title: Auditory Distraction and Acclimatization to Hearing Aids

abstract: OBJECTIVE: It is widely recognized by hearing aid users and audiologists that a period of auditory acclimatization and adjustment is needed for new users to become accustomed to their devices. The aim of the present study was to test the idea that auditory acclimatization and adjustment to hearing aids involves a process of learning to "tune out" newly audible but undesirable sounds, which are described by new hearing aid users as annoying and distracting. It was hypothesized that (1) speech recognition thresholds in noise would improve over time for new hearing aid users, (2) distractibility to noise would reduce over time for new hearing aid users, (3) there would be a correlation between improved speech recognition in noise and reduced distractibility to background sounds, (4) improvements in speech recognition and distraction would be accompanied by self-report of reduced annoyance, and (5) improvements in speech recognition and distraction would be associated with higher general cognitive ability and more hearing aid use. DESIGN: New adult hearing aid users (n = 35) completed a test of aided speech recognition in noise (SIN) and a test of auditory distraction by background sound amplified by hearing aids on the day of fitting and 1, 7, 14, and 30 days post fitting. At day 30, participants completed self-ratings of the annoyance of amplified sounds. Daily hearing aid use was measured via hearing aid data logging, and cognitive ability was measured with the Wechsler Abbreviated Scale of Intelligence block design test. A control group of experienced hearing aid users (n = 20) completed the tests over a similar time frame. RESULTS: At day 30, there was no statistically significant improvement in SIN among new users versus experienced users. However, levels of hearing loss and hearing aid use varied widely among new users. A subset of new users with moderate hearing loss who wore their hearing aids at least 6 hr/day (n = 10) had significantly improved SIN (by ~3-dB signal to noise ratio), compared with a control group of experienced hearing aid users. Improvements in SIN were associated with more consistent HA use and more severe hearing loss. No improvements in the test of auditory distraction by background sound were observed. Improvements in SIN were associated with self-report of background sound being less distracting and greater self-reported hearing aid benefit. There was no association between improvements in SIN and cognitive ability or between SIN and auditory distraction. CONCLUSIONS: Improvements in SIN were accompanied by self-report of background sounds being less intrusive, consistent with auditory acclimatization involving a process of learning to "tune out" newly audible unwanted sounds. More severe hearing loss may afford the room for improvement required to show better SIN performance with time. Consistent hearing aid use may facilitate acclimatization to hearing aids and better SIN performance. Language: en


title: Looking Beyond the Simple Scenarios: Combining Learners and Optimizers in 3D Temporal Tracking

abstract: 3D object temporal trackers estimate the 3D rotation and 3D translation of a rigid object by propagating the transformation from one frame to the next. To confront this task, algorithms either learn the transformation between two consecutive frames or optimize an energy function to align the object to the scene. The motivation behind our approach stems from a consideration on the nature of learners and optimizers. Throughout the evaluation of different types of objects and working conditions, we observe their complementary nature — on one hand, learners are more robust when undergoing challenging scenarios, while optimizers are prone to tracking failures due to the entrapment at local minima; on the other, optimizers can converge to a better accuracy and minimize jitter. Therefore, we propose to bridge the gap between learners and optimizers to attain a robust and accurate RGB-D temporal tracker that runs at approximately 2 ms per frame using one CPU core. Our work is highly suitable for Augmented Reality (AR), Mixed Reality (MR) and Virtual Reality (VR) applications due to its robustness, accuracy, efficiency and low latency. Aiming at stepping beyond the simple scenarios used by current systems, often constrained by having a single object in the absence of clutter, averting to touch the object to prevent close-range partial occlusion or selecting brightly colored objects to easily segment them individually, we demonstrate the capacity to handle challenging cases under clutter, partial occlusion and varying lighting conditions.


title: Div150Multi: a social image retrieval result diversification dataset with multi-topic queries

abstract: In this paper we introduce a new dataset, Div150Multi, that was designed to support shared evaluation of diversification techniques in different areas of social media photo retrieval and related areas. The dataset comes with associated relevance and diversity assessments performed by trusted annotators. The data consists of around 300 complex queries represented via 86,769 Flickr photos, around 27M photo links for around 6,000 users, metadata, Wikipedia pages and content descriptors for text and visual modalities, including state of the art deep features. To facilitate distribution, only Creative Commons content allowing redistribution was included in the dataset. The proposed dataset was validated during the 2015 Retrieving Diverse Social Images Task at the MediaEval Benchmarking.


Efficient , High-Quality Force-Directed Graph Drawing


title: A deterministic global optimization algorithm

abstract: In this paper, we consider a deterministic global optimization algorithm for solving a general linear sum of ratios (LFP). First, an equivalent optimization problem (LFP1) of LFP is derived by exploiting the characteristics of the constraints of LFP. By a new linearizing method the linearization relaxation function of the objective function of LFP1 is derived, then the linear relaxation programming (RLP) of LFP1 is constructed and the proposed branch and bound algorithm is convergent to the global minimum through the successive refinement of the linear relaxation of the feasible region of the objection function and the solutions of a series of RLP. And finally the numerical experiments are given to illustrate the feasibility of the proposed algorithm.


title: Using recurrent neural networks for decompilation

abstract: Decompilation, recovering source code from binary, is useful in many situations where it is necessary to analyze or understand software for which source code is not available. Source code is much easier for humans to read than binary code, and there are many tools available to analyze source code. Existing decompilation techniques often generate source code that is difficult for humans to understand because the generated code often does not use the coding idioms that programmers use. Differences from human-written code also reduce the effectiveness of analysis tools on the decompiled source code. To address the problem of differences between decompiled code and human-written code, we present a novel technique for decompiling binary code snippets using a model based on Recurrent Neural Networks. The model learns properties and patterns that occur in source code and uses them to produce decompilation output. We train and evaluate our technique on snippets of binary machine code compiled from C source code. The general approach we outline in this paper is not language-specific and requires little or no domain knowledge of a language and its properties or how a compiler operates, making the approach easily extensible to new languages and constructs. Furthermore, the technique can be extended and applied in situations to which traditional decompilers are not targeted, such as for decompilation of isolated binary snippets; fast, on-demand decompilation; domain-specific learned decompilation; optimizing for readability of decompilation; and recovering control flow constructs, comments, and variable or function names. We show that the translations produced by this technique are often accurate or close and can provide a useful picture of the snippet's behavior.


title: State-of-the-Art Intelligent Flight Control Systems in Unmanned Aerial Vehicles

abstract: We discuss state-of-the-art intelligent robotic aircraft with the special focus on evolutionary autopilots for small unmanned aerial vehicles (UAVs). Under the umbrella of adaptive autopilots, we highlight the pros and cons of the most widely implemented intelligent algorithms against the navigational and maneuvering capabilities of small UAVs. We present several cutting-edge applications of bioinspired flight control systems that have the capability of self-learning. We also highlight several research opportunities and challenges associated with each technique. Note to Practitioners —Soft computing methods have been widely implemented in numerous engineering applications. Recent advancements in computational technology have enabled the implementations of intelligent autopilots in real time. This paper aims to discuss many aspects of the developments and implementations of soft computing techniques in aerial robotics with the main focus on its flight control systems.


title: Qualitative Multi-Armed Bandits: A Quantile-Based Approach

abstract: We formalize and study the multi-armed bandit (MAB) problem in a generalized stochastic setting, in which rewards are not assumed to be numerical. Instead, rewards are measured on a qualitative scale that allows for comparison but invalidates arithmetic operations such as averaging. Correspondingly, instead of characterizing an arm in terms of the mean of the underlying distribution, we opt for using a quantile of that distribution as a representative value. We address the problem of quantile-based online learning both for the case of a finite (pure exploration) and infinite time horizon (cumulative regret minimization). For both cases, we propose suitable algorithms and analyze their properties. These properties are also illustrated by means of first experimental studies.


Identifying Efficient Kernel Function in Multiclass Support Vector Machines


title: Does incubation enhance problem solving? A meta-analytic review.

abstract: A meta-analytic review of empirical studies that have investigated incubation effects on problem solving is reported. Although some researchers have reported increased solution rates after an incubation period (i.e., a period of time in which a problem is set aside prior to further attempts to solve), others have failed to find effects. The analysis examined the contributions of moderators such as problem type, presence of solution-relevant or misleading cues, and lengths of preparation and incubation periods to incubation effect sizes. The authors identified a positive incubation effect, with divergent thinking tasks benefiting more than linguistic and visual insight tasks from incubation. Longer preparation periods gave a greater incubation effect, whereas filling an incubation period with high cognitive demand tasks gave a smaller incubation effect. Surprisingly, low cognitive demand tasks yielded a stronger incubation effect than did rest during an incubation period when solving linguistic insight problems. The existence of multiple types of incubation effect provides evidence for differential invocation of knowledge-based vs. strategic solution processes across different classes of problem, and it suggests that the conditions under which incubation can be used as a practical technique for enhancing problem solving must be designed with care.


title: Design of Effective Neural Network Ensembles for Image Classification Purposes

abstract: Abstract In the field of pattern recognition, the combination of an ensemble of neural networks has been proposed as an approach to the development of high performance image classification systems. However, previous work clearly showed that such image classification systems are effective only if the neural networks forming them make different errors. Therefore, the fundamental need for methods aimed to design ensembles of ‘error-independent’ networks is currently acknowledged. In this paper, an approach to the automatic design of effective neural network ensembles is proposed. Given an initial large set of neural networks, our approach is aimed to select the subset formed by the most error-independent nets. Reported results on the classification of multisensor remote-sensing images show that this approach allows one to design effective neural network ensembles.


title: Research of the noise characteristic on the upstream channel for HFC network

abstract: Currently there is a growing demand towards interactive multi-media services using high capacity Hybrid Fiber Coaxial (HFC) networks. With regards to the realization of upstream traffic, many problems occur with the accumulation of noise due to the tree and branch architecture of the coaxial access network. We have figured out the typical network topology for the two-way HFC network, and pointed out where the DS1610s were set. From this figure, we can clear know the reason of noise accumulation and know where and how we've got the characteristic of the damage signal. Several types of typical noise and interference for the upstream channel in actual HFC network are given in this paper, with the help of a special equipment called DS1610. Several parameters of the noise, such as the types, causes, effect to the upstream, and the frequency of appearance and so on, which are given in form of spectrum or constellation, can be easily found in this paper. And it also gives the main characteristics of some typical noise and interference, which can be very useful for further study of the noise and interference for the upstream channel in HFC network.


title: Role of Endothelial Dysfunction in Atherosclerosis

abstract: As the major regulator of vascular homeostasis, the endothelium exerts a number of vasoprotective effects, such as vasodilation, suppression of smooth muscle cell growth, and inhibition of inflammatory responses. Many of these effects are largely mediated by nitric oxide, the most potent endogenous vasodilator. Nitric oxide opposes the effects of endothelium-derived vasoconstrictors and inhibits oxidation of low-density lipoprotein. A defect in the production or activity of nitric oxide leads to endothelial dysfunction, signaled by impaired endothelium-dependent vasodilation. Accumulating evidence suggests that endothelial dysfunction is an early marker for atherosclerosis and can be detected before structural changes to the vessel wall are apparent on angiography or ultrasound. Many of the risk factors that predispose to atherosclerosis can also cause endothelial dysfunction, and the presence of multiple risk factors has been found to predict endothelial dysfunction. A number of clinical trials have shown that 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) improve endothelial dysfunction in patients with coronary risk factors beyond what could be attributed to their impact on plasma lipids. Studies have elucidated several possible mechanisms by which statin therapy may improve endothelial dysfunction, including upregulation of nitric oxide production or activity and reduction of oxidative stress.


title: Angular position measurement using resolvers together with a robust linearization technique

abstract: Resolvers are used for both absolute angular and linear position sensing. A robust amplitude to phase converter is described for the determination of position from the sine and cosine output signals of the resolver. The scheme is based on a linearization technique for converting the co-sinusoids into a nearly perfect triangular signal from which the angle is determined using simple linear equation. Even though the amplitudes of the resolver output signals are proportional to that of the resolver excitation signal, the proposed amplitude to phase technique is robust to fluctuation and drift of the amplitude and frequency of the excitation signal. The theoretical absolute error of non-linearity of the converter is 0.0082 degree over the full 360 degree range. The converter may be implemented numerically or electronically. In this work, details are given for an implementation using basic analogue electronic circuitry The theory of operation and experimental results are given.


title: SIMACT: a 3D Open Source Smart Home Simulator for Activity Recognition with Open Database and Visual Editor

abstract: Smart home technologies have become, in the last few years, a very active topic of research. However, many scientists working in this field do not possess smart home infrastructures allowing them to conduct satisfactory experiments in a concrete environment with real data. To address this issue, this paper presents a new flexible 3D smart home infrastructure simulator, which is developed in Java specifically to help researchers working in the field of activity recognition. A set of pre-recorded scenarios, made with data extracted from clinical trials, will be included with the simulator in order to give a common foundation to test activity recognition algorithms. The goal is to release the SIMACT simulator with a visual scenario editor as an open source component that will benefit the whole smart home research community.


title: HeartChat: Heart Rate Augmented Mobile Chat to Support Empathy and Awareness

abstract: Textual communication via mobile phones suffers from a lack of context and emotional awareness. We present a mobile chat application, HeartChat, which integrates heart rate as a cue to increase awareness and empathy. Through a literature review and a focus group, we identified design dimensions important for heart rate augmented chats. We created three concepts showing heart rate per message, in real-time, or sending it explicitly. We tested our system in a two week in-the-wild study with 14 participants (7 pairs). Interviews and questionnaires showed that HeartChat supports empathy between people, in particular close friends and partners. Sharing heart rate helped them to implicitly understand each other's context (e.g. location, physical activity) and emotional state, and sparked curiosity on special occasions. We discuss opportunities, challenges, and design implications for enriching mobile chats with physiological sensing.


title: Effect of dietary supplementation of Bacillus licheniformis on gut microbiota, growth and immune response in giant freshwater prawn, Macrobrachium rosenbergii (de Man, 1879)

abstract: The present study was conducted to evaluate the effect of dietary supplementation of probiotic bacterium Bacillus licheniformis on the gut microbiota, growth and immune response in Macrobrachium rosenbergii juveniles (4.0 ± 0.02 g). Five isocaloric experimental diets supplemented with different concentrations of B. licheniformis, viz. Control (without probiotic supplementation), T1 (1.0 × 106 cfu g−1feed), T2 (1.0 × 107 cfu g−1feed), T3 (1.0 × 108 cfu g−1 feed) and T4 (1.0 × 109 cfu g−1 feed) were used for the experiment. After 60 days of feeding trial, the growth performance increases significantly in all the experimental groups compared with control, and highest weight gain, specific growth rate and lowest feed conversion ratio were recorded from T4 (P > 0.05). However, juvenile survival was not found to be significantly affected by probiotic supplementation (P > 0.05). The total bacterial count and Bacillus spp. count in the gut were significantly increased with concurrent decrease in pathogenic Aeromonas spp. and Pseudomonas spp. in all the experimental groups compared with control (P < 0.05). Compared with control, immune parameters were increased significantly in all the experimental groups (P < 0.05). Highest haemocyte count, phenol oxidase, superoxide dismutase and antibacterial activity in haemolymph were recorded from T4 (P < 0.05). When prawns were challenged with Vibrio alginolyticus, the cumulative mortality was significantly lower in T4 (P < 0.05). These results indicate that dietary supplementation of B. licheniformis can manipulate the gut microbiota and increase the growth and immune response of M. rosenbergii. In the present study, concentration response on the probiotic effect of B. licheniformis was observed which showed higher effects at a concentration of 1.0 × 109 cfu g−1 feed in the laboratory condition.


title: A first look at problems in the cloud

abstract: Cloud computing provides a revolutionary model for the deployment of enterprise applications and Web services alike. In this new model, cloud users save on the cost of purchasing and managing base infrastructure, while the cloud providers save on the cost of maintaining underutilized CPU, memory, and network resources. Inmigrating to this new model, users face a variety of issues. Commercial clouds provide several support models to aide users in resolving the reported issues ::: ::: This paper arises from our quest to understand how to design IaaS support models for more efficient user troubleshooting. Using a data driven approach, we start our exploration into this issue with an investigation into the problems encountered by users and the methods utilized by the cloud support's staff to resolve these problems. We examine message threads appearing in the forum of a large IaaS provider over a 3 year period. We argue that the lessons derived from this study point to a set of principles that future IaaS offerings can implement to provide users with a more efficient support model. This data driven approach enables us to propose a set of principles that are pertinent to the experiences of users and that we believe could vastly improve the SLA observed by the users.


title: Mitigating program security vulnerabilities: Approaches and challenges

abstract: Programs are implemented in a variety of languages and contain serious vulnerabilities which might be exploited to cause security breaches. These vulnerabilities have been exploited in real life and caused damages to related stakeholders such as program users. As many security vulnerabilities belong to program code, many techniques have been applied to mitigate these vulnerabilities before program deployment. Unfortunately, there is no comprehensive comparative analysis of different vulnerability mitigation works. As a result, there exists an obscure mapping between the techniques, the addressed vulnerabilities, and the limitations of different approaches. This article attempts to address these issues. The work extensively compares and contrasts the existing program security vulnerability mitigation techniques, namely testing, static analysis, and hybrid analysis. We also discuss three other approaches employed to mitigate the most common program security vulnerabilities: secure programming, program transformation, and patching. The survey provides a comprehensive understanding of the current program security vulnerability mitigation approaches and challenges as well as their key characteristics and limitations. Moreover, our discussion highlights the open issues and future research directions in the area of program security vulnerability mitigation.


title: Positive priming and intentional binding: Eye-blink rate predicts reward information effects on the sense of agency

abstract: Human society is strongly rooted in people's experiences of agency; that is, the pervasive feeling that one engages in voluntary behavior and causes one's own actions and resulting outcomes. Rewards and positive affect play an important role in the control of voluntary action. However, the role of positive reward signals in the sense of agency is poorly understood. This study examined effects of reward-related information on the sense of agency by employing the intentional binding paradigm. This paradigm measures the extent to which actions and their effects subjectively shift together across time, reflecting a crucial component of people's sense of agency. Results showed that intentional binding is stronger when participants are primed with reward-related information via brief exposure to positive pictures. Interestingly, this positive priming effect was moderated by baseline eye-blink rates (an indirect marker of striatal dopaminergic functioning); reward-related information increased intentional binding mainly for participants displaying higher baseline eye-blink rates. These findings suggest a possible role for striatal dopamine activity in the process by which reward-related information shapes the way people see themselves as agents.


title: Probabilistic reasoning by neurons

abstract: Our brains allow us to reason about alternatives and to make choices that are likely to pay off. Often there is no one correct answer, but instead one that is favoured simply because it is more likely to lead to reward. A variety of probabilistic classification tasks probe the covert strategies that humans use to decide among alternatives based on evidence that bears only probabilistically on outcome. Here we show that rhesus monkeys can also achieve such reasoning. We have trained two monkeys to choose between a pair of coloured targets after viewing four shapes, shown sequentially, that governed the probability that one of the targets would furnish reward. Monkeys learned to combine probabilistic information from the shape combinations. Moreover, neurons in the parietal cortex reveal the addition and subtraction of probabilistic quantities that underlie decision-making on this task.


title: Deep Learning in Medical Imaging: General Overview

abstract: The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.


title: An efficient data collection and dissemination for IOT based WSN

abstract: Data collection issues play a significant role in the area of Wireless Sensor Networks (WSNs) and Internet of Things (IOT). Energy efficiency is affected by the number of data transmissions from the node to the Sink / base station (BS). Control of the update data is one of the feasible ways to reduce the messages sent by the nodes. In transmitting data strategies most of the WSN real-time data applications update the data directly, and some current study made a logical or absolute check to measure the change between the current and last sensor measuring value before updating the data. In this paper, we proposed a method to reduce the number of data transmissions as well as decrease the amount of data that will result in prolonging the lifetime of the network. The proposed method aims to reduce the number of transmitted messages via a node supporting single and multiple sensors based on the relative change or relative differences between the current and last sensor measured values transmitted. The results show the proposed method appears to deliver the best performance in terms of reducing the number of messages transitions and packets size, in this study the mean percentage of reducing the number of messages transmissions is 74%, and 80% is the mean percentage reduce of node packet size. From the results, it's clear that reduce the number of packet's transitions and reduce the node packet size, will result in a decrease the energy consumption as well as prolonging the lifetime of the system.


title: A confidence-based filtering method for DDoS attack defense in cloud environment

abstract: Distributed Denial-of-Service attack (DDoS) is a major threat for cloud environment. Traditional defending approaches cannot be easily applied in cloud security due to their relatively low efficiency, large storage, to name a few. In view of this challenge, a Confidence-Based Filtering method, named CBF, is investigated for cloud computing environment, in this paper. Concretely speaking, the method is deployed by two periods, i.e., non-attack period and attack period. More specially, legitimate packets are collected in the non-attack period, for extracting attribute pairs to generate a nominal profile. With the nominal profile, the CBF method is promoted by calculating the score of a particular packet in the attack period, to determine whether to discard it or not. At last, extensive simulations are conducted to evaluate the feasibility of the CBF method. The result shows that CBF has a high scoring speed, a small storage requirement, and an acceptable filtering accuracy. It specifically satisfies the real-time filtering requirements in cloud environment.


title: RFID Systems and Security and Privacy Implications

abstract: The Auto-ID Center is developing low-cost radio frequency identification (RFID) based systems with the initial application as next generation bar-codes. We describe RFID technology, summarize our approach and our research, and most importantly, describe the research opportunities in RFID for experts in cryptography and information security. The common theme in low-cost RFID systems is that computation resources are very limited, and all aspects of the RFID system are connected to each other. Understanding these connections and the resulting design trade-offs is an important prerequisite to effectively answering the challenges of security and privacy in low-cost RFID systems.


title: Bluetooth in Intelligent Transportation Systems: A Survey

abstract: The rise of Bluetooth-equipped devices in personal consumer electronics and in in-car systems has revealed the potential to develop Bluetooth sensor systems for applications in intelligent transportation systems. These applications may include measurements of traffic presence, density, and flow, as well as longitudinal and comparative traffic analysis. A basic Bluetooth sensor system for traffic monitoring consists of a Bluetooth probe device (s) that scans for other Bluetooth-enabled device (s) within its radio proximity, and then stores the data for future analysis and use. The scanned devices are typically on-board vehicular electronics and consumer devices carried by the driver and/or passengers which use Bluetooth communications, and which then reasonably proxy for the vehicle itself. This paper surveys the scope and evolution of these systems, with system attributes and design decisions illustrated via a reference design. The work provides motivation for continued development of non-invasive systems that leverage the existing communication infrastructure and consumer devices that incorporate short range communication technology like Bluetooth.


title: Expert Finding for Question Answering via Graph Regularized Matrix Completion

abstract: Expert finding for question answering is a challenging problem in community-based question answering (CQA) systems, arising in many real applications such as question routing and identification of best answers. In order to provide high-quality experts, many existing approaches learn the user model from their past question-answering activities in CQA systems. However, the past activities of users in most CQA systems are rather few, and thus the user model may not be well inferred in practice. In this paper, we consider the problem of expert finding from the viewpoint of missing value estimation. We then employ users’ social networks for inferring user model, and thus improve the performance of expert finding in CQA systems. In addition, we develop a novel graph-regularized matrix completion algorithm for inferring the user model. We further develop two efficient iterative procedures, GRMC-EGM and GRMC-AGM, to solve the optimization problem. GRMC-EGM utilizes the Extended Gradient Method (EGM), while GRMC-AGM applies the Accelerated proximal Gradient search Method (AGM), for the optimization. We evaluate our methods on the well-known question answering system Quora, and the popular social network Twitter. Our empirical study shows the effectiveness of the proposed algorithms in comparison to the state-of-the-art expert finding algorithms.


title: Reconciliation of RDF* and Property Graphs

abstract: Both the notion of Property Graphs (PG) and the Resource Description Framework (RDF) are commonly used models for representing graph-shaped data. While there exist some system-specific solutions to convert data from one model to the other, these solutions are not entirely compatible with one another and none of them appears to be based on a formal foundation. In fact, for the PG model, there does not even exist a commonly agreed-upon formal definition. ::: The aim of this document is to reconcile both models formally. To this end, the document proposes a formalization of the PG model and introduces well-defined transformations between PGs and RDF. As a result, the document provides a basis for the following two innovations: On one hand, by implementing the RDF-to-PG transformations defined in this document, PG-based systems can enable their users to load RDF data and make it accessible in a compatible, system-independent manner using, e.g., the graph traversal language Gremlin or the declarative graph query language Cypher. On the other hand, the PG-to-RDF transformation in this document enables RDF data management systems to support compatible, system-independent queries over the content of Property Graphs by using the standard RDF query language SPARQL. Additionally, this document represents a foundation for systematic research on relationships between the two models and between their query languages.


title: Promoting Cooperation by the Social Incentive Mechanism in Mobile Crowdsensing

abstract: An incentive mechanism is important for mobile crowdsensing to recruit sufficient participants to complete large-scale sensing tasks with high quality. Previous incentive mechanisms have focused on quantifying participants' contribution to the quality of sensing and provide incentives directly to them. In this article, we introduce a novel approach, called the social incentive mechanism, which, surprisingly, incentivizes the social friends of the participants who perform the sensing tasks. The basic idea is to leverage the social ties among participants to promote global cooperation. Since the incentive that a participant receives largely relies on the behaviors of his/her social friends, participants have the motivation to impact their friends' behaviors through their social relationships in order to gain a higher payoff. This approach is applicable to many scenarios where the contributions to the quality of sensing among participants are interdependent, such as data aggregation. We have provided a case study which shows that the social incentive mechanism is more cost-effective than traditional incentive mechanisms.


title: Implementation of Competency-Based Pharmacy Education (CBPE)

abstract: Implementation of competency-based pharmacy education (CBPE) is a time-consuming, complicated process, which requires agreement on the tasks of a pharmacist, commitment, institutional stability, and a goal-directed developmental perspective of all stakeholders involved. In this article the main steps in the development of a fully-developed competency-based pharmacy curriculum (bachelor, master) are described and tips are given for a successful implementation. After the choice for entering into CBPE is made and a competency framework is adopted (step 1), intended learning outcomes are defined (step 2), followed by analyzing the required developmental trajectory (step 3) and the selection of appropriate assessment methods (step 4). Designing the teaching-learning environment involves the selection of learning activities, student experiences, and instructional methods (step 5). Finally, an iterative process of evaluation and adjustment of individual courses, and the curriculum as a whole, is entered (step 6). Successful implementation of CBPE requires a system of effective quality management and continuous professional development as a teacher. In this article suggestions for the organization of CBPE and references to more detailed literature are given, hoping to facilitate the implementation of CBPE.


title: GHTorrent: GitHub's data from a firehose

abstract: A common requirement of many empirical software engineering studies is the acquisition and curation of data from software repositories. During the last few years, GitHub has emerged as a popular project hosting, mirroring and collaboration platform. GitHub provides an extensive rest api, which enables researchers to retrieve both the commits to the projects' repositories and events generated through user actions on project resources. GHTorrent aims to create a scalable off line mirror of GitHub's event streams and persistent data, and offer it to the research community as a service. In this paper, we present the project's design and initial implementation and demonstrate how the provided datasets can be queried and processed.


Social Networking Privacy Behaviors and Risks


title: Transformational Leadership and Job Satisfaction

abstract: A vast literature describes transformational leadership in terms of leader having charismatic and inspiring personality, stimulating followers, and providing them with individualized consideration. A considerable empirical support exists for transformation leadership in terms of its positive effect on followers with respect to criteria like effectiveness, extra role behaviour and organizational learning. This study aims to explore the effect of transformational leadership characteristics on followers' job satisfaction. Survey method was utilized to collect the data from the respondents. The study reveals that individualized consideration and intellectual stimulation affect followers' job satisfaction. However, intellectual stimulation is positively related with job satisfaction and individualized consideration is negatively related with job satisfaction. Leader's charisma or inspiration was found to be having no affect on the job satisfaction. The three aspects of transformational leadership were tested against job satisfaction through structural equation modeling using Amos.


Acoustic Modeling Using Bidirectional Gated Recurrent Convolutional Units.


title: Helminths of Ross' and Greater White-fronted Geese Wintering in South Texas, U.S.A.

abstract: Abstract Helminth community structure and pattern were assessed in 16 Ross' geese, Chen rossii, and 46 greater white-fronted geese, Anser albifrons, collected during winter 1999–2000 in Kleberg County, Texas, U.S.A. Helminths found in individual Ross' geese ranged from 1 to 6 species and from 1 to 95 individuals; infracommunities averaged 3.5 ± 0.3 (SE) species and 42.5 ± 7.7 individuals. Ten species were found in the Ross' goose component community, in which Amidostomum anseris, Epomidiostomum crami, Heterakis dispar, and Trichostrongylus tenuis were the most prevalent and numerically dominant. Helminths found in individual white-fronted geese ranged from 1 to 7 species and from 4 to 117 individuals; infracommunities averaged 4.2 ± 0.2 species and 28.9 ± 4.0 individuals. Sixteen species were found in the white-fronted goose component community. Epomidiostomum crami, Amidostomum spatulatum, and T. tenuis were the most prevalent and numerically dominant. In white-fronted geese, chi-square and analysis of v...


title: Self–Forgiveness: The Stepchild of Forgiveness Research

abstract: Although research on interpersonal forgiveness is burgeoning, there is little conceptual or empirical scholarship on self–forgiveness. To stimulate research on this topic, a conceptual analysis of self–forgiveness is offered in which self–forgiveness is defined and distinguished from interpersonal forgiveness and pseudo self–forgiveness. The conditions under which self–forgiveness is appropriate also are identified. A theoretical model describing the processes involved in self–forgiveness following the perpetration of an interpersonal transgression is outlined and the proposed emotional, social–cognitive, and offense–related determinants of self–forgiveness are described. The limitations of the model and its implications for future research are explored.


title: IoT Security Techniques Based on Machine Learning

abstract: Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine learning based IoT authentication, access control, secure offloading and malware detection schemes to protect data privacy. In this article, we discuss the challenges that need to be addressed to implement these machine learning based security schemes in practical IoT systems.


title: From information to knowledge: harvesting entities and relationships from web sources

abstract: There are major trends to advance the functionality of search engines to a more expressive semantic level. This is enabled by the advent of knowledge-sharing communities such as Wikipedia and the progress in automatically extracting entities and relationships from semistructured as well as natural-language Web sources. Recent endeavors of this kind include DBpedia, EntityCube, KnowItAll, ReadTheWeb, and our own YAGO-NAGA project (and others). The goal is to automatically construct and maintain a comprehensive knowledge base of facts about named entities, their semantic classes, and their mutual relations as well as temporal contexts, with high precision and high recall. This tutorial discusses state-of-the-art methods, research opportunities, and open challenges along this avenue of knowledge harvesting.


title: The Rewarding Aspects of Music Listening Are Related to Degree of Emotional Arousal

abstract: BACKGROUND ::: Listening to music is amongst the most rewarding experiences for humans. Music has no functional resemblance to other rewarding stimuli, and has no demonstrated biological value, yet individuals continue listening to music for pleasure. It has been suggested that the pleasurable aspects of music listening are related to a change in emotional arousal, although this link has not been directly investigated. In this study, using methods of high temporal sensitivity we investigated whether there is a systematic relationship between dynamic increases in pleasure states and physiological indicators of emotional arousal, including changes in heart rate, respiration, electrodermal activity, body temperature, and blood volume pulse. ::: ::: ::: METHODOLOGY ::: Twenty-six participants listened to self-selected intensely pleasurable music and "neutral" music that was individually selected for them based on low pleasure ratings they provided on other participants' music. The "chills" phenomenon was used to index intensely pleasurable responses to music. During music listening, continuous real-time recordings of subjective pleasure states and simultaneous recordings of sympathetic nervous system activity, an objective measure of emotional arousal, were obtained. ::: ::: ::: PRINCIPAL FINDINGS ::: Results revealed a strong positive correlation between ratings of pleasure and emotional arousal. Importantly, a dissociation was revealed as individuals who did not experience pleasure also showed no significant increases in emotional arousal. ::: ::: ::: CONCLUSIONS/SIGNIFICANCE ::: These results have broader implications by demonstrating that strongly felt emotions could be rewarding in themselves in the absence of a physically tangible reward or a specific functional goal.


title: Magnetism and microfluidics

abstract: Magnetic forces are now being utilised in an amazing variety of microfluidic applications. Magnetohydrodynamic flow has been applied to the pumping of fluids through microchannels. Magnetic materials such as ferrofluids or magnetically doped PDMS have been used as valves. Magnetic microparticles have been employed for mixing of fluid streams. Magnetic particles have also been used as solid supports for bioreactions in microchannels. Trapping and transport of single cells are being investigated and recently, advances have been made towards the detection of magnetic material on-chip. The aim of this review is to introduce and discuss the various developments within the field of magnetism and microfluidics.


Object-centric spatial pooling for image classification


title: Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data

abstract: Abstract Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the robust time series cluster kernel (TCK). The approach taken leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with informative prior distributions. An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel. We evaluate the TCK on synthetic and real data and compare to other state-of-the-art techniques. The experimental results demonstrate that the TCK is robust to parameter choices, provides competitive results for MTS without missing data and outstanding results for missing data.


title: Snow Harvesting: A Potential Water Source for Afghanistan

abstract: Abstract : Afghanistan has limited water sources, and the possibility of using a snow fence to concentrate, or "collect," snow to provide a supple-mental source of water, upon melting, in the Bamian region was examined. In Afghan villages, women and/or children often fetch water for their families. Improving water usage efficiency could improve agricultural pro-ductivity, reduce the number of trips to the nearest water source, and provide healthier daily nutritional intake. Snow fences are currently used to manage the distribution of snow for the purposes of supplementing water supply. Hence, the researchers determined the average amount of water required for sheep and cows per village using demographic data. Then, they compared this volume of water to the equivalent volume of snow that can be captured with a snow fence, based on current knowledge of snow fence function. Analysis of topographic and meteorological data indicated that many areas in Bamian are favorable for collection of snow for eventual water harvesting. A 100-m-wide "solid" fencing (e.g., rocks), approximately 3 m high, was estimated to be able to provide 16%-17% of a "typical" Bamian village's livestock water, whereas a highly efficient, 50% porous fence could provide approximately 47% of the livestock water. The research team's recommendation was to further assess implementation of the snow fence water-collection method as a supplementary water source in rural areas of Afghanistan that receive annual snowfall.


title: An Ultra Low Energy 12-bit Rate-Resolution Scalable SAR ADC for Wireless Sensor Nodes

abstract: A resolution-rate scalable ADC for micro-sensor networks is described. Based on the successive approximation register (SAR) architecture, this ADC has two resolution modes: 12 bit and 8 bit, and its sampling rate is scalable, at a constant figure-of-merit, from 0-100 kS/s and 0-200 kS/s, respectively. At the highest performance point (i.e., 12 bit, 100 kS/s), the entire ADC (including digital, analog, and reference power) consumes 25 muW from a 1-V supply. The ADC's CMRR is enhanced by common-mode independent sampling and passive auto-zero reference generation. The efficiency of the comparator is improved by an analog offset calibrating latch, and the preamplifier settling time is relaxed by self-timing the bit-decisions. Prototyped in a 0.18-mum, 5M2P CMOS process, the ADC, at 12 bit, 100 kS/s, achieves a Nyquist SNDR of 65 dB (10.55 ENOB) and an SFDR of 71 dB. Its INL and DNL are 0.68 LSB and 0.66 LSB, respectively


How can we promote understanding and fluency in learning from multiple representations ? Intelligent Tutoring System support for connection making


title: On the relationship between emotion and cognition

abstract: The current view of brain organization supports the notion that there is a considerable degree of functional specialization and that many regions can be conceptualized as either 'affective' or 'cognitive'. Popular examples are the amygdala in the domain of emotion and the lateral prefrontal cortex in the case of cognition. This prevalent view is problematic for a number of reasons. Here, I will argue that complex cognitive-emotional behaviours have their basis in dynamic coalitions of networks of brain areas, none of which should be conceptualized as specifically affective or cognitive. Central to cognitive-emotional interactions are brain areas with a high degree of connectivity, called hubs, which are critical for regulating the flow and integration of information between regions.


Suprisal-Driven Zoneout


title: Parametric study of UWB antenna loaded with stacked parasitic patch and reflector

abstract: This paper presents a parametric study of a UWB antenna loaded with parasitic patches on the front and metal sheets as reflectors on the back side. A maximum increase of 2.6dB in gain with a maximum gain of 10.4dBi was observed by increasing the number of parasitic patches but keeping their sizes fixed. Similarly by varying the size of the parasitic patches, gain increased by 5dB. Finally increasing the size of the metal reflector showed an increase of 3.5dB in gain. This parametric study provides the design guidelines for the best configuration of UWB antenna integrated with parasitic patches and metal reflectors.


title: Exploiting spectro-temporal locality in deep learning based acoustic event detection

abstract: In recent years, deep learning has not only permeated the computer vision and speech recognition research fields but also fields such as acoustic event detection (AED). One of the aims of AED is to detect and classify non-speech acoustic events occurring in conversation scenes including those produced by both humans and the objects that surround us. In AED, deep learning has enabled modeling of detail-rich features, and among these, high resolution spectrograms have shown a significant advantage over existing predefined features (e.g., Mel-filter bank) that compress and reduce detail. In this paper, we further asses the importance of feature extraction for deep learning-based acoustic event detection. AED, based on spectrogram-input deep neural networks, exploits the fact that sounds have “global” spectral patterns, but sounds also have “local” properties such as being more transient or smoother in the time-frequency domain. These can be exposed by adjusting the time-frequency resolution used to compute the spectrogram, or by using a model that exploits locality leading us to explore two different feature extraction strategies in the context of deep learning: (1) using multiple resolution spectrograms simultaneously and analyzing the overall and event-wise influence to combine the results, and (2) introducing the use of convolutional neural networks (CNN), a state of the art 2D feature extraction model that exploits local structures, with log power spectrogram input for AED. An experimental evaluation shows that the approaches we describe outperform our state-of-the-art deep learning baseline with a noticeable gain in the CNN case and provides insights regarding CNN-based spectrogram characterization for AED.


title: Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs

abstract: Existing Bayesian models, especially nonparametric Bayesian methods, rely on specially conceived priors to incorporate domain knowledge for discovering improved latent representations. While priors affect posterior distributions through Bayes' rule, imposing posterior regularization is arguably more direct and in some cases more natural and general. In this paper, we present regularized Bayesian inference (RegBayes), a novel computational framework that performs posterior inference with a regularization term on the desired post-data posterior distribution under an information theoretical formulation. RegBayes is more flexible than the procedure that elicits expert knowledge via priors, and it covers both directed Bayesian networks and undirected Markov networks. When the regularization is induced from a linear operator on the posterior distributions, such as the expectation operator, we present a general convex-analysis theorem to characterize the solution of RegBayes. Furthermore, we present two concrete examples of RegBayes, infinite latent support vector machines (iLSVM) and multi-task infinite latent support vector machines (MT-iLSVM), which explore the large-margin idea in combination with a nonparametric Bayesian model for discovering predictive latent features for classification and multi-task learning, respectively. We present efficient inference methods and report empirical studies on several benchmark data sets, which appear to demonstrate the merits inherited from both large-margin learning and Bayesian nonparametrics. Such results contribute to push forward the interface between these two important subfields, which have been largely treated as isolated in the community.


Attention-based Transactional Context Embedding for Next-Item Recommendation


title: Throughput analysis for wireless networks with full-duplex radios

abstract: This paper investigates the throughput for wireless network with full-duplex radios using stochastic geometry. Full-duplex (FD) radios can exchange data simultaneously with each other. On the other hand, the downside of FD transmission is that it will inevitably cause extra interference to the network compared to half-duplex (HD) transmission. In this paper, we focus on a wireless network of nodes with both HD and FD capabilities and derive and optimize the throughput in such a network. Our analytical result shows that if the network is adapting an ALOHA protocol, the maximal throughput is always achieved by scheduling all concurrently transmitting nodes to work in FD mode instead of a mixed FD/HD mode or HD mode regardless of the network configurations. Moreover, the throughput gain of using FD transmission over HD transmission is analytically lower and upper bounded.


title: Batch-incremental versus instance-incremental learning in dynamic and evolving data

abstract: Many real world problems involve the challenging context of data streams, where classifiers must be incremental: able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Two approaches dominate the literature: batch-incremental methods that gather examples in batches to train models; and instance-incremental methods that learn from each example as it arrives. Typically, papers in the literature choose one of these approaches, but provide insufficient evidence or references to justify their choice. We provide a first in-depth analysis comparing both approaches, including how they adapt to concept drift, and an extensive empirical study to compare several different versions of each approach. Our results reveal the respective advantages and disadvantages of the methods, which we discuss in detail.


title: A reliable MTD design for MLC flash-memory storage systems

abstract: The reliability of flash-memory chips has dropped dramatically in recent years. In order to solve this problem, a reliable memory technology device (MTD) design is proposed to address this concern at the device driver layer so as to release the design complexity of flash-memory management software/firmware and to improve the maintainability and portability of flash management designs for existing and future products. The proposed design was evaluated through a series of experiments based on realistic traces to show that the proposed approach could significantly improve the reliability of flash memory with limited overheads.


title: Spacing practice sessions across days earlier rather than later in training improves performance of a visuomotor skill

abstract: Our goal was to determine whether the extent of off-line performance improvements on a visuomotor task depends on the amount of practice individuals experience prior to a 24-h between-session break. Subjects completed ten trials of a mirror-tracing task over two days. On Day 1, subjects experienced either one, three or seven trials. Twenty-four hours later subjects completed the remainder of the ten trials. Despite experiencing an equivalent number of total training trials, subjects experiencing the 24-h delay after one or three trials demonstrated off-line performance improvements, but those experiencing the delay after seven trials did not. Furthermore, the one- and three-trial groups reached a superior level of performance by the end of training relative to the seven-trial group.


Design of a novel wheeled tensegrity robot: a comparison of tensegrity concepts and a prototype for travelling air ducts


title: Hardware implementation of echo state networks using memristor double crossbar arrays

abstract: Neuromorphic computing systems are inspired by humans brains, where data are stored and processed at the same location. Contrary to von Neumann systems, neuromorphic computing systems offer excellent real-time processing for huge data sizes, at low costs and power consumption. Most of these systems rely on emerging new devices, such as memristors, to build crossbar arrays implementing different neural network topologies. The Echo State Network model is a special type of recurrent neural networks, which can correctly represent spatiotemporal dataset. In this paper, a new hardware implementation design for the Echo State Network model using memristor double crossbar arrays is proposed. Moreover, a detailed design procedure is proposed for designing and simulating the proposed architecture. The system has been evaluated using the ubiquitous Mackey-Glass dataset showing promising results, compared to the software implementation of the model. In addition, the system shows excellent immunity against memristor process variations.


title: Design of High-Isolation Wideband Dual-Polarized Compact MIMO Antennas With Multiobjective Optimization

abstract: Multiobjective optimization design of compact multiple-input and multiple-output (MIMO) antennas with canonical structures (viz., transformation or combination by regular shapes) for wideband radiation and fragment structures (viz., combination by discrete fragment patches) for high isolation is proposed and demonstrated. In the design, multiobjective evolutionary algorithm based on decomposition combined with differential evolution (MOEA/D-DE) for optimization of canonical structures and MOEA/D combined with genetic operator (MOEA/D-GO) for optimization of fragment structures are combined and iterated. The design is demonstrated with compact bow-tie MIMO antennas sharing a small common ground plane. The four orthogonally deployed bow-tie antennas are optimized by using MOEA/D-DE to acquire dual polarization and wideband radiation. Fragment-type structures on the common ground plane are optimized by using MOEA/D-GO to acquire very high isolation. Both simulation and measurement show that the optimized MIMO bow-tie antennas provide isolation higher than 30 dB in a relative bandwidth of 40% (from 2.6 to 3.9 GHz). With the proposed design technique, dual-band high-isolation MIMO antennas may also be designed.


title: Earpod: eyes-free menu selection using touch input and reactive audio feedback

abstract: We present the design and evaluation of earPod: an eyes-free menu technique using touch input and reactive auditory feedback. Studies comparing earPod with an iPod-like visual menu technique on reasonably-sized static menus indicate that they are comparable in accuracy. In terms of efficiency (speed), earPod is initially slower, but outperforms the visual technique within 30 minutes of practice. Our results indicate that earPod is potentially a reasonable eyes-free menu technique for general use, and is a particularly exciting technique for use in mobile device interfaces.


title: The cellular neurobiology of depression

abstract: Major depressive disorders, long considered to be of neurochemical origin, have recently been associated with impairments in signaling pathways that regulate neuroplasticity and cell survival. Agents designed to directly target molecules in these pathways may hold promise as new therapeutics for depression.


Systems Architecting for a Retrofittable Subsea System Application


title: Chitosan-Decorated Doxorubicin-Encapsulated Nanoparticle Targets and Eliminates Tumor Reinitiating Cancer Stem-like Cells

abstract: Tumor reinitiating cancer stem-like cells are responsible for cancer recurrence associated with conventional chemotherapy. We developed a doxorubicin-encapsulated polymeric nanoparticle surface-decorated with chitosan that can specifically target the CD44 receptors of these cells. This nanoparticle system was engineered to release the doxorubicin in acidic environments, which occurs when the nanoparticles are localized in the acidic tumor microenvironment and when they are internalized and localized in the cellular endosomes/lysosomes. This nanoparticle design strategy increases the cytotoxicity of the doxorubicin by six times in comparison to the use of free doxorubicin for eliminating CD44+ cancer stem-like cells residing in 3D mammary tumor spheroids (i.e., mammospheres). We further show these nanoparticles reduced the size of tumors in an orthotopic xenograft tumor model with no evident systemic toxicity. The development of nanoparticle system to target cancer stem-like cells with low systemic toxicit...


title: Magic Mirror: A virtual handbag shopping system

abstract: We present an augmented reality system based on Kinect for on-line handbag shopping. The users can virtually try on different handbags on a TV screen at home. They can interact with the virtual handbags naturally, such as sliding a handbag to different positions on their arms and rotating a handbag to see it from different angles. The users can also see how the handbags fit them in different virtual environments other than the current real background. We describe the technical details for building such a system and demonstrate the experimental results.


title: Cyber Attack Classification using Game Theoretic Weighted Metrics Approach

abstract: We propose a classification approach of cyber attack which uses characteristics metrics and game theoretic approach to classify the attacks to their closest category. The standard weights of the metrics are used as the base line to classify the cyber attacks in the proper category. The approach is simple and extendible; as new characters of the newly identified attacks can be added to the attack characteristic metrics and the corresponding unique weight to the character are assigned by the proposed formula. Besides this, the proposed approach clearly represents the cause effect relationship for all possible attacks which helps us to find the appropriate solution to restrict them in the Internet. A case study is also provided in the paper to classify Nimda worm, Slammer worm, W32/HLLP.Philips.jn Trojan, Stealth virus and Boot sector virus.


title: Long-term biosolids application alters the composition of soil microbial groups and nutrient status in a pine plantation

abstract: Tree nutrient concentrations, root biomass, soil nitrogen (N) and phosphorus (P) contents, and enzyme activities were determined in a radiata pine plantation (Pinus radiata D. Don) on a sandy loam soil receiving 19 years of repeated biosolids applications, and the composition of soil microbial groups was assessed using phospholipid fatty acid analysis. Biosolids application significantly increased soil organic N, available P and foliar N concentration, but had no significant impact on foliar P concentration over the long term. Biosolids application improved aboveground tree growth but reduced fine-root biomass. Repeated biosolids applications also altered the composition of soil microbial community and increased the potential activities of N-acetyl-β-glucosaminidase and acid phosphomonoesterase. Changes in soil N and P availability were better explained by variations in the composition of bacterial groups than those of fungal groups. The fungi-to-bacteria ratios were not changed, but arbuscular mycorrhizal fungi were suppressed possibly due to elevated soil P caused by biosolids application. The ratios of Gram-positive to Gram-negative bacteria were significantly increased by biosolids application and were linked to increased N mineralisation rates. In addition, it is evident that variations in soil P availability and the composition of soil microbial groups following biosolids application were not significantly related to foliar P status. These findings contribute to a better understanding of the effects of biosolids application on microbial-mediated soil N and P processes.


title: Inspectability and control in social recommenders

abstract: Users of social recommender systems may want to inspect and control how their social relationships influence the recommendations they receive, especially since recommendations of social recommenders are based on friends rather than anonymous "nearest neighbors". We performed an online user experiment (N=267) with a Facebook music recommender system that gives users control over the recommendations, and explains how they came about. The results show that inspectability and control indeed increase users' perceived understanding of and control over the system, their rating of the recommendation quality, and their satisfaction with the system.


title: Review: Graph Databases

abstract: This paper covers NOSQL databases. With the increase in net users and applications it is important to understand graph databases, the future of tomorrow's data storage and management. Basic architecture of some graph databases has been discussed to know their working and how data is stored in the form of nodes and relationships.


title: Dynamic Bayesian Networks for Student Modeling

abstract: Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge Tracing (BKT) is a popular approach for student modeling. The structure of BKT models, however, makes it impossible to represent the hierarchy and relationships between the different skills of a learning domain. Dynamic Bayesian networks (DBN) on the other hand are able to represent multiple skills jointly within one model. In this work, we suggest the use of DBNs for student modeling. We introduce a constrained optimization algorithm for parameter learning of such models. We extensively evaluate and interpret the prediction accuracy of our approach on five large-scale data sets of different learning domains such as mathematics, spelling learning, and physics. We furthermore provide comparisons to previous student modeling approaches and analyze the influence of the different student modeling techniques on instructional policies. We demonstrate that our approach outperforms previous techniques in prediction accuracy on unseen data across all learning domains and yields meaningful instructional policies.


title: Interference analysis: From 2G to 4G

abstract: Unfortunately, better throughputs are continuously requested mobile networks face spectral resource scarcity. Consequently, spectrum resource's use must be optimized for that operators have introduced frequency reuse mechanism. Unfortunately, this mechanism is limited by co-channel interference and adjacent channel interference. An Orthogonal Frequency Division Multiple Access (OFDMA) has been introduced which was also better enhanced by femto cells concept. From this work it can be deduced that the Signal to co-channel interference ratio power does not exceed 14.1 dB for a user in edge cell whereas it can achieved 15.3 dB for a user in center cell with the same path loss value (γ=3.5). The signal to adjacent channel interference power ratio decreases from 20.7 w to 6.9 w if the number of active users in the neighbor interfering cell is increased from 10 to 30. However, from the fourth generation's study, we deduce that macro user's performances are reversely varied with femto cells number. In fact, SINR decreases from 10 dB to −5 dB if we increase the number of femto cells from 1 to 7.


title: Bayesian Inverse Synthetic Aperture Radar Imaging

abstract: In this letter, a novel algorithm of inverse synthetic aperture radar (ISAR) imaging based on Bayesian estimation is proposed, wherein the ISAR imaging joint with phase adjustment is mathematically transferred into signal reconstruction via maximum a posteriori estimation. In the scheme, phase errors are treated as model errors and are overcome in the sparsity-driven optimization regardless of the formats, while data-driven estimation of the statistical parameters for both noise and target is developed, which guarantees the high precision of image generation. Meanwhile, the fast Fourier transform is utilized to implement the solution to image formation, promoting its efficiency effectively. Due to the high denoising capability of the proposed algorithm, high-quality image also could be achieved even under strong noise. The experimental results using simulated and measured data confirm the validity.


Multi-view dimensionality reduction via canonical correlation analysis


title: Sharp Minima Can Generalize For Deep Nets

abstract: Despite their overwhelming capacity to overfit, deep learning architectures tend to generalize relatively well to unseen data, allowing them to be deployed in practice. However, explaining why this is the case is still an open area of research. One standing hypothesis that is gaining popularity, e.g. Hochreiter & Schmidhuber (1997); Keskar et al. (2017), is that the flatness of minima of the loss function found by stochastic gradient based methods results in good generalization. This paper argues that most notions of flatness are problematic for deep models and can not be directly applied to explain generalization. Specifically, when focusing on deep networks with rectifier units, we can exploit the particular geometry of parameter space induced by the inherent symmetries that these architectures exhibit to build equivalent models corresponding to arbitrarily sharper minima. Furthermore, if we allow to reparametrize a function, the geometry of its parameters can change drastically without affecting its generalization properties.


title: Semantic Parsing with Syntax- and Table-Aware SQL Generation

abstract: We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query word-by-word, however, a large portion of the generated results are incorrect or not executable due to the mismatch between question words and table contents. Our approach addresses this problem by considering the structure of table and the syntax of SQL language. The quality of the generated SQL query is significantly improved through (1) learning to replicate content from column names, cells or SQL keywords; and (2) improving the generation of WHERE clause by leveraging the column-cell relation. Experiments are conducted on WikiSQL, a recently released dataset with the largest question-SQL pairs. Our approach significantly improves the state-of-the-art execution accuracy from 69.0% to 74.4%.


title: Acquiring 3D indoor environments with variability and repetition

abstract: Large-scale acquisition of exterior urban environments is by now a well-established technology, supporting many applications in search, navigation, and commerce. The same is, however, not the case for indoor environments, where access is often restricted and the spaces are cluttered. Further, such environments typically contain a high density of repeated objects (e.g., tables, chairs, monitors, etc.) in regular or non-regular arrangements with significant pose variations and articulations. In this paper, we exploit the special structure of indoor environments to accelerate their 3D acquisition and recognition with a low-end handheld scanner. Our approach runs in two phases: (i) a learning phase wherein we acquire 3D models of frequently occurring objects and capture their variability modes from only a few scans, and (ii) a recognition phase wherein from a single scan of a new area, we identify previously seen objects but in different poses and locations at an average recognition time of 200ms/model. We evaluate the robustness and limits of the proposed recognition system using a range of synthetic and real world scans under challenging settings.


title: Active Gate Control for Current Balancing of Parallel-Connected IGBT Modules in Solid-State Modulators

abstract: In modern pulsed power systems often fast solid state switches like MOSFETs and IGBT modules are used to generate short high power pulses. In order to increase the pulsed power, solid state switches have to be connected in series or in parallel. Depending on the interconnection of the switches, parameter variations in the switches and in the system can lead to an unbalanced voltage or current. Therefore, the switches are generally derated, which results in an increased number of required devices and volume. With an active gate control, derating and preselection of the switching devices can be avoided. In this paper an active gate control of paralleled IGBT modules, which has been developed for converters with inductive load, is explained in detail and adapted to a solid state modulator. There, the paper focuses on the low inductance IGBT current measurement, the control unit implementation with a FPGA and DSP, as well as the balancing of the pulse currents.


title: 2D Gabor filters-based steganalysis of content-adaptive JPEG steganography

abstract: To improve the detection accuracy for content-adaptive JPEG steganography, which often constrains the embedding changes to complex texture regions, a new steganalysis feature called the GRF (Gabor Rich Feature) based on two-dimensional (2D) Gabor filters is proposed. First, the diverse 2D Gabor filters are generated and used to filter the decompressed JPEG image. Second, five types of statistical features are extracted from the filtered images and these features are merged according to their respective symmetry. Third, all the features are combined and feature selection is performed to reduce dimensionality. Last, an ensemble classifier is used to assemble the steganalysis feature as well as the final steganalyzer. The experimental results show that the proposed steganalysis feature can achieve a performance that is competitive with the state-of-the-art steganalysis features when used for the detection of the latest content-adaptive JPEG steganography algorithms.


title: Ensemble Classifiers for Network Intrusion Detection System

abstract: Two of the major challenges in designing anomaly intrusion detection are to maximize detection accuracy and to minimize false alarm rate. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each adopts different learning paradigms. The techniques deployed in this ensemble model are; Linear Genetic Programming (LGP), Adaptive Neural Fuzzy Inference System (ANFIS) and Random Forest (RF). The strengths from the individual models were evaluated and ensemble rule was formulated. Prior to classification, a 2-tier feature selection process was performed to expedite the detection process. Empirical results show an improvement in detection accuracy for all classes of network traffic; Normal, Probe, DoS, U2R and R2L. Random Forest, which is an ensemble learning technique that generates many classification trees and aggregates the individual result was also able to address imbalance dataset problem that many of machine learning techniques fail to sufficiently address it.


title: Practical online failure prediction for Blue Gene/P: Period-based vs event-driven

abstract: To facilitate proactive fault management in large-scale systems such as IBM Blue Gene/P, online failure prediction is of paramount importance. While many techniques have been presented for online failure prediction, questions arise regarding two commonly used approaches: period-based and event-driven. Which one has better accuracy? What is the best observation window (i.e., the time interval used to collect evidence before making a prediction)? How does the lead time (i.e., the time interval from the prediction to the failure occurrence) impact prediction arruracy? To answer these questions, we analyze and compare period-based and event-driven prediction approaches via a Bayesian prediction model. We evaluate these prediction approaches, under a variety of testing parameters, by means of RAS logs collected from a production supercomputer at Argonne National Laboratory. Experimental results show that the period-based Bayesian model and the event-driven Bayesian model can achieve up to 65.0% and 83.8% prediction accuracy, respectively. Furthermore, our sensitivity study indicates that the event-driven approach seems more suitable for proactive fault management in large-scale systems like Blue Gene/P.


Analysis of a Hybrid System for Decentralized Power Generation


title: Anticipatory robot control for efficient human-robot collaboration

abstract: Efficient collaboration requires collaborators to monitor the behaviors of their partners, make inferences about their task intent, and plan their own actions accordingly. To work seamlessly and efficiently with their human counterparts, robots must similarly rely on predictions of their users' intent in planning their actions. In this paper, we present an anticipatory control method that enables robots to proactively perform task actions based on anticipated actions of their human partners. We implemented this method into a robot system that monitored its user's gaze, predicted his or her task intent based on observed gaze patterns, and performed anticipatory task actions accord- ing to its predictions. Results from a human-robot interaction experiment showed that anticipatory control enabled the robot to respond to user requests and complete the task faster-2.5 seconds on average and up to 3.4 seconds-compared to a robot using a reactive control method that did not anticipate user intent. Our findings highlight the promise of performing anticipatory actions for achieving efficient human-robot teamwork.


title: Kinematic analysis and dynamic control of a 3-PUU parallel manipulator for cardiopulmonary resuscitation

abstract: The concept of a medical parallel manipulator applicable to chest compression in the process of cardiopulmonary resuscitation (CPR) is proposed in this paper. According to the requirement of CPR action, a three-prismatic-universal-universal (3-PUU) translational parallel manipulator (TPM) is designed for such applications. And a thorough analysis involving the issues of kinematics, dynamics, and control have been performed for the 3-PUU TPM. Not only the inverse and forward kinematics problems are solved in closed-form, but also the Jacobian matrix is derived analytically, along with the manipulator workspace generated in view of the physical constraints imposed by mechanical joints. Based on the principle of virtual work with a simplifying hypothesis adopted, the dynamic modeling is performed, and dynamic control utilizing computed torque method is implemented, at last the simulation results illustrate the well performance of the control algorithm. The research work provide a sound base for the development of a medical manipulator to assist in CPR operation


title: Fast Data Anonymization with Low Information Loss

abstract: Recent research studied the problem of publishing microdata without revealing sensitive information, leading to the privacy preserving paradigms of k-anonymity and l-diversity. k-anonymity protects against the identification of an individual's record. l-diversity, in addition, safeguards against the association of an individual with specific sensitive information. However, existing approaches suffer from at least one of the following drawbacks: (i) The information loss metrics are counter-intuitive and fail to capture data inaccuracies inflicted for the sake of privacy. (ii) l-diversity is solved by techniques developed for the simpler k-anonymity problem, which introduces unnecessary inaccuracies. (iii) The anonymization process is inefficient in terms of computation and I/O cost. ::: ::: In this paper we propose a framework for efficient privacy preservation that addresses these deficiencies. First, we focus on one-dimensional (i.e., single attribute) quasi-identifiers, and study the properties of optimal solutions for k-anonymity and l-diversity, based on meaningful information loss metrics. Guided by these properties, we develop efficient heuristics to solve the one-dimensional problems in linear time. Finally, we generalize our solutions to multi-dimensional quasi-identifiers using space-mapping techniques. Extensive experimental evaluation shows that our techniques clearly outperform the state-of-the-art, in terms of execution time and information loss.


title: Design and electro-thermo-mechanical analysis of high temperature molybdenum microheaters for exhaust gas sensing applications

abstract: Microheaters play a vital role in gas sensor applications. Exhaust gas sensors need high temperature microheaters to heat sensing films uniformly at low powers. In this paper, we present design and electro-thermo-mechanical analysis of molybdenum microheaters suitable for high temperature exhaust gas sensors. Double-spiral (DS), double-meander (DM), cross-meander (CS), modified-S (MS) and modified double spiral (MDS) shape structures were considered for simulation. The geometry of the resistive structure was optimized to improve temperature uniformity over a heating area of 500 × 500 µm2. Simulations show that the microheater consumed 83.65 mW power to reach a maximum temperature of 800 °C with a temperature gradient of 8.2°C. Structural deformation of the microheater membrane was studied to determine its stability and reliability under high thermal stresses. The maximum membrane deformation was found to be 15.25 µm at 800°C. Platinum, tungsten and molybdenum microheaters were compared in terms of their power consumption, temperature gradient and membrane deformations.


title: A FPGA-Based Approach to the Digital Control of a Class-D Amplifier for Sound Applications

abstract: The continuing trend in audio amplifier technology is towards more compact systems, lower distortion levels, and greater power efficiency. Thus, the relatively new consensus in the audio industry is the class-D amplifier, which generally uses compact H-bridge power inverters and a highly power-efficient, high frequency PWM switching scheme which yields little distortion after filtering. This paper presents an on-going research effort with the class-D amplifier that focuses on keeping the audio signal chain in the digital domain until just before the output filter stage that drives the speaker, which prevents transmission noise from distorting the audio signal and also provides the opportunity for a digital closed-loop control system for generating switching commands. Different solutions for the control system are theoretically and experimentally compared. The hardware structure is composed of a host computer, a board featuring a DSP chip coupled with a FPGA, a silicon H-bridge power inverter, a signal conditioning stage, and an ultra fast ADC


title: A systematic literature review of data governance and cloud data governance

abstract: Data management solutions on their own are becoming very expensive and not able to cope with the reality of everlasting data complexity. Businesses have grown more sophisticated in their use of data, which drives new demands that require different ways to handle this data. Forward-thinking organizations believe that the only way to solve the data problem will be the implementation of an effective data governance. Attempts in governing data failed before, as they were driven by IT, and affected by rigid processes and fragmented activities carried out on system by system basis. Up to very recently governance is mostly informal with very ambiguous and generic regulations, in siloes around specific enterprise repositories, lacking structure and the wider support of the organization. Despite its highly recognized importance, the area of data governance is still under developed and under researched. Since data governance is still under researched, there is need to advance research in data governance in order to deepen practice. Currently, what exist are mostly descriptive literature reviews in the area of data governance. In this paper, a systematic literature review (SLR), which offers a structured, methodical, and rigorous approach to the understanding of the state-of-the-art of research in data governance. The objective of the study is to provide a credible intellectual guide for upcoming researchers in data governance to help them identify areas in data governance research where they can make the most impact. The systematic literature review covered published contributions from both academia and industry. The presented SLR searches and examines most relevant published work since year 2000 to-date for data governance for non-cloud, and for cloud computing since 2007. Only 52 studies met the inclusion criteria, which are critically reviewed.


title: Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition

abstract: In this paper, we propose a multimodal multi-stream deep learning framework to tackle the egocentric activity recognition problem, using both the video and sensor data. First, we experiment and extend a multi-stream Convolutional Neural Network to learn the spatial and temporal features from egocentric videos. Second, we propose a multistream Long Short-Term Memory architecture to learn the features from multiple sensor streams (accelerometer, gyroscope, etc.). Third, we propose to use a two-level fusion technique and experiment different pooling techniques to compute the prediction results. Experimental results using a multimodal egocentric dataset show that our proposed method can achieve very encouraging performance, despite the constraint that the scale of the existing egocentric datasets is still quite limited.


title: Ultra compact filters for ultra-wideband (UWB) applications using multilayer ring resonators

abstract: Novel compact ultra-wideband (UWB) bandpass filters are proposed. It is based on multilayer ring resonators (MRR). The ring is applied to any loop shape. The filter has a simple structure for fabrication and the design technique can be adapted in order to realize both narrowband and ultra-wideband filters. With the MRR, filters of bandwidths in excess of 10 GHz while enjoying a wide stop-band can be designed. The fractional bandwidth of more than 143% and a group delay of less than 0.2ns are achieved with this class of filters. Also, in this class of filters, it is easy to place the attenuation poles in order to fit a requirement. Due to its small circuit size (around 2 mm in each dimension), this filter can be used in hybrid integration with small UWB, WLAN and radar devices. Since the filter is located within two ground planes, no radiation affects its characteristics. Two types of the proposed UWB filters were fabricated and the measurement verified the theory and simulation.


title: A Monolithically Integrated Large-Scale Optical Phased Array in Silicon-on-Insulator CMOS

abstract: A large-scale monolithic silicon nanophotonic phased array on a chip creates and dynamically steers a high-resolution optical beam in free space, enabling emerging applications in sensing, imaging, and communication. The scalable architecture leverages sub-array structure, mitigating the impact of process variation on the phased array performance. In addition, sharing control electronics among multiple optical modulators in the scalable architecture reduces the number of digital-to-analog converters (DACs) required for an $N^{2}$ array from $\mathcal {O}(N^{2})$ to $\mathcal {O}(N)$ , allowing a small silicon footprint. An optical phased array for 1550-nm wavelength with 1024 uniformly spaced optical grating antennas, 1192 optical variable phase shifters, and 168 optical variable attenuators is integrated into a 5.7 mm $\times$ 6.4 mm chip in a commercial 180-nm silicon-on-insulator RF CMOS technology. The control signals for the optical variable phase shifters and attenuators are provided by 136 DACs with 14-bit nonuniform resolution using 2.5-V input-output transistors. The implemented phased array can create 0.03° narrow optical beams that can be steered unambiguously within ±22.5°.


title: "Hey Alexa, What's Up?": A Mixed-Methods Studies of In-Home Conversational Agent Usage

abstract: In-home, place-based, conversational agents have exploded in popularity over the past three years. In particular, Amazon's conversational agent, Alexa, now dominates the market and is in millions of homes. This paper presents two complementary studies investigating the experience of households living with a conversational agent over an extended period of time. First, we gathered the history logs of 75 Alexa participants and quantitatively analyzed over 278,000 commands. Second, we performed seven in-home, contextual interviews of Alexa owners focusing on how their household interacts with Alexa. Our findings give the first glimpse of how households integrate Alexa into their lives. We found interesting behaviors around purchasing and acclimating to Alexa, in the number and physical placement of devices, and in daily use patterns. Participants also uniformly described interactions between children and Alexa. We conclude with suggestions for future improvement for intelligent conversational agents.


title: The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting

abstract: With the rapid development of robot and other intelligent and autonomous agents, how a human could be influenced by a robot's expressed mood when making decisions becomes a crucial question in human-robot interaction. In this pilot study, we investigate (1) in what way a robot can express a certain mood to influence a human's decision making behavioral model; (2) how and to what extent the human will be influenced in a game theoretic setting. More specifically, we create an NLP model to generate sentences that adhere to a specific affective expression profile. We use these sentences for a humanoid robot as it plays a Stackelberg security game against a human. We investigate the behavioral model of the human player.


title: Unsupervised Video-to-Video Translation

abstract: Unsupervised image-to-image translation is a recently proposed task of translating an image to a different style or domain given only unpaired image examples at training time. In this paper, we formulate a new task of unsupervised video-to-video translation, which poses its own unique challenges. Translating video implies learning not only the appearance of objects and scenes but also realistic motion and transitions between consecutive frames.We investigate the performance of per-frame video-to-video translation using existing image-to-image translation networks, and propose a spatio-temporal 3D translator as an alternative solution to this problem. We evaluate our 3D method on multiple synthetic datasets, such as moving colorized digits, as well as the realistic segmentation-to-video GTA dataset and a new CT-to-MRI volumetric images translation dataset. Our results show that frame-wise translation produces realistic results on a single frame level but underperforms significantly on the scale of the whole video compared to our three-dimensional translation approach, which is better able to learn the complex structure of video and motion and continuity of object appearance.


title: Low cost flip chip (LCFC): An innovative approach for breakthrough reduction in flip chip package cost

abstract: A new low cost flip chip (LCFC) packaging solution is developed that dramatically reduces flip chip package cost. The solution entails innovations and improvements in the bump, interconnect structure, substrate design and underfilling process. Cu column bumps with solder caps are used to form a “Bump on Lead (BOL)” interconnection with narrow substrate pads with no solder mask confinement (“open solder mask”, or “open SR”) and no solder-on-pad finish (“no SOP”). This results in a substrate design with relaxed design rules despite high I/O escape densities, enabling a majority of IC designs in the sub-1000 pin range to fit in 2-lyr laminate substrates. The novel BOL/open SR/no-SOP interconnect structure is combined with a mold underfilling (MUF) process using high density 1-up matrix substrates to achieve high substrate utilization and low process cost. Furthermore, it is shown through empirical data and FEA modeling that the unique mechanical structure of the BOL interconnection remarkably reduces the stress on Si subsurface layers resulting in elimination of the low K damage phenomenon commonly observed in sub-45 nm Si nodes. The LCFC technology is Pb-free, scalable to very fine pitch in the sub-100 um range and highly electromigration-resistant, providing a natural migration path to 3D/TSV micro bump / micro bond and green solutions for the future. In the present paper, we present in detail, the structure, assembly process and reliability of this packaging solution. We also present data on the application of the solution to advanced sub-45 nm Si nodes with ELK (Extra Low K) dielectrics.


title: Trajectory generation and control for precise aggressive maneuvers with quadrotors

abstract: We study the problem of designing dynamically feasible trajectories and controllers that drive a quadrotor to a desired state in state space. We focus on the development of a family of trajectories defined as a sequence of segments, each with a controller parameterized by a goal state or region in state space. Each controller is developed from the dynamic model of the robot and then iteratively refined through successive experimental trials in an automated fashion to account for errors in the dynamic model and noise in the actuators and sensors. We show that this approach permits the development of trajectories and controllers enabling such aggressive maneuvers as flying through narrow, vertical gaps and perching on inverted surfaces with high precision and repeatability.


An autonomous selfreplicating robotic system


title: Modelling the Scene Dependent Imaging in Cameras with a Deep Neural Network

abstract: We present a novel deep learning framework that models the scene dependent image processing inside cameras. Often called as the radiometric calibration, the process of recovering RAW images from processed images (JPEG format in the sRGB color space) is essential for many computer vision tasks that rely on physically accurate radiance values. All previous works rely on the deterministic imaging model where the color transformation stays the same regardless of the scene and thus they can only be applied for images taken under the manual mode. In this paper, we propose a data-driven approach to learn the scene dependent and locally varying image processing inside cameras under the automode. Our method incorporates both the global and the local scene context into pixel-wise features via multi-scale pyramid of learnable histogram layers. The results show that we can model the imaging pipeline of different cameras that operate under the automode accurately in both directions (from RAW to sRGB, from sRGB to RAW) and we show how we can apply our method to improve the performance of image deblurring.


Systematically Adapting Machine Translation for Grammatical Error Correction