time, thus reducing the margin of false positives.


title: Dry sliding wear of eutectic Al–Si

abstract: The wear of as-cast eutectic Al–Si was studied using pin-on-disk tribotests in two different environments, air and dry argon. The counterface in all tests was yttria-stabilized zirconia. It was found that wear of the Al–Si was reduced by about 60% by the removal of oxygen from the test environment. The zirconia counterfaces showed measurable wear after tests performed in air, while there was very little wear of the zirconia for tests conducted under argon. The near-surface regions of the Al–Si pins were examined using a transmission electron microscope (TEM), using specimens produced by focussed ion beam milling. The specimens that had been worn in air were characterized by a near-surface mechanically mixed layer containing a considerable amount of both aluminum oxide and zirconium oxide—the aluminum oxide particles had evidently acted as abrasive agents to remove material from the zirconia counterface. In contrast, TEM analysis of the Al–Si tested in argon showed little zirconium oxide in the near-surface regions.


abstract: In recent years, two communities have grown around a joint interest on how big data can be exploited to benefit education and the science of learning: Educational Data Mining and Learning Analytics. This article discusses the relationship between these two communities, and the key methods and approaches of educational data mining. The article discusses how these methods emerged in the early days of research in this area, which methods have seen particular interest in the EDM and learning analytics communities, and how this has changed as the field matures and has moved to making significant contributions to both educational research and practice.


title: Modeling, control and design optimization for a fully-actuated hexarotor aerial vehicle with tilted propellers

abstract: Mobility of a hexarotor UAV in its standard configuration is limited, since all the propeller force vectors are parallel and they achieve only 4-DoF actuation, similar, e.g., to quadrotors. As a consequence, the hexarotor pose cannot track an arbitrary trajectory while the center of mass is tracking a position trajectory. In this paper, we consider a different hexarotor architecture where propellers are tilted, without the need of any additional hardware. In this way, the hexarotor gains a 6-DoF actuation which allows to independently reach positions and orientations in free space and to be able to exert forces on the environment to resist any wrench for aerial manipulation tasks. After deriving the dynamical model of the proposed hexarotor, we discuss the controllability and the tilt angle optimization to reduce the control effort for the specific task. An exact feedback linearization and decoupling control law is proposed based on the input-output mapping, considering the Jacobian and task acceleration, for non-linear trajectory tracking. The capabilities of our approach are shown by simulation results.


title: Tensor LRR and Sparse Coding-Based Subspace Clustering

abstract: Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established stateof- the-art methods.


title: Conditional Image-to-Image Translation

abstract: Image-to-image translation tasks have been widely investigated with Generative Adversarial Networks (GANs) and dual learning. However, existing models lack the ability to control the translated results in the target domain and their results usually lack of diversity in the sense that a fixed image usually leads to (almost) deterministic translation result. In this paper, we study a new problem, conditional image-to-image translation, which is to translate an image from the source domain to the target domain conditioned on a given image in the target domain. It requires that the generated image should inherit some domain-specific features of the conditional image from the target domain. Therefore, changing the conditional image in the target domain will lead to diverse translation results for a fixed input image from the source domain, and therefore the conditional input image helps to control the translation results. We tackle this problem with unpaired data based on GANs and dual learning. We twist two conditional translation models (one translation from A domain to B domain, and the other one from B domain to A domain) together for inputs combination and reconstruction while preserving domain independent features. We carry out experiments on men's faces from-to women's faces translation and edges to shoes&bags translations. The results demonstrate the effectiveness of our proposed method.


title: High-Performance Transformerless Online UPS

abstract: In this paper, a high-performance single-phase transformerless online uninterruptible power supply (UPS) is proposed. The proposed UPS is composed of a four-leg-type converter, which operates as a rectifier, a battery charger/discharger, and an inverter. The rectifier has the capability of power-factor collection and regulates a constant dc-link voltage. The battery charger/discharger eliminates the need for the transformer and the increase of the number of battery and supplies the power demanded by the load to the dc-link capacitor in the event of the input-power failure or abrupt decrease of the input voltage. The inverter provides a regulated sinusoidal output voltage to the load and limits the output current under an impulsive load. The control of the dc-link voltage enhances the transient response of the output voltage and the utilization of the input power. By utilizing the battery charger/discharger, the overall efficiency of the system is improved, and the size, weight, and cost of the system are significantly reduced. Experimental results obtained with a 3-kVA prototype show a normal efficiency of over 95.6% and an input power factor of over 99.7%.


title: Optimising Health Informatics Outcomes – Getting Good Evidence to Where it Matters

abstract: This editorial is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Evidence-based Health informatics: How do we know what we know?", written by Elske Ammenwerth [1]. Health informatics uses and applications have crept up on health systems over half a century, starting as simple automation of large-scale calculations, but now manifesting in many cases as rule- and algorithm-based creation of composite clinical analyses and 'black box' computation of clinical aspects, as well as enablement of increasingly complex care delivery modes and consumer health access. In this process health informatics has very largely bypassed the rules of precaution, proof of effectiveness, and assessment of safety applicable to all other health sciences and clinical support systems. Evaluation of informatics applications, compilation and recognition of the importance of evidence, and normalisation of Evidence Based Health Informatics, are now long overdue on grounds of efficiency and safety. Ammenwerth has now produced a rigorous analysis of the current position on evidence, and evaluation as its lifeblood, which demands careful study then active promulgation. Decisions based on political aspirations, 'modernisation' hopes, and unsupported commercial claims must cease - poor decisions are wasteful and bad systems can kill. Evidence Based Health Informatics should be promoted, and expected by users, as rigorously as Cochrane promoted Effectiveness and Efficiency, and Sackett promoted Evidence Based Medicine - both of which also were introduced retrospectively to challenge the less robust and partially unsafe traditional 'wisdom' in vogue. Ammenwerth's analysis gives the necessary material to promote that mission.


title: R2G: a Tool for Migrating Relations to Graphs

abstract: We present R2G, a tool for the automatic migration of databases from a relational to a Graph Database Management System (GDBMS). GDBMSs provide a flexible and efficient solution to the management of graph-based data (e.g., social and semantic Web data) and, in this context, the conversion of the persistent layer of an application from a relational to a graph format can be very beneficial. R2G provides a thorough solution to this problem with a minimal impact to the application layer: it transforms a relational database r into a graph database g and any conjunctive query over r into a graph query over g. Constraints defined over r are suitably used in the translation to minimize the number of data access required by graph queries. The approach refers to an abstract notion of graph database and this allows R2G to map relational database into different GDBMSs. The demonstration of R2G allows the direct comparison of the relational and the graph approaches to data management.


title: The psychometric properties of the Chinese version of the Fagerstrom Test for Nicotine Dependence

abstract: Abstract This study examined the psychometric properties of the Chinese version of the Fagerstrom Test for Nicotine Dependence (FTND). Criterion validity, factorial structure, and construct reliability were conducted using PRELIS 2 and LISREL 8.7 software. Two hundred and forty-five adult smokers were recruited from April to August 2005 in Taiwan. Because some items of the FTND are dichotomous and ordinal, biserial and polyserial correlations between the items and saliva cotinine levels were utilized. Confirmatory factor analyses (CFA) were performed to compare the relative fit of three competing models. Computation of the construct reliability was necessitated by a lack of tau-equivalence. The results demonstrated that the Chinese version has satisfactory validity and reliability. One item, “Smoke more in the morning,” had low correlations with saliva cotinine. CFA found the items of the Chinese version were best modeled as two correlated factors without a cross-loading. These findings differ from the previous research testing the English version. Studies addressing the specific features of cigarette dependence in different socio-cultural contexts are needed.


title: Humanoid robot LOLA

abstract: This paper presents our new 25-DoF humanoid walking robot LOLA. The goal of our research is to realize a fast, human-like walking motion (target speed: 5 km/h). Furthermore, we want to increase the robot's autonomous, vision-guided walking capabilities. The robot has 25 degrees of freedom, including 7-DoF legs with actively driven toe joints. It is characterized by its lightweight construction, a modular, multi-sensory joint design with brushless motors and an electronics architecture using decentralized joint controllers. Special emphasis was put on an improved mass distribution of the leg apparatus to achieve good dynamic performance. The sensor system comprises absolute angular sensors in all links, two custom-made force/torque sensors in the feet and a high-precision inertial sensor in the upper body. Trajectory generation and control system aim at faster, more flexible, and more robust walking patterns.


title: Hopping in legged systems — Modeling and simulation for the two-dimensional one-legged case

abstract: A two-dimensional one-legged hopping machine is modeled and simulated in order to better understand legged systems that hop and run. The analysis is focused on balance, dynamic stability, and resonant oscillation for the planar case. A springy leg with nonzero mass, a simple body, and an actuated hinge-type hip are incorporated in the model. Control of the model is decomposed into a vertical hopping part, a horizontal velocity part, and a body attitude part. Estimates of total system energy are used in regulating hopping height in order to initiate hopping, to maintain level hopping, to change from one hopping height to another, and to terminate hopping. Balance and control of forward velocity are explored with three algorithms. The feasibility of decomposing the control of running into a height control part, a forward velocity control part, and an attitude control part is verified by simulations.


title: Structuring remote object systems for mobile hosts with intermittent connectivity

abstract: In distributed object systems, remote method invocation attempts to abstract communication between different machines to the level of object method invocation. Link failure in such systems often appears as a failed method invocation, which must be explicitly handled outside the remote object infrastructure. This usually implies that techniques to handle the effects of link failure are embedded within application functionality. The resulting application can become unnecessarily specialized to a particular environment, restricting the situations where it could be used. We explore the possibility of cleanly decoupling policies for handling a link failure from the application. With this goal in mind, we present extensions to the Java RMI system in the context of a distributed system with mobile hosts. An intermittent communication link is typical in such systems, and applications use techniques such as data caching or queuing of method calls to alleviate the effects. We show how such techniques and policies can be implemented generically so that not only does the application become oblivious of them, but also policies can be chosen dynamically depending on a particular situation.