title: Control of contact via tactile sensing

text: In this paper, we present our approach to using tactile sensing in the feedback control of robot contact tasks. A general framework, called tactile servo, is first introduced. We then address the critical issue of how to model the state of contact in terms that are both sufficient for defining general contacts and conducive to bridging the gap between a robot task description and the information observable by a tactile sensor. We subsequently examine techniques for deriving tactile sensor models that are required for computing the state of contact from the sensor output. Two basic methods for mapping a tactile sensor image to the contact state variables are introduced—one based on explicit inverse modeling and the other on numerical tactile Jacobian. In both cases, moments of the tactile sensor image are used as the features that capture the necessary information about contact. The theoretical development is supported by extensive experiments, which include edge tracking, object shape construction, and object manipulation.


title: External Computations and Interoperability in the New DLV Grounder

text: In this paper we focus on some of the most recent advancements in I-DLV, the new intelligent grounder of DLV; the system has been endowed with means aimed at easing the interoperability and integration with external systems and accommodating external source of computation and value invention within ASP programs. In particular, we describe here the support for external computations via explicit calls to Python scripts, and tools for the interoperability with both relational and graph databases.


title: Study of Planar Inverted-F Antenna ( PIFA ) for Mobile Devices

text: Now-a-days more and more radios are being integrated into a single wireless platform to allow maximum connectivity. In this paper a theoretical study of Planar Inverted F Antenna is presented. PIFA as a low profile antenna is widely used in several application areas due to several advantages. Research and experiments done on PIFA structures are discussed and categorized. Some methods that can be used to improve efficiency, bandwidth & reduce volume of an antenna are also discussed which includes effect of dielectric substrate, dimensions of the antenna etc.


title: S-Sensors: Integrating physical world inputs with social networks using wireless sensor networks

text: Real-world monitoring and surveillance applications are rapidly increasing with the growth in the demand for awareness of the environment and surrounding. Sensor-to-web integration models are envisaged to improve the utilisation of sensory infrastructures and data via advanced coordination and collaboration of sensor nodes and web applications. This paper proposes to employ micro-blogging to publish and share sensory data and resources. S-Sensors provides a framework to globally share locally measured sensory readings. Short messages depicting the status of the environment are used to convey sensory data of the physical world. Moreover, data accessibility and resource manageability are facilitated by using social networks paradigms to form communities of sensor networks.


title: Development of ambient environmental monitoring system through wireless sensor network (WSN) using NodeMCU and “WSN monitoring”

text: In this paper we have developed a system for web based environment monitoring using the WSN Technology. WSN sensor nodes transmit data to the cloud-based database via Web API request. Measured data can be monitored by the user anywhere from internet by using the Web Application which one is also compatible for mobile phones. If the data measured by sensor node exceeds the configured value range in Web Application, Web Application sends a warning e-mail to users for improving environmental conditions.


title: Multicenter randomized clinical trial evaluating the effectiveness of the Lokomat in subacute stroke.

text:
OBJECTIVE
To compare the efficacy of robotic-assisted gait training with the Lokomat to conventional gait training in individuals with subacute stroke.

METHODS
A total of 63 participants<6 months poststroke with an initial walking speed between 0.1 to 0.6 m/s completed the multicenter, randomized clinical trial. All participants received twenty-four 1-hour sessions of either Lokomat or conventional gait training. Outcome measures were evaluated prior to training, after 12 and 24 sessions, and at a 3-month follow-up exam. Self-selected overground walking speed and distance walked in 6 minutes were the primary outcome measures, whereas secondary outcome measures included balance, mobility and function, cadence and symmetry, level of disability, and quality of life measures.

RESULTS
Participants who received conventional gait training experienced significantly greater gains in walking speed (P=.002) and distance (P=.03) than those trained on the Lokomat. These differences were maintained at the 3-month follow-up evaluation. Secondary measures were not different between the 2 groups, although a 2-fold greater improvement in cadence was observed in the conventional versus Lokomat group.

CONCLUSIONS
For subacute stroke participants with moderate to severe gait impairments, the diversity of conventional gait training interventions appears to be more effective than robotic-assisted gait training for facilitating returns in walking ability.


title: Advances in Neuropsychoanalysis , Attachment Theory , and Trauma Research : Implications for Self Psychology

text: In 1971, Heinz Kohut, trained in neurology and then psychoanalysis, published The Analysis of the Self, a detailed exposition of the central role of the self in human existence. This classic volume of both twentieth century psychoanalysis and psychology was more than a collection of various clinical observations—rather it represented an overarching integrated theory of the development, structuralization, psychopathogenesis, and psychotherapy of disorders of the self. Although some of these ideas were elaborations of previous psychoanalytic principles, a large number of his concepts, including an emphasis on self rather than ego, signified an innovative departure from mainstream psychoanalysis and yet a truly creative addition to Freud’s theory.


title: Linearization Through Dithering: A 50 MHz Bandwidth, 10-b ENOB, 8.2 mW VCO-Based ADC

text: Non-linear voltage-to-frequency characteristic of a voltage-controlled oscillator (VCO) severely curtails the dynamic range of analog-to-digital converters (ADCs) built with VCOs. Typical approaches to enhance the dynamic range include embedding the VCO-based ADC in a ΔΣ loop or to post-process the digital data for calibration, both of which impose significant power constraints. In contrast, in this work the VCO-based ADC is linearized through a filtered dithering technique, wherein the VCO-based ADC is used as a fine stage that processes the residue from a coarse stage in a 0-1 MASH structure. The proposed filtered dithering technique conditions the signal to the VCO input to appear as white noise thereby eliminating spurious signal content arising out of the VCO nonlinearity. The work resorts to multiple other signal processing techniques to build a high-resolution, wideband prototype, in 65 nm complementary metal-oxide semiconductor (CMOS), that achieves 10 effective number of bits (ENOB) in digitizing signals with 50 MHz bandwidth consuming 8.2 mW at a figure of merit (FoM) of 90 fJ/conv.step.


title: Image compression with auto-encoder algorithm using deep neural network (DNN)

text: An image compression is necessary for the image processing applications such as data storing, image classification, image recognition etc. Then, several research articles have been proposed to reserve for these topics. However, the image compression with auto encoder has been found for a small number of the improvements. Therefore, this paper presents a detailed study to demonstrate the image compression algorithm using the deep neural network (DNN). The proposed algorithm consists of 1) compressing image with auto encoder, and 2) decoding image. The proposed compressing image with auto encoder algorithm uses non-recurrent three-layer neural networks (NRTNNs) which use an extended Kalman filter (EKF) to update the weights of the networks. To evaluate the proposed algorithm performances, the Matlab program is used for implementations of the overall testing algorithm. From our simulation results, it shows that the proposed image compression algorithm is able to reduce the image dimensionality and is able to recall the compressed image with low loss.


title: Distribution expansion planning considering reliability and security of energy using modified PSO ( Particle Swarm Optimization ) algorithm

text: Distribution feeders and substations need to provide additional capacity to serve the growing electrical demand of customers without compromising the reliability of the electrical networks. Also, more control devices, such as DG (Distributed Generation) units are being integrated into distribution feeders. Distribution networks were not planned to host these intermittent generation units before construction of the systems. Therefore, additional distribution facilities are needed to be planned and prepared for the future growth of the electrical demand as well as the increase of network hosting capacity by DG units. This paper presents a multiobjective optimization algorithm for the MDEP (Multi-Stage Distribution Expansion Planning) in the presence of DGs using nonlinear formulations. The objective functions of the MDEP consist of minimization of costs, END (Energy-Not-Distributed), active power losses and voltage stability index based on SCC (Short Circuit Capacity). A MPSO (modified Particle Swarm Optimization) algorithm is developed and used for this multiobjective MDEP optimization. In the proposed MPSO algorithm, a new mutation method is implemented to improve the global searching ability and restrain the premature convergence to local minima. The effectiveness of the proposed method is tested on a typical 33-bus test system and results are presented.


title: Word similarity computation based on HowNet

text: Word similarity computation has important applications in many areas, such as natural language processing, intelligence information retrieval, document clustering, automatic answering, word sense disambiguation, machine translation etc. This article makes an intensive study of word similarity computation based on HowNet and the word similarity is computed in three steps: (1) computes sememes similarity, (2) computes concepts similarity using the weight sum method of sememes similarity, (3) takes the maximum similarity of concepts as words similarity. This article mainly introduces numbering the sememes and presents a more accurate method of decomposing the concept description type. The experiment shows that the algorithm of word similarity presented in this article is simple and feasible, and the computed results are relatively consistent with awareness.


title: A Resource-Based Model For E-Commerce In Developing Countries

text: Previous efforts in electronic commerce (e-commerce) research in developing countries shows that there is an acute lack of theoretical frameworks and empirical evidence to understand how developing country firms realize electronic commerce benefits amidst their national constraints. This paper sets out to develop a theoretically abstracted but contextually grounded model of how developing country firms can orient their resources and realize these benefits amidst their national constraints. A review of e-commerce and strategy management literature to develop a resource – based model for e-commerce benefits was undertaken. The process-based model provides an understanding of how to identify, integrate, and reconfigure resources to achieve electronic commerce benefits; provides propositions that serves as theoretical platforms for future empirically grounded research on electronic commerce in developing country contexts and brings organizations closer to identifying and categorizing the strategic value of resources and the role managerial capabilities and intangible resources play in sustaining e-commerce benefits. Finally, our findings provides organizations the strategic options to address resources which have lost their value or have become less valuable to their strategic orientation in e-commerce adoption thereby serving as a starting point of examining e-commerce in developing countries through the theoretical lens of information systems and strategic management.


title: Personalized Security Messaging : Nudges for Compliance with Browser Warnings

text: Decades of psychology and decision-making research show that everyone makes decisions differently; yet security messaging is still one-size-fits-all. This suggests that we can improve outcomes by delivering information relevant to how each individual makes decisions. We tested this hypothesis by designing messaging customized for stable personality traits— specifically, the five dimensions of the General Decision-Making Style (GDMS) instrument. We applied this messaging to browser warnings, security messaging encountered by millions of web users on a regular basis. To test the efficacy of our nudges, we conducted experiments with 1,276 participants, who encountered a warning about broken HTTPS due to an invalid certificate under realistic circumstances. While the effects of some nudges correlated with certain traits in a statistically significant manner, we could not reject the null hypothesis—that the intervention did not affect the subjects’ behavior—for most of our nudges, especially after accounting for participants who did not pay close attention to the message. In this paper, we present the detailed results of our experiments, discuss potential reasons for why the outcome contradicts the decision-making research, and identify lessons for researchers based on our experience.


title: Coding of pleasant touch by unmyelinated afferents in humans

text: Pleasant touch sensations may begin with neural coding in the periphery by specific afferents. We found that during soft brush stroking, low-threshold unmyelinated mechanoreceptors (C-tactile), but not myelinated afferents, responded most vigorously at intermediate brushing velocities (1−10 cm s−1), which were perceived by subjects as being the most pleasant. Our results indicate that C-tactile afferents constitute a privileged peripheral pathway for pleasant tactile stimulation that is likely to signal affiliative social body contact.


title: A Systematic Mapping Study on Software Reuse

text: Context: Software reuse is considered as the key to a successful software development because of its potential to reduce the time to market, increase quality and reduce costs. This increase in demand made the software organizations to envision the use of software reusable assets which can also help in solving recurring problems. Till now, software reuse is confined to reuse of source code in the name of code scavenging. Now a day, software organizations are extending the concepts of software reuse to other life cycle objects as they realized that reuse of source code alone does not save money. The academia has put forward some assets as reusable and presented methods or approaches for reusing them. Also, for a successful software reuse the organizations should assess the value of reuse and keep track on their reuse programs. The other area which is vital for software reuse is the maintenance. Maintenance of reusable software has direct impact on the cost of the software. In this regard, academia has presented a number of techniques, methods, metrics and models for assessing the value of reuse and for maintaining the reusable software. Objectives: In our thesis, we investigate on the reusable assets and the methods/ approaches that are put forward by the academia for reusing those assets. Also a systematic mapping study is performed to investigate what techniques, methods, models and metrics for assessing the value of reuse and for maintaining the reused software are proposed and we also investigate their validation status as well. Methods: The databases like IEEE Xplore, ACM digital library, Inspec, Springer and Google scholar were used to search for the relevant studies for our systematic mapping study. We followed basic inclusion criteria along with detailed inclusion/exclusion criteria for selecting the appropriate article. Results: Through our systematic mapping study, we could summarize the list of 14 reusable assets along with the approaches/methods for reusing them. Taxonomy for assessing the value of reuse and taxonomy for maintaining the reusable software are presented. We also presented the methods/metrics/models/techniques for measuring reuse to assess its value and for maintaining the reusable software along with their validation status and areas in focus. Conclusion: We conclude that, there is a need for defining a standard set of reusable assets that are commonly accepted by the researchers in the field of software reuse. Most metrics/models/methods/approaches presented for assessing the value of reuse and for maintaining the reuse software are academically validated. Efforts have to be put on industrially validating them using the real data.


title: Design of Lightweight Authentication and Key Agreement Protocol for Vehicular Ad Hoc Networks

text: Due to the widespread popularity in both academia and industry, vehicular ad hoc networks (VANETs) have been used in a wide range of applications starting from intelligent transportation to e-health and itinerary planning. This paper proposes a new decentralized lightweight authentication and key agreement scheme for VANETs. In the proposed scheme, there are three types of mutual authentications: 1) between vehicles; 2) between vehicles and their respective cluster heads; and 3) between cluster heads and their respective roadside units. Apart from these authentications, the proposed scheme also maintains secret keys between roadside units for their secure communications. The rigorous formal and informal security analysis shows that the proposed scheme is capable to defend various malicious attacks. Moreover, the ns-2 simulation demonstrates the practicability of the proposed scheme in VANET environment.


title: A Theoretical Framework for Structured Prediction using Factor Graph Complexity ∗

text: We present a general theoretical analysis of structured prediction. By introducing a new complexity measure that explicitly factors in the structure of the output space and the loss function, we are able to derive new data-dependent learning guarantees for a broad family of losses and for hypothesis sets with an arbitrary factor graph decomposition. To the best of our knowledge, these are both the most favorable and the most general guarantees for structured prediction (and multiclass classification) currently known. We also extend this theory by leveraging the principle of Voted Risk Minimization (VRM) and showing that learning is possible with complex factor graphs. We both present new learning bounds in this advanced setting as well as derive two new families of algorithms, Voted Conditional Random Field and Voted Structured Boosting, which can make use of very complex features and factor graphs without overfitting. Finally, we validate our theory through experiments on several datasets.


title: Theoretical perspectives on the relation between catastrophizing and pain.

text: The tendency to "catastrophize" during painful stimulation contributes to more intense pain experience and increased emotional distress. Catastrophizing has been broadly conceived as an exaggerated negative "mental set" brought to bear during painful experiences. Although findings have been consistent in showing a relation between catastrophizing and pain, research in this area has proceeded in the relative absence of a guiding theoretical framework. This article reviews the literature on the relation between catastrophizing and pain and examines the relative strengths and limitations of different theoretical models that could be advanced to account for the pattern of available findings. The article evaluates the explanatory power of a schema activation model, an appraisal model, an attention model, and a communal coping model of pain perception. It is suggested that catastrophizing might best be viewed from the perspective of hierarchical levels of analysis, where social factors and social goals may play a role in the development and maintenance of catastrophizing, whereas appraisal-related processes may point to the mechanisms that link catastrophizing to pain experience. Directions for future research are suggested.


title: A realist review of mobile phone-based health interventions for non-communicable disease management in sub-Saharan Africa

text:
BACKGROUND
The prevalence of non-communicable diseases (NCDs) is increasing in sub-Saharan Africa. At the same time, the use of mobile phones is rising, expanding the opportunities for the implementation of mobile phone-based health (mHealth) interventions. This review aims to understand how, why, for whom, and in what circumstances mHealth interventions against NCDs improve treatment and care in sub-Saharan Africa.

METHODS
Four main databases (PubMed, Cochrane Library, Web of Science, and Google Scholar) and references of included articles were searched for studies reporting effects of mHealth interventions on patients with NCDs in sub-Saharan Africa. All studies published up until May 2015 were included in the review. Following a realist review approach, middle-range theories were identified and integrated into a Framework for Understanding the Contribution of mHealth Interventions to Improved Access to Care for patients with NCDs in sub-Saharan Africa. The main indicators of the framework consist of predisposing characteristics, needs, enabling resources, perceived usefulness, and perceived ease of use. Studies were analyzed in depth to populate the framework.

RESULTS
The search identified 6137 titles for screening, of which 20 were retained for the realist synthesis. The contribution of mHealth interventions to improved treatment and care is that they facilitate (remote) access to previously unavailable (specialized) services. Three contextual factors (predisposing characteristics, needs, and enabling resources) influence if patients and providers believe that mHealth interventions are useful and easy to use. Only if they believe mHealth to be useful and easy to use, will mHealth ultimately contribute to improved access to care. The analysis of included studies showed that the most important predisposing characteristics are a positive attitude and a common language of communication. The most relevant needs are a high burden of disease and a lack of capacity of first-contact providers. Essential enabling resources are the availability of a stable communications network, accessible maintenance services, and regulatory policies.

CONCLUSIONS
Policy makers and program managers should consider predisposing characteristics and needs of patients and providers as well as the necessary enabling resources prior to the introduction of an mHealth intervention. Researchers would benefit from placing greater attention on the context in which mHealth interventions are being implemented instead of focusing (too strongly) on the technical aspects of these interventions.


title: Visual attention measures for multi-screen TV

text: We introduce a set of nine measures to characterize viewers' visual attention patterns for multi-screen TV. We apply our measures during an experiment involving nine screen layouts with two, three, and four TV screens, for which we report new findings on visual attention. For example, we found that viewers need an average discovery time up to 4.5 seconds to visually fixate four screens, and their perceptions of how long they watched each screen are substantially accurate, i.e., we report Pearson correlations up to .892 with measured eye tracking data. We hope our set of new measures (and the companion toolkit to compute them automatically) will benefit the community as a first step toward understanding visual attention for emerging multi-screen TV applications.


title: A first look at traffic on smartphones

text: Using data from 43 users across two platforms, we present a detailed look at smartphone traffic. We find that browsing contributes over half of the traffic, while each of email, media, and maps contribute roughly 10%. We also find that the overhead of lower layer protocols is high because of small transfer sizes. For half of the transfers that use transport-level security, header bytes correspond to 40% of the total. We show that while packet loss is the main factor that limits the throughput of smartphone traffic, larger send buffers at Internet servers can improve the throughput of a quarter of the transfers. Finally, by studying the interaction between smartphone traffic and the radio power management policy, we find that the power consumption of the radio can be reduced by 35% with minimal impact on the performance of packet exchanges.


title: Bartendr: a practical approach to energy-aware cellular data scheduling

text:
Cellular radios consume more power and suffer reduced data rate when the signal is weak. According to our measurements, the communication energy per bit can be as much as 6x higher when the signal is weak than when it is strong. To realize energy savings, applications must preferentially communicate when the signal is strong, either by deferring non-urgent communication or by advancing anticipated communication to coincide with periods of strong signal. Allowing applications to perform such scheduling requires predicting signal strength, so that opportunities for energy-efficient communication can be anticipated. Furthermore, such prediction must be performed at little energy cost.
 In this paper, we make several contributions towards a practical system for energy-aware cellular data scheduling called Bartendr. First, we establish, via measurements, the relationship between signal strength and power consumption. Second, we show that location alone is not sufficient to predict signal strength and motivate the use of tracks to enable effective prediction. Finally, we develop energy-aware scheduling algorithms for different workloads - syncing and streaming - and evaluate these via simulation driven by traces obtained during actual drives, demonstrating energy savings of up to 60%. Our experiments have been performed on four cellular networks across two large metropolitan areas, one in India and the other in the U.S.


title: Accurate online power estimation and automatic battery behavior based power model generation for smartphones

text: This paper describes PowerBooter, an automated power model construction technique that uses built-in battery voltage sensors and knowledge of battery discharge behavior to monitor power consumption while explicitly controlling the power management and activity states of individual components. It requires no external measurement equipment. We also describe PowerTutor, a component power management and activity state introspection based tool that uses the model generated by PowerBooter for online power estimation. PowerBooter is intended to make it quick and easy for application developers and end users to generate power models for new smartphone variants, which each have different power consumption properties and therefore require different power models. PowerTutor is intended to ease the design and selection of power efficient software for embedded systems. Combined, PowerBooter and PowerTutor have the goal of opening power modeling and analysis for more smartphone variants and their users.


title: Applied spatial data analysis with R

text: Do you need the book of Applied Spatial Data Analysis with R pdf with ISBN of 9781461476177? You will be glad to know that right now Applied Spatial Data Analysis with R pdf is available on our book collections. This Applied Spatial Data Analysis with R comes PDF and EPUB document format. If you want to get Applied Spatial Data Analysis with R pdf eBook copy, you can download the book copy here. The Applied Spatial Data Analysis with R we think have quite excellent writing style that make it easy to comprehend.


title: Diversity in smartphone usage

text: Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.


title: Chip to wafer temporary bonding with self-alignment by patterned FDTS layer for size-free MEMS integration

text: In this paper, we present a low-cost and rapid self-alignment process for temporary bonding of MEMS chip onto carrier wafer for size-free MEMS-IC integration. For the first time, a hydrophobic self-assembled monolayer (SAM), FDTS (CF<inf>3</inf>(CF<inf>2</inf>)<inf>7</inf>(CH<inf>2</inf>)<inf>2</inf>SiCl<inf>3</inf>), was successfully patterned by lift-off process on an oxidized silicon carrier wafer. Small volume of H<inf>2</inf>O (∼µl/cm2) was then dropped and spread on the non-coated hydrophilic SiO<inf>2</inf> surface for temporary bonding of MEMS chip. Our results demonstrated that the hydrophobic FDTS pattern on carrier wafer enables rapid and precise self-alignment of MEMS chip onto SiO<inf>2</inf> binding-site by capillary force. After transfer the MEMS chips to target wafer, FDTS can be removed by O<inf>2</inf> plasma treatment or UV irradiation.


title: Exploiting users' social relations to forward data in opportunistic networks: The HiBOp solution

text: Opportunistic networks, in which nodes opportunistically exploit any pair-wise contact to identify next hops towards the destination, are one of the most interesting technologies to support the pervasive networking vision. Opportunistic networks allow content sharing between mobile users without requiring any pre-existing Internet infrastructure, and tolerate partitions, long disconnections, and topology instability in general. In this paperwe propose a context-aware framework for routing and forwarding in opportunistic networks. The framework is general, and able to host various flavors of context-aware routing. In this work we also present a particular protocol, HiBOp, which, by exploiting the framework, learns and represents through context information, the users’ behavior and their social relations, and uses this knowledge to drive the forwarding process. The comparison of HiBOp with reference to alternative solutions shows that a context-aware approach based on users’ social relations turns out to be a very efficient solution for forwarding in opportunistic networks.We showperformance improvements over the reference solutions both in terms of resource utilization and in terms of user perceived QoS. © 2008 Elsevier B.V. All rights reserved.


title: Are Cyberbullies Less Empathic? Adolescents' Cyberbullying Behavior and Empathic Responsiveness

text: Meta-analyses confirm a negative relationship between aggressive behavior and empathy, that is, the ability to understand and share the feelings of others. Based on theoretical considerations, it was, therefore, hypothesized that a lack of empathic responsiveness may be characteristic for cyberbullies in particular. In the present study, 2.070 students of Luxembourg secondary schools completed an online survey that included a cyberbullying questionnaire(4) and a novel empathy short scale. According to the main hypothesis, analyses of variances indicated that cyberbullies demonstrated less empathic responsiveness than non-cyberbullies. In addition, cyberbullies were also more afraid of becoming victims of cyberbullying. The findings confirm and substantially extend the research on the relationship between empathy and aggressive behavior. From an educational point of view, the present findings suggest that training of empathy skills might be an important tool to decrease cyberbullying.


title: Applying Graph theory to the Internet of Things

text: In the Internet of Things (IoT), we all are ``things''. Graph theory, a branch of discrete mathematics, has been proven to be useful and powerful in understanding complex networks in history. By means of graph theory, we define new concepts and terminology, and explore the definition of IoT, and then show that IoT is the union of a topological network, a data-functional network and a domi-functional network.


title: Dermatologists' accuracy in early diagnosis of melanoma of the nail matrix.

text:
OBJECTIVE
To measure and compare the accuracy of 4 different clinical methods in the diagnosis of melanoma in situ of the nail matrix among dermatologists with different levels of clinical experience.

DESIGN
Twelve cases of melanonychias (5 melanomas and 7 nonmelanomas) were presented following 4 successive steps: (1) clinical evaluation, (2) evaluation according to the ABCDEF rule, (3) dermoscopy of the nail plate, and (4) intraoperative dermoscopy. At each step, the dermatologists were asked to decide if the lesion was a melanoma.

SETTING
The test was administered at 2 dermatological meetings in 2008.

PARTICIPANTS
A total of 152 dermatologists, including 11 nail experts, 53 senior dermatologists, and 88 junior dermatologists.

MAIN OUTCOME MEASURES
The answers were evaluated as percentage of right answers for each diagnostic step according to the different grade of expertise. Differences among the percentage of right answers in the different steps were evaluated with the z test at a 5% level of significance. The agreement was investigated using Cohen kappa statistic.

RESULTS
The only method that statistically influenced the correct diagnosis for each category (experts, seniors, and juniors) was intraoperative dermoscopy (z test; P < .05). Cohen kappa statistic showed a moderate interobserver agreement.

CONCLUSIONS
Overall accuracy of dermatologists in the diagnosis of nail matrix melanoma in situ is low because the percentages of physicians who indicated the correct diagnosis during each of the first 3 clinical steps of the test ranged from 46% to 55%. The level of expertise did not statistically influence the correct diagnosis.


title: Rethink fronthaul for soft RAN

text: In this article we discuss the design of a new fronthaul interface for future 5G networks. The major shortcomings of current fronthaul solutions are first analyzed, and then a new fronthaul interface called next-generation fronthaul interface (NGFI) is proposed. The design principles for NGFI are presented, including decoupling the fronthaul bandwidth from the number of antennas, decoupling cell and user equipment processing, and focusing on high-performancegain collaborative technologies. NGFI aims to better support key 5G technologies, in particular cloud RAN, network functions virtualization, and large-scale antenna systems. NGFI claims the advantages of reduced bandwidth as well as improved transmission efficiency by exploiting the tidal wave effect on mobile network traffic. The transmission of NGFI is based on Ethernet to enjoy the benefits of flexibility and reliability. The major impact, challenges, and potential solutions of Ethernet-based fronthaul networks are also analyzed. Jitter, latency, and time and frequency synchronization are the major issues to overcome.


title: Language within our grasp

text: In monkeys, the rostral part of ventral premotor cortex (area F5) contains neurons that discharge, both when the monkey grasps or manipulates objects and when it observes the experimenter making similar actions. These neurons (mirror neurons) appear to represent a system that matches observed events to similar, internally generated actions, and in this way forms a link between the observer and the actor. Transcranial magnetic stimulation and positron emission tomography (PET) experiments suggest that a mirror system for gesture recognition also exists in humans and includes Broca's area. We propose here that such an observation/execution matching system provides a necessary bridge from'doing' to'communicating',as the link between actor and observer becomes a link between the sender and the receiver of each message.


title: DCU-UVT: Word-Level Language Classification with Code-Mixed Data

text: This paper describes the DCU-UVT team’s participation in the Language Identification in Code-Switched Data shared task in the Workshop on Computational Approaches to Code Switching. Wordlevel classification experiments were carried out using a simple dictionary-based method, linear kernel support vector machines (SVMs) with and without contextual clues, and a k-nearest neighbour approach. Based on these experiments, we select our SVM-based system with contextual clues as our final system and present results for the Nepali-English and Spanish-English datasets.


title: Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network

text: Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context. In this paper, we investigate matching a response with its multi-turn context using dependency information based entirely on attention. Our solution is inspired by the recently proposed Transformer in machine translation (Vaswani et al., 2017) and we extend the attention mechanism in two ways. First, we construct representations of text segments at different granularities solely with stacked self-attention. Second, we try to extract the truly matched segment pairs with attention across the context and response. We jointly introduce those two kinds of attention in one uniform neural network. Experiments on two large-scale multi-turn response selection tasks show that our proposed model significantly outperforms the state-of-the-art models.


title: Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Pattern Detection

text: Holistically detecting interstitial lung disease (ILD) patterns from CT images is challenging yet clinically important. Unfortunately, most existing solutions rely on manually provided regions of interest, limiting their clinical usefulness. In addition, no work has yet focused on predicting more than one ILD from the same CT slice, despite the frequency of such occurrences. To address these limitations, we propose two variations of multi-label deep convolutional neural networks (CNNs). The first uses a deep CNN to detect the presence of multiple ILDs using a regression-based loss function. Our second variant further improves performance, using spatially invariant Fisher Vector encoding of the CNN feature activations. We test our algorithms on a dataset of 533 patients using five-fold cross-validation, achieving high area-under-curve (AUC) scores of 0.982, 0.972, 0.893 and 0.993 for Ground Glass, Reticular, Honeycomb and Emphysema, respectively. As such, our work represents an important step forward in providing clinically effective ILD detection.


title: Syntax-based Alignment of Multiple Translations: Extracting Paraphrases and Generating New Sentences

text: We describe a syntax-based algorithm that automatically builds Finite State Automata (word lattices) from semantically equivalent translation sets. These FSAs are good representations of paraphrases. They can be used to extract lexical and syntactic paraphrase pairs and to generate new, unseen sentences that express the same meaning as the sentences in the input sets. Our FSAs can also predict the correctness of alternative semantic renderings, which may be used to evaluate the quality of translations.


title: Narrating a Knowledge Base

text: We aim to automatically generate natural language narratives about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new table position self-attention to capture the inter-dependencies among related slots. For evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we also propose a KB reconstruction based metric by extracting a KB from the generation output and comparing it with the input KB. We also create a new data set which includes 106,216 pairs of structured KBs and their corresponding natural language descriptions for two distinct entity types. Experiments show that our approach significantly outperforms state-of-the-art methods. The reconstructed KB achieves 68.8% 72.6% F-score.1


title: Robust high-dimensional precision matrix estimation

text: The dependency structure of multivariate data can be analyzed using the covariance matrix Σ. In many fields the precision matrix Σ −1 is even more informative. As the sample covariance estimator is singular in high-dimensions, it cannot be used to obtain a precision matrix estimator. A popular high-dimensional estimator is the graphical lasso, but it lacks robustness. We consider the high-dimensional independent contamination model. Here, even a small percentage of contaminated cells in the data matrix may lead to a high percentage of contaminated rows. Downweighting entire observations, which is done by traditional robust procedures, would then results in a loss of information. In this paper, we formally prove that replacing the sample covariance matrix in the graphical lasso with an elementwise robust covariance matrix leads to an elementwise robust, sparse precision matrix estimator computable in high-dimensions. Examples of such elementwise robust covariance estimators are given. The final precision matrix estimator is positive definite, has a high breakdown point under elementwise contamination and can be computed fast.


title: The Impact of Clinical Empathy on Patients and Clinicians : Understanding Empathy ' s Side Effects

text: Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.


Platelet-Rich Plasma: Applications in Dermatology


title: Computer Forensics Field Triage Process Model

text: With the proliferation of digital based evidence, the need for the timely identification, analysis and interpretation of digital evidence is becoming more crucial. In many investigations critical information is required while at the scene or within a short period of time measured in hours as opposed to days. The traditional cyber forensics approach of seizing a system(s)/media, transporting it to the lab, making a forensic image(s), and then searching the entire system for potential evidence, is no longer appropriate in some circumstances. In cases such as child abductions, pedophiles, missing or exploited persons, time is of the essence. In these types of cases, investigators dealing with the suspect or crime scene need investigative leads quickly; in some cases it is the difference between life and death for the victim(s). The Cyber Forensic Field Triage Process Model (CFFTPM) proposes an onsite or field approach for providing the identification, analysis and interpretation of digital evidence in a short time frame, without the requirement of having to take the system(s)/media back to the lab for an in-depth examination or acquiring a complete forensic image(s). The proposed model adheres to commonly held forensic principles, and does not negate the ability that once the initial field triage is concluded, the system(s)/storage media be transported back to a lab environment for a more thorough examination and analysis. The CFFTPM has been successfully used in various real world cases, Journal of Digital Forensics, Security and Law, Vol. 1(2) 20 and its investigative importance and pragmatic approach has been amply demonstrated. Furthermore, the derived evidence from these cases has not been challenged in the court proceedings where it has been introduced. The current article describes the CFFTPM in detail, discusses the model’s forensic soundness, investigative support capabilities and practical considerations.


title: Saliency-Guided Detection of Unknown Objects in RGB-D Indoor Scenes

text: This paper studies the problem of detecting unknown objects within indoor environments in an active and natural manner. The visual saliency scheme utilizing both color and depth cues is proposed to arouse the interests of the machine system for detecting unknown objects at salient positions in a 3D scene. The 3D points at the salient positions are selected as seed points for generating object hypotheses using the 3D shape. We perform multi-class labeling on a Markov random field (MRF) over the voxels of the 3D scene, combining cues from object hypotheses and 3D shape. The results from MRF are further refined by merging the labeled objects, which are spatially connected and have high correlation between color histograms. Quantitative and qualitative evaluations on two benchmark RGB-D datasets illustrate the advantages of the proposed method. The experiments of object detection and manipulation performed on a mobile manipulator validate its effectiveness and practicability in robotic applications.


title: Interactive Character Animation Using Simulated Physics: A State-of-the-Art Review

text: Physics simulation offers the possibility of truly responsive and realistic animation. Despite wide adoption of physics simulation for the animation of passive phenomena, such as fluids, cloths and rag-doll characters, commercial applications still resort to kinematics-based approaches for the animation of actively controlled characters. However, following a renewed interest in the use of physics simulation for interactive character animation, many recent publications demonstrate tremendous improvements in robustness, visual quality and usability. We present a structured review of over two decades of research on physics-based character animation, as well as point out various open research areas and possible future directions.


title: Resilient packet delivery through software defined redundancy: An experimental study

text: Mission-critical applications that use computer networks require guaranteed communication with bounds on the time needed to deliver packets, even if hardware along a path fails or congestion occurs. Current networking technologies follow a reactive approach in which retransmission mechanisms and routing update protocols respond once packet loss occurs. We present a proactive approach that pre-builds and monitors redundant paths to ensure timely delivery with no recovery latency, even in the case of a single-link failure. The paper outlines the approach, describes a new mechanism we call a redundancy controller, and explains the use of SDN to establish and change paths. The paper also reports on a prototype system and gives experimental measurements.


title: The FuzzyLog: A Partially Ordered Shared Log

text: The FuzzyLog is a partially ordered shared log abstraction. Distributed applications can concurrently append to the partial order and play it back. FuzzyLog applications obtain the benefits of an underlying shared log – extracting strong consistency, durability, and failure atomicity in simple ways – without suffering from its drawbacks. By exposing a partial order, the FuzzyLog enables three key capabilities for applications: linear scaling for throughput and capacity (without sacrificing atomicity), weaker consistency guarantees, and tolerance to network partitions. We present Dapple, a distributed implementation of the FuzzyLog abstraction that stores the partial order compactly and supports efficient appends / playback via a new ordering protocol. We implement several data structures and applications over the FuzzyLog, including several map variants as well as a ZooKeeper implementation. Our evaluation shows that these applications are compact, fast, and flexible: they retain the simplicity (100s of lines of code) and strong semantics (durability and failure atomicity) of a shared log design while exploiting the partial order of the FuzzyLog for linear scalability, flexible consistency guarantees (e.g., causal+ consistency), and network partition tolerance. On a 6-node Dapple deployment, our FuzzyLogbased ZooKeeper supports 3M/sec single-key writes, and 150K/sec atomic cross-shard renames.


title: Predictors of Relationship Satisfaction in Online Romantic Relationships

text: Based on traditional theories of interpersonal relationship development and on the hyperpersonal communication theory, this study examined predictors of relationship satisfaction for individuals involved in online romantic relationships. One hundredfourteen individuals (N1⁄4114) involved in online romantic relationships, and who had only engaged in computer-mediated communication (CMC) with their partners, completed an online questionnaire about their relationships. Intimacy, trust, and communication satisfaction were found to be the strongest predictors of relationship satisfaction for individuals involved in online romances. Additionally, perceptions of relationship variables differed depending on relationship length and time spent communicating. Implications for interpersonal and hyperpersonal communication theories, and future investigation of online relationships, are discussed.


title: Impact of Corpora Quality on Neural Machine Translation

text: Large parallel corpora that are automatically obtained from the web, documents or elsewhere often exhibit many corrupted parts that are bound to negatively affect the quality of the systems and models that learn from these corpora. This paper describes frequent problems found in data and such data affects neural machine translation systems, as well as how to identify and deal with them. The solutions are summarised in a set of scripts that remove problematic sentences from input corpora.


title: Computed tomographic analysis of tooth-bearing alveolar bone for orthodontic miniscrew placement.

text:
INTRODUCTION
When monocortical orthodontic miniscrews are placed in interdental alveolar bone, the safe position of the miniscrew tip should be ensured. This study was designed to quantify the periradicular space in the tooth-bearing area to provide practical guidelines for miniscrew placement.

METHODS
Computerized tomographs of 30 maxillae and mandibles were taken from nonorthodontic adults with normal occlusion. Both mesiodistal interradicular distance and bone thickness over the narrowest interradicular space (safety depth) were measured at 2, 4, 6, and 8 mm from the cementoenamel junction.

RESULTS
Mesiodistal space greater than 3 mm was available at the 8-mm level in the maxillary anterior region, between the premolars, and between the second premolar and the first molar at 4 mm. In the mandible, sufficient mesiodistal space was found between the premolars, between the molars, and between the second premolar and the first molar at the 4-mm level. Safety depth greater than 4 mm was found in the maxillary and mandibular intermolar regions, and between the second premolar and the first molar in both arches.

CONCLUSIONS
Subapical placement is advocated in the anterior segment. Premolar areas appear reliable in both arches. Angulated placement in the intermolar area is suggested to use the sufficient safety depth in this area.


title: Block Chain Technologies & The Semantic Web : A Framework for Symbiotic Development

text: The concept of peer-to-peer applications is not new, nor is the concept of distributed hash tables. What emerged in 2008 with the publication of the Bitcoin white paper was an incentive structure that unified these two software paradigms with a set of economic stimuli to motivate the creation of a dedicated computing network orders of magnitude more powerful than the world’s fastest supercomputers. The purpose of which is the maintenance of a massive distributed database known as the Bitcoin “block chain”. Apart from the digital currency it enables, block chain technology is a fascinating new computing paradigm with broad implications for the future development of the World Wide Web, and by extension, the further growth of Linked Data and the Semantic Web. This work is divided into two main sections, we first demonstrate how block chain technologies can contribute toward the realization of a more robust Semantic Web, and subsequently we provide a framework wherein the Semantic Web is utilized to ameliorate block chain technology itself.


title: A survey of remote optical photoplethysmographic imaging methods

text: In recent years researchers have presented a number of new methods for recovering physiological parameters using just low-cost digital cameras and image processing. The ubiquity of digital cameras presents the possibility for many new, low-cost applications of vital sign monitoring. In this paper we present a review of the work on remote photoplethysmographic (PPG) imaging using digital cameras. This review specifically focuses on the state-of-the-art in PPG imaging where: 1) measures beyond pulse rate are evaluated, 2) non-ideal conditions (e.g., the presence of motion artifacts) are explored, and 3) use cases in relevant environments are demonstrated. We discuss gaps within the literature and future challenges for the research community. To aid in the continuing advancement of PPG imaging research, we are making available a website with the references collected for this review as well as information on available code and datasets of interest. It is our hope that this website will become a valuable resource for the PPG imaging community. The site can be found at: http://web.mit.edu/~djmcduff/www/ remote-physiology.html.


title: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation.

text: Remote measurements of the cardiac pulse can provide comfortable physiological assessment without electrodes. However, attempts so far are non-automated, susceptible to motion artifacts and typically expensive. In this paper, we introduce a new methodology that overcomes these problems. This novel approach can be applied to color video recordings of the human face and is based on automatic face tracking along with blind source separation of the color channels into independent components. Using Bland-Altman and correlation analysis, we compared the cardiac pulse rate extracted from videos recorded by a basic webcam to an FDA-approved finger blood volume pulse (BVP) sensor and achieved high accuracy and correlation even in the presence of movement artifacts. Furthermore, we applied this technique to perform heart rate measurements from three participants simultaneously. This is the first demonstration of a low-cost accurate video-based method for contact-free heart rate measurements that is automated, motion-tolerant and capable of performing concomitant measurements on more than one person at a time.


title: Mobile monitoring with wearable photoplethysmographic biosensors.

text: earable biosensors (WBS) will permit continuous cardiovascular (CV) monitoring in a number of novel settings. Benefits may be realized in the diagnosis and treatment of a number of major diseases. WBS, in conjunction with appropriate alarm algorithms, can increase surveillance capabilities for CV catastrophe for high-risk subjects. WBS could also play a role in the treatment of chronic diseases, by providing information that enables precise titration of therapy or detecting lapses in patient compliance. WBS could play an important role in the wireless surveillance of people during hazardous operations (military, fire-fighting, etc.), or such sensors could be dispensed during a mass civilian casualty occurrence. Given that CV physio-logic parameters make up the " vital signs " that are the most important information in emergency medical situations, WBS might enable a wireless monitoring system for large numbers of at-risk subjects. This same approach may also have utility in monitoring the waiting room of today's overcrowded emergency departments. For hospital inpatients who require CV monitoring, current biosensor technology typically tethers patients in a tangle of cables, whereas wearable CV sensors could increase inpatient comfort and may even reduce the risk of tripping and falling, a perennial problem for hospital patients who are ill, medicated, and in an unfamiliar setting. On a daily basis, wearable CV sensors could detect a missed dose of medication by sensing untreated elevated blood pressure and could trigger an automated reminder for the patient to take the medication. Moreover, it is important for doctors to titrate the treatment of high blood pressure, since both insufficient therapy as well as excessive therapy (leading to abnormally low blood pressures) increase mortality. However, healthcare providers have only intermittent values of blood pressure on which to base therapy decisions; it is possible that continuous blood pressure monitoring would permit enhanced titration of therapy and reductions in mortality. Similarly, WBS would be able to log the physiologic signature of a patient's exercise efforts (manifested as changes in heart rate and blood pressure), permitting the patient and healthcare provider to assess compliance with a regimen proven to improve health outcomes. For patients with chronic cardiovascular disease, such as heart failure, home monitoring employing WBS may detect exacerbations in very early (and often easily treated) stages, long before the patient progresses to more dangerous levels that necessitate an emergency room visit and costly hospital admission. In this article we will address both technical and clinical …


title: An extended set of Haar-like features for rapid object detection

text: Recently Viola et al. [5] have introduced a rapid object detection scheme based on a boosted cascade of simple features. In this paper we introduce a novel set of rotated haar-like features, which significantly enrich this basic set of simple haar-like features and which can also be calculated very efficiently. At a given hit rate our sample face detector shows off on average a 10% lower false alarm rate by means of using these additional rotated features. We also present a novel post optimization procedure for a given boosted cascade improving on average the false alarm rate further by 12.5%. Using both enhancements the number of false detections is only 24 at a hit rate of 82.3% on the CMU face set [7].


title: Recovering pulse rate during motion artifact with a multi-imager array for non-contact imaging photoplethysmography

text: Photoplethysmography relies on characteristic changes in the optical absorption of tissue due to pulsatile (arterial) blood flow in peripheral vasculature. Sensors for observing the photoplethysmographic effect have traditionally required contact with the skin surface. Recent advances in non-contact imaging photoplethysmography have demonstrated that measures of cardiopulmonary system state, such as pulse rate, pulse rate variability, and respiration rate, can be obtained from a participant by imaging their face under relatively motionless conditions. A critical limitation in this method that must be resolved is the inability to recover these measures under conditions of head motion artifact. To investigate the adequacy of channel space dimensionality for the use of blind source separation in this context, nine synchronized, visible spectrum imagers positioned in a semicircular array centered on the imaged participant were used for data acquisition in a controlled lighting environment. Three-lead electrocardiogram and finger-tip reflectance photoplethysmogram were also recorded as ground truth signals. Controlled head motion artifact trial conditions were compared to trials in which the participant remained stationary, with and without the aid of a chinrest. Bootstrapped means of one-minute, non-overlapping trial segments show that, for situations involving little to no head motion, a single imager is sufficient for recovering pulse rate with an average absolute error of less than two beats per minute. However, error in the recovered pulse rate measurement for the single imager can be as high as twenty-two beats per minute when head motion artifact is severe. This increase in measurement error during motion artifact was mitigated by increasing the dimensionality of the imager channel space with multiple imagers in the array prior to applying blind source separation. In contrast to single-imager results, the multi-imager channel space resulted in an absolute error in the recovered pulse rate measurement that is comparable with pulse rate measured via fingertip reflectance photoplethysmography. These results demonstrate that non-contact, imaging photoplethysmography can be accurate in the presence of head motion artifact when a multi-imager array is implemented to increase the dimensionality of the decomposed channel space.


title: Inverse Risk-Sensitive Reinforcement Learning

text: We address the problem of inverse reinforcement learning in Markov decision processes where the agent is risk-sensitive. We derive a risk-sensitive reinforcement learning algorithm with convergence guarantees that employs convex risk metrics and models of human decisionmaking deriving from behavioral economics. The risk-sensitive reinforcement learning algorithm provides the theoretical underpinning for a gradient-based inverse reinforcement learning algorithm that minimizes a loss function defined on observed behavior of a risk-sensitive agent. We demonstrate the performance of the proposed technique on two examples: (i) the canonical Grid World example and (ii) a Markov decision process modeling ride-sharing passengers’ decisions given price changes. In the latter, we use pricing and travel time data from a ride-sharing company to construct the transition probabilities and rewards of the Markov decision process.


title: Minimization of symbolic automata

text: Symbolic Automata extend classical automata by using symbolic alphabets instead of finite ones. Most of the classical automata algorithms rely on the alphabet being finite, and generalizing them to the symbolic setting is not a trivial task. In this paper we study the problem of minimizing symbolic automata. We formally define and prove the basic properties of minimality in the symbolic setting, and lift classical minimization algorithms (Huffman-Moore's and Hopcroft's algorithms) to symbolic automata. While Hopcroft's algorithm is the fastest known algorithm for DFA minimization, we show how, in the presence of symbolic alphabets, it can incur an exponential blowup. To address this issue, we introduce a new algorithm that fully benefits from the symbolic representation of the alphabet and does not suffer from the exponential blowup. We provide comprehensive performance evaluation of all the algorithms over large benchmarks and against existing state-of-the-art implementations. The experiments show how the new symbolic algorithm is faster than previous implementations.


title: An Empirical Evaluation of True Online TD({\lambda})

text: The true online TD(λ) algorithm has recently been proposed (van Seijen and Sutton, 2014) as a universal replacement for the popular TD(λ) algorithm, in temporal-difference learning and reinforcement learning. True online TD(λ) has better theoretical properties than conventional TD(λ), and the expectation is that it also results in faster learning. In this paper, we put this hypothesis to the test. Specifically, we compare the performance of true online TD(λ) with that of TD(λ) on challenging examples, random Markov reward processes, and a real-world myoelectric prosthetic arm. We use linear function approximation with tabular, binary, and non-binary features. We assess the algorithms along three dimensions: computational cost, learning speed, and ease of use. Our results confirm the strength of true online TD(λ): 1) for sparse feature vectors, the computational overhead with respect to TD(λ) is minimal; for non-sparse features the computation time is at most twice that of TD(λ), 2) across all domains/representations the learning speed of true online TD(λ) is often better, but never worse than that of TD(λ), and 3) true online TD(λ) is easier to use, because it does not require choosing between trace types, and it is generally more stable with respect to the step-size. Overall, our results suggest that true online TD(λ) should be the first choice when looking for an efficient, general-purpose TD method.


title: Cause related marketing campaigns and consumer purchase intentions : The mediating role of brand awareness and corporate image

text: The purpose of this research is to investigate the kind of relationship between Cause Related Marketing (CRM) campaigns, brand awareness and corporate image as possible antecedents of consumer purchase intentions in the less developed country of Pakistan. An initial conceptualization was developed from mainstream literature to be validated through empirical research. The conceptualization was then tested with primary quantitative survey data collected from 203 students studying in different universities of Rawalpindi and Islamabad. Correlation and regression analysis were used to test the key hypothesis derived from literature positing brand awareness and corporate image as mediating the relationship between CRM and consumer purchase intentions. The findings indicate that consumer purchase intentions are influenced by the cause related marketing campaigns. Furthermore it was observed that the brand awareness and corporate image partially mediate the impact of CRM campaigns on consumer purchase intentions. The data was gathered from universities situated in Rawalpindi and Islamabad only. Hence, future research could extend these findings to other cities in Pakistan to test their generalizability. Further research can be carried out through data collection from those people who actually participated in cause related marketing campaigns to identify the original behavior of customers instead of their purchase intentions. This research and the claims made are limited to the FMCG industry. The key implications cause related marketing of these findings for marketing managers lend support for the use of campaigns in Pakistan. The findings also suggest some measures which can be taken in to consideration in order to enhance brand awareness and to improve corporate image as both variables mediate the impact of CRM campaigns on consumer purchase intentions. The study contributes to cause related marketing literature by indicating a mediating role of brand awareness and corporate image on CRM campaigns and consumer purchase intentions. This mediating role was ignored in previous studies. Moreover, it contributes to close the gap of empirical research in this field, which exists particularly due to the diverse attitude of customers in less developed countries such as Pakistan.


title: Loosecut: Interactive image segmentation with loosely bounded boxes

text: One popular approach to interactively segment an object of interest from an image is to annotate a bounding box that covers the object, followed by a binary labeling. However, the existing algorithms for such interactive image segmentation prefer a bounding box that tightly encloses the object. This increases the annotation burden, and prevents these algorithms from utilizing automatically detected bounding boxes. In this paper, we develop a new LooseCut algorithm that can handle cases where the bounding box only loosely covers the object. We propose a new Markov Random Fields (MRF) model for segmentation with loosely bounded boxes, including an additional energy term to encourage consistent labeling of similar-appearance pixels and a global similarity constraint to better distinguish the foreground and background. This MRF model is then solved by an iterated max-flow algorithm. We evaluate LooseCut in three public image datasets, and show its better performance against several state-of-the-art methods when increasing the bounding-box size.


title: Morphological Smoothing and Extrapolation of Word Embeddings

text: Languages with rich inflectional morphology exhibit lexical data sparsity, since the word used to express a given concept will vary with the syntactic context. For instance, each count noun in Czech has 12 forms (where English uses only singular and plural). Even in large corpora, we are unlikely to observe all inflections of a given lemma. This reduces the vocabulary coverage of methods that induce continuous representations for words from distributional corpus information. We solve this problem by exploiting existing morphological resources that can enumerate a word’s component morphemes. We present a latentvariable Gaussian graphical model that allows us to extrapolate continuous representations for words not observed in the training corpus, as well as smoothing the representations provided for the observed words. The latent variables represent embeddings of morphemes, which combine to create embeddings of words. Over several languages and training sizes, our model improves the embeddings for words, when evaluated on an analogy task, skip-gram predictive accuracy, and word similarity.


title: K-Automorphism: A General Framework For Privacy Preserving Network Publication

text: The growing popularity of social networks has generated interesting data management and data mining problems. An important concern in the release of these data for study is their privacy, since social networks usually contain personal information. Simply removing all identifiable personal information (such as names and social security number) before releasing the data is insufficient. It is easy for an attacker to identify the target by performing different structural queries. In this paper we propose k-automorphism to protect against multiple structural attacks and develop an algorithm (called KM) that ensures k-automorphism. We also discuss an extension of KM to handle “dynamic” releases of the data. Extensive experiments show that the algorithm performs well in terms of protection it provides.


title: Making Smart Contracts Smarter

text:
Cryptocurrencies record transactions in a decentralized data structure called a blockchain. Two of the most popular cryptocurrencies, Bitcoin and Ethereum, support the feature to encode rules or scripts for processing transactions. This feature has evolved to give practical shape to the ideas of smart contracts, or full-fledged programs that are run on blockchains. Recently, Ethereum's smart contract system has seen steady adoption, supporting tens of thousands of contracts, holding millions dollars worth of virtual coins.
 In this paper, we investigate the security of running smart contracts based on Ethereum in an open distributed network like those of cryptocurrencies. We introduce several new security problems in which an adversary can manipulate smart contract execution to gain profit. These bugs suggest subtle gaps in the understanding of the distributed semantics of the underlying platform. As a refinement, we propose ways to enhance the operational semantics of Ethereum to make contracts less vulnerable. For developers writing contracts for the existing Ethereum system, we build a symbolic execution tool called Oyente to find potential security bugs. Among 19, 336 existing Ethereum contracts, Oyente flags 8, 833 of them as vulnerable, including the TheDAO bug which led to a 60 million US dollar loss in June 2016. We also discuss the severity of other attacks for several case studies which have source code available and confirm the attacks (which target only our accounts) in the main Ethereum network.


title: Enumerating the Non-Isomorphic Assembly Configurations of Modular Robotic Systems

text: A \modular" robotic system consists of joint and link modules that can be assembled in a variety of conngurations to meet diierent or changing task requirements. However, due to typical symmetries in module design, diierent assembly conngurations may lead to robotic structures which are kinematically identical, or isomorphic. This paper considers how to enumerate the non-isomorphic assembly conngurations of a modular robotic system. We introduce an Assembly Incidence Matrix (AIM) to represent a modular robot assembly con-guration. Then we use symmetries of the module geometry and graph isomorphisms to deene an equivalence relation on the AIMs. Equivalent AIMs represent isomorphic robot assembly conngurations. Based on this equivalence relation, we propose an algorithm to generate non-isomorphic assembly conngurations of an n-link tree-like robot with diierent joint and link module types. Examples demonstrate that this method is a signiicant improvement over a brute force enumeration process.


title: Locally Normalized Filter Banks Applied to Deep Neural-Network-Based Robust Speech Recognition

text: This letter describes modifications to locally normalized filter banks (LNFB), which substantially improve their performance on the Aurora-4 robust speech recognition task using a Deep Neural Network-Hidden Markov Model (DNN-HMM)-based speech recognition system. The modified coefficients, referred to as LNFB features, are a filter-bank version of locally normalized cepstral coefficients (LNCC), which have been described previously. The ability of the LNFB features is enhanced through the use of newly proposed dynamic versions of them, which are developed using an approach that differs somewhat from the traditional development of delta and delta–delta features. Further enhancements are obtained through the use of mean normalization and mean–variance normalization, which is evaluated both on a per-speaker and a per-utterance basis. The best performing feature combination (typically LNFB combined with LNFB delta and delta–delta features and mean–variance normalization) provides an average relative reduction in word error rate of 11.4% and 9.4%, respectively, compared to comparable features derived from Mel filter banks when clean and multinoise training are used for the Aurora-4 evaluation. The results presented here suggest that the proposed technique is more robust to channel mismatches between training and testing data than MFCC-derived features and is more effective in dealing with channel diversity.


title: DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

text: Probabilistic forecasting, i.e. estimating the probability distribution of a time series’ future given its past, is a key enabler for optimizing business processes. In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place. In this paper we propose DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an auto-regressive recurrent network model on a large number of related time series. We demonstrate how by applying deep learning techniques to forecasting, one can overcome many of the challenges faced by widely-used classical approaches to the problem. We show through extensive empirical evaluation on several real-world forecasting data sets that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.


title: IMPACTS DUE TO INTERNET ADDICTION AMONG MALAYSIAN UNIVERSITY STUDENTS Azizah Zainudin

text: The purpose of this study is to study the impacts due to Internet addiction among Malaysian university students. Research methodology used in this study was by distributing survey questions to 653 university students from five different universities in Malaysia. There were four possible impacts measured in this research study which include Academic Performances, Relationships, Personality and Lifestyle. The finding shows that, Internet addiction cause problems with respondents’ academic performances, having bad personality and practicing an unhealthy lifestyle. There were significantly differences in academic performances, personality and lifestyle between “Average user” and “Excessive user”. The above matter will be further discussed throughout this


title: Micropower energy harvesting

text: More than a decade of research in the field of thermal, motion, vibration and electromagnetic radiation energy harvesting has yielded increasing power output and smaller embodiments. Power management circuits for rectification and DC–DC conversion are becoming able to efficiently convert the power from these energy harvesters. This paper summarizes recent energy harvesting results and their power man-


title: The Role of Asymmetric Dimethylarginine (ADMA) in Endothelial Dysfunction and Cardiovascular Disease

text: Endothelium plays a crucial role in the maintenance of vascular tone and structure. Endothelial dysfunction is known to precede overt coronary artery disease. A number of cardiovascular risk factors, as well as metabolic diseases and systemic or local inflammation cause endothelial dysfunction. Nitric oxide (NO) is one of the major endothelium derived vaso-active substances whose role is of prime importance in maintaining endothelial homeostasis. Low levels of NO are associated with impaired endothelial function. Asymmetric dimethylarginine (ADMA), an analogue of L-arginine, is a naturally occurring product of metabolism found in human circulation. Elevated levels of ADMA inhibit NO synthesis and therefore impair endothelial function and thus promote atherosclerosis. ADMA levels are increased in people with hypercholesterolemia, atherosclerosis, hypertension, chronic heart failure, diabetes mellitus and chronic renal failure. A number of studies have reported ADMA as a novel risk marker of cardiovascular disease. Increased levels of ADMA have been shown to be the strongest risk predictor, beyond traditional risk factors, of cardiovascular events and all-cause and cardiovascular mortality in people with coronary artery disease. Interventions such as treatment with L-arginine have been shown to improve endothelium-mediated vasodilatation in people with high ADMA levels. However the clinical utility of modifying circulating ADMA levels remains uncertain.


title: Sindice.com: a document-oriented lookup index for open linked data

text: Data discovery on the SemanticWeb requires crawling and indexing of statements, in addition to the ‘linked-data’ approach of de-referencing resourceURIs. Existing SemanticWeb search engines are focused on database-like functionality, compromising on index size, query performance and live updates.Wepresent Sindice, a lookup indexover SemanticWeb resources. Our index allows applications to automatically locate documents containing information about a given resource. In addition, we allow resource retrieval through inverse-functional properties, offer a full-text search and index SPARQL endpoints. Finally, we extend the sitemap protocol to efficiently index large datasets with minimal impact on data providers.


title: Evaluating radial, axial and transverse flux topologies for 'in-wheel' motor

text: Three different motor topologies, (1) radial, (2) axial and (3) transverse flux, are investigated for 'in-wheel' electric propulsion system. The design constraints are: 350mm/spl times/100mm package limitation, 48 V DC bus restriction and per phase inverter current limited to 500 A/sup pk/. In addition, magnet pole pairs are fixed to 9 due to limitation in 'position sensor' capacity. The paper summarizes results obtained from analytical design followed by 2D and 3D static and transient finite element analysis accompanied with experimental verification for the radial flux topology.


title: Essential features of serious games design in higher education: Linking learning attributes to game mechanics

text: This paper consolidates evidence and material from a range of specialist and disciplinary fields to provide an evidence-based review and synthesis on the design and use of serious games in higher education. Search terms identified 165 papers reporting conceptual and empirical evidence on how learning attributes and game mechanics may be planned, designed, and implemented by university teachers interested in using games, which are integrated into lesson plans and orchestrated as part of a learning sequence at any scale. The findings outline the potential of classifying the links between learning attributes and game mechanics as a means to scaffold teachers’ understanding of how to perpetuate learning in optimal ways whilst enhancing the in-game learning experience. The findings of this paper provide a foundation for describing methods, frames, and discourse around experiences of design and use of serious games, linked to methodological limitations and recommendations for further research in this area. Practitioner Notes What is already known about this topic • Serious game design is a relatively new discipline that couples learning design with game features. A key characteristic of this approach is grounded in educational need and theory, rather than a focus purely on entertainment. Under this approach, a common method for designing serious games involves the creation of learning strategies, content and principles for the purpose of enhancing the student’s learning experience. • There are no pedagogically driven strategies that take into account how learning attributes are interlinked to game elements for balancing learning with play. This is due to a limited evidence base of comparative evaluations assessing differing game designs against a single pedagogical model, or vice versa. This often leads practitioners seeking to introduce games into classroom with difficulties identifying what defines a game or how design should be enacted in a way that encompasses particular learning interventions. • Qualitative research methodologies have not been utilised as often for discerning how people experience, understand and use serious games for teaching and learning in higher education. What this paper adds • This paper presents the foundation of a taxonomy linking learning and game attributes along with teacher roles, aiming to encourage the cross-fertilisation and further integration of evidence on serious game design. The initial findings provide insight for practitioners on elements common to games and serious games, and how these link to particular learning design strategies that may afford pedagogically-rich games. • It informs development in the design and use of serious games to support teaching and learning in higher education. Given the key role of practitioners in the development of a wide range of serious games, due to the iterative and participatory methods frequently adopted, it offers insight into game elements and mechanics, allowing practitioners to easily relate to how learning activities, outcomes, feedback and roles may vary and visualised in relation to what needs to be learned out of playing the game. • The paper also identifies gaps in the evidence base and avenues for future research. Through this a practitioner can gain insight into the current unknowns in the area, and relate their experience of introducing games in the classroom to the current evidence base. Implications for practice and/or policy • The taxonomy informs serious game/instructional designers, game developers, academics and students about how learning elements (e.g. learning activities, learning outcomes, feedback and assessment) can be represented in games. We advocate dialogue between teachers and serious game designers, for improving the process of amalgamating learning with fun; and whilst the idea of using digital games is relatively new, teachers have a wealth of experience introducing learning features in games to be used into their classroom activities which frequently set out undocumented. • It aids the delineation of game attributes and associated game categories for characterising games based on primary purpose and use. Planning and designing teaching and learning in a game and teacher’s associated role in guiding the in-game learning process is a consistent challenge and this paper seeks to provide a foundation by which this process can be undertaken in an informed and conscious manner. • The findings of the review act as the point of departure for creating a research agenda for understanding disjunctions between espoused and enacted personal theories of using serious games through qualitative methodologies. Introduction Serious Games (SGs) design is a relatively new discipline that couples learning design with game mechanics and logic (de Freitas, 2006; Hainley 2006; Westera et al., 2008). Design for SGs involves the creation of learning activities that may use the whole game or entail a gaming element (e.g. leader boards, virtual currencies, in-game hints) for the purpose of transforming the student’s learning experience. Arguments against SGs have centred upon lack of empirical evidence in support of their efficacy; and the fact that appropriate research methodologies have not been enacted as yet for discerning how people understand and use SGs for teaching and learning in higher education (Mayer 2012; Connolly 2012). However, there are studies in the UK and US respectively that have demonstrated positive results in large sample groups (see for example Dunwell et al., 2014; Hertzog et al., 2014; Kato et al., 2008). Research evidence stresses the lack of commonly accepted pedagogically-driven strategies to afford game mechanisms and suggest that an inclusive model that takes into account pedagogy and teaching strategy, aligned to game activity and assessment is necessary for balancing play features with pedagogical aspects. This argument stems from an important observation that learning is a constructive process, which encompasses aspects of collaborative learning in which knowledge creation emerges through discussion and negotiation between individuals and groups. With these perspectives, this paper draws together evidence and material from a range of specialist and disciplinary fields to offer a critical review and synthesis of the design and use of SGs in higher


title: Removing Shadows from Images

text: We attempt to recover a 2D chromaticity intrinsic variation of a single RGB image which is independent of lighting, without requiring any prior knowledge about the camera. The proposed algorithm aims to learn an invariant direction for projecting from 2D color space to the greyscale intrinsic image. We notice that along this direction, the entropy in the derived invariant image is minimized. The experiments conducted on various inputs indicate that this method achieves intrinsic 2D chromaticity images which are free of shadows. In addition, we examined the idea to utilize projection pursuit instead of entropy minimization to find the desired direction.


title: Kwyjibo: automatic domain name generation

text: Automatically generating ‘good’ domain names that are random yet pronounceable is a problem harder than it first appears. The problem is related to random word generation, and we survey and categorize existing techniques before presenting our own syllable-based algorithm that produces higher-quality results. Our results are also applicable elsewhere, in areas such as password generation, username generation, and even computer-generated poetry. Copyright © 2008 John Wiley & Sons, Ltd.


title: Multi-session PLDA scoring of i-vector for partially open-set speaker detection

text: This paper advocates the use of probabilistic linear discriminant analysis (PLDA) for partially open-set detection task with multiple i-vectors enrollment condition. Also referred to as speaker verification, the speaker detection task has always been considered under an open-set scenario. In this paper, a more general partially open-set speaker detection problem in considered, where the imposters might be one of the known speakers previously enrolled to the system. We show how this could be coped with by modifying the definition of the alternative hypothesis in the PLDA scoring function. We also look into the impact of the conditionalindependent assumption as it was used to derive the PLDA scoring function with multiple training i-vectors. Experiments were conducted using the NIST 2012 Speaker Recognition Evaluation (SRE’12) datasets to validate various points discussed in the paper.


title: The effects of prosocial video games on prosocial behaviors: international evidence from correlational, longitudinal, and experimental studies.

text: Although dozens of studies have documented a relationship between violent video games and aggressive behaviors, very little attention has been paid to potential effects of prosocial games. Theoretically, games in which game characters help and support each other in nonviolent ways should increase both short-term and long-term prosocial behaviors. We report three studies conducted in three countries with three age groups to test this hypothesis. In the correlational study, Singaporean middle-school students who played more prosocial games behaved more prosocially. In the two longitudinal samples of Japanese children and adolescents, prosocial game play predicted later increases in prosocial behavior. In the experimental study, U.S. undergraduates randomly assigned to play prosocial games behaved more prosocially toward another student. These similar results across different methodologies, ages, and cultures provide robust evidence of a prosocial game content effect, and they provide support for the General Learning Model.


title: Oral squamous cell carcinoma overview.

text: Most cancer in the head and neck is squamous cell carcinoma (HNSCC) and the majority is oral squamous cell carcinoma (OSCC). This is an overview of oral squamous cell carcinoma (OSCC) highlighting essential points from the contributors to this issue, to set the scene. It emphasises the crucial importance of prevention and the necessarily multidisciplinary approach to the diagnosis and management of patients with OSCC.


title: Hybrid crafting: towards an integrated practice of crafting with physical and digital components

text: With current digital technologies, people have large archives of digital media, such as images and audio files, but there are only limited means to include these media in creative practices of crafting and making. Nevertheless, studies have shown that crafting with digital media often makes these media more cherished and that people enjoy being creative with their digital media. This paper aims to open up the way for novel means for crafting, which include digital media in integrations with physical construction, here called ‘hybrid crafting’. Notions of hybrid crafting were explored to inform the design of products or systems that may support these new crafting practices. We designed ‘Materialise’—a building set that allows for the inclusion of digital images and audio files in physical constructions by using tangible building blocks that can display images or play audio files, alongside a variety of other physical components—and used this set in four hands-on creative workshops to gain insight into how people go about doing hybrid crafting; whether hybrid crafting is desirable; what the characteristics of hybrid crafting are; and how we may design to support these practices. By reflecting on the findings from these workshops, we provide concrete guidelines for the design of novel hybrid crafting products or systems that address craft context, process and result. We aim to open up the design space to designing for hybrid crafting because these new practices provide interesting new challenges and opportunities for future crafting that can lead to novel forms of creative expression.


title: MOA: Massive Online Analysis

text: MassiveOnline Analysis (MOA) is a software environment for implementing al orithms and running experiments for online learning from evolving data str eams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Na ı̈ve Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.


title: Deep Learning with Limited Numerical Precision

text: Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a crucial role in determining the network’s behavior during training. Our results show that deep networks can be trained using only 16-bit wide fixed-point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy. We also demonstrate an energy-efficient hardware accelerator that implements low-precision fixed-point arithmetic with stochastic rounding.


title: Learning Deep Features for Scene Recognition using Places Database

text: [1] L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding, 2007. [2] G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. 2007. [3] S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. CVPR, 2006. [4] L.-J. Li and L. Fei-Fei. What, where and who? classifying events by scene and object recognition. In Proc. ICCV, 2007. [5] G. Patterson and J. Hays. Sun attribute database: Discovering, annotating, and recognizing scene attributes. In Proc. CVPR, 2012.


title: A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks

text: Deep networks are state-of-the-art models used for understanding the content of images, videos, audio and raw input data. Current computing systems are not able to run deep network models in real-time with low power consumption. In this paper we present nn-X: a scalable, low-power coprocessor for enabling real-time execution of deep neural networks. nn-X is implemented on programmable logic devices and comprises an array of configurable processing elements called collections. These collections perform the most common operations in deep networks: convolution, subsampling and non-linear functions. The nn-X system includes 4 high-speed direct memory access interfaces to DDR3 memory and two ARM Cortex-A9 processors. Each port is capable of a sustained throughput of 950 MB/s in full duplex. nn-X is able to achieve a peak performance of 227 G-ops/s, a measured performance in deep learning applications of up to 200 G-ops/s while consuming less than 4 watts of power. This translates to a performance per power improvement of 10 to 100 times that of conventional mobile and desktop processors.


title: Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks

text: Convolutional neural network (CNN) has been widely employed for image recognition because it can achieve high accuracy by emulating behavior of optic nerves in living creatures. Recently, rapid growth of modern applications based on deep learning algorithms has further improved research and implementations. Especially, various accelerators for deep CNN have been proposed based on FPGA platform because it has advantages of high performance, reconfigurability, and fast development round, etc. Although current FPGA accelerators have demonstrated better performance over generic processors, the accelerator design space has not been well exploited. One critical problem is that the computation throughput may not well match the memory bandwidth provided an FPGA platform. Consequently, existing approaches cannot achieve best performance due to under-utilization of either logic resource or memory bandwidth. At the same time, the increasing complexity and scalability of deep learning applications aggravate this problem. In order to overcome this problem, we propose an analytical design scheme using the roofline model. For any solution of a CNN design, we quantitatively analyze its computing throughput and required memory bandwidth using various optimization techniques, such as loop tiling and transformation. Then, with the help of rooine model, we can identify the solution with best performance and lowest FPGA resource requirement. As a case study, we implement a CNN accelerator on a VC707 FPGA board and compare it to previous approaches. Our implementation achieves a peak performance of 61.62 GFLOPS under 100MHz working frequency, which outperform previous approaches significantly.