title: Delay-Aware Scheduling and Resource Optimization With Network Function Virtualization

abstract: To accelerate the implementation of network functions/middle boxes and reduce the deployment cost, recently, the concept of network function virtualization (NFV) has emerged and become a topic of much interest attracting the attention of researchers from both industry and academia. Unlike the traditional implementation of network functions, a software-oriented approach for virtual network functions (VNFs) creates more flexible and dynamic network services to meet a more diversified demand. Software-oriented network functions bring along a series of research challenges, such as VNF management and orchestration, service chaining, VNF scheduling for low latency and efficient virtual network resource allocation with NFV infrastructure, among others. In this paper, we study the VNF scheduling problem and the corresponding resource optimization solutions. Here, the VNF scheduling problem is defined as a series of scheduling decisions for network services on network functions and activating the various VNFs to process the arriving traffic. We consider VNF transmission and processing delays and formulate the joint problem of VNF scheduling and traffic steering as a mixed integer linear program. Our objective is to minimize the makespan/latency of the overall VNFs’ schedule. Reducing the scheduling latency enables cloud operators to service (and admit) more customers, and cater to services with stringent delay requirements, thereby increasing operators’ revenues. Owing to the complexity of the problem, we develop a genetic algorithm-based method for solving the problem efficiently. Finally, the effectiveness of our heuristic algorithm is verified through numerical evaluation. We show that dynamically adjusting the bandwidths on virtual links connecting virtual machines, hosting the network functions, reduces the schedule makespan by 15%–20% in the simulated scenarios.


title: O278-7393/87/SOO.75 Retention of Spanish Vocabulary Over

abstract: Thirty-five individuals who had learned and relearaed 50 English-Spanish word pairs were tested for recall and recognition after an interval of 8 years. Two variables, the spacing between successive releaming sessions and the number of presentations required to encode individual word pairs, are excellent predictors of the likelihood of achieving permastore retention. Optimum recall occurs for words encoded in 1 -2 presentations and accessed at intervals of 30 days. Both variables yield monotonic retention functions that account for a range of variation from 0% to 23% recall. These variables also have very significant effects on the recognition of unrecalled words. A recent investigation of the retention of Spanish language learned in school (Bahrick, 1984) shows that a portion of the acquired knowledge has a life span of more than 25 years even if the knowledge is not rehearsed or accessed during that long interval. Another part of the originally acquired knowledge is lost within 5 years after training terminates, and virtually no knowledge is lost during the interval between 5 and 25 years following acquisition. This finding suggests the challenging possibilities of identifying conditions of learning and/or characteristics of material associated with a prospective life span of more than 25 years and of differentiating these conditions and characteristics from those associated with material destined to have a relatively short life span (less than 5 years). Such information would augment the very limited knowledge of life span memory currently available and make memory research more relevant to the needs of educators who have an obvious interest in prolonging the life span of transmitted knowledge. However, the research is arduous because it requires longitudinal investigations to extend substantially beyond the 5-year period during which material with a short life span is likely to be forgotten. The foregoing considerations led us to search for relevant longitudinal data, and we realized that such data might be available to us if we conducted a follow-up to an earlier investigation (Bahrick, 1979). The aim of the earlier investigation was to establish the effect of various rehearsal schedules on maintaining access to learned material over periods of 1 -9 months. One part of that investigation required college students to learn and relearn 50 English-Spanish word pairs in successive training sessions, spaced at intervals varying from a few seconds


title: Open Design Challenges for Interactive Emergent Narrative

abstract: We introduce a research framework for the design of interactive experiences in the domain of emergent narrative, an application area of computational narrative in which stories emerge bottom-up from the behavior of autonomous characters in a simulated storyworld. Prior work in this area has largely concerned the development and tuning of the simulations themselves from which interesting stories may reliably emerge, but this approach will not necessarily improve system performance at its most crucial level—the actual interactive experience. Looking to completed experiences, namely simulation games like Dwarf Fortress and The Sims, we identify a series of shortcomings that yield four design challenges at the level of interaction: modular content, compositional representational strategies, story recognition, and story support. In this paper, we motivate and discuss each of these design challenges and, for each, summarize prior work and propose new approaches that future work might take.


title: PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes

abstract: DNA microarrays can be used to identify gene expression changes characteristic of human disease. This is challenging, however, when relevant differences are subtle at the level of individual genes. We introduce an analytical strategy, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes. Using this approach, we identify a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle. Expression of these genes is high at sites of insulin-mediated glucose disposal, activated by PGC-1alpha and correlated with total-body aerobic capacity. Our results associate this gene set with clinically important variation in human metabolism and illustrate the value of pathway relationships in the analysis of genomic profiling experiments.


title: RFID Technology for IoT-Based Personal Healthcare in Smart Spaces

abstract: The current evolution of the traditional medical model toward the participatory medicine can be boosted by the Internet of Things (IoT) paradigm involving sensors (environmental, wearable, and implanted) spread inside domestic environments with the purpose to monitor the user’s health and activate remote assistance. RF identification (RFID) technology is now mature to provide part of the IoT physical layer for the personal healthcare in smart environments through low-cost, energy-autonomous, and disposable sensors. It is here presented a survey on the state-of-the-art of RFID for application to bodycentric systems and for gathering information (temperature, humidity, and other gases) about the user’s living environment. Many available options are described up to the application level with some examples of RFID systems able to collect and process multichannel data about the human behavior in compliance with the power exposure and sanitary regulations. Open challenges and possible new research trends are finally discussed.


title: Evaluation of Web Service Recommendation Performance via Sparsity Alleviating by Specificity-Aware Ontology-Based Clustering

abstract: With the development of information technology, considerable web services are published on the Internet rapidly in the last few years. It becomes a challenging task to recommend applicable web services to users and service recommendation becomes an influential approach to guide users to discover suitable services. In this situation, Collaborative Filtering (CF) based on rating is one of the powerful approaches for service recommendation but suffers from the data sparsity and cold-start problems due to the insufficiency of user-service information. In this paper, we present a novel ontology-based clustering approach that based on the terms specificity and similarity to overcome those limitations. We alleviate the sparsity problem using this novel clustering approach and then service user similarity is calculated using a Pearson Correlation Coefficient (PCC) measurement. Finally, user rating is predicted based on the alleviated user ratings and PCC values and recommendation is based on these predictions. We evaluated in the several viewpoints based on our previous work and results show that our approach can successfully alleviate the sparsity and cold-start problems and works effectively by lower prediction error compared with existing approaches.


The New DLR Flight Dynamics Library Gertjan Looye


title: Robotics CTF (RCTF), a playground for robot hacking

abstract: Robots state of insecurity is onstage. There is an emerging concern about major robot vulnerabilities and their adverse consequences. However, there is still a considerable gap between robotics and cybersecurity domains. For the purpose of filling that gap, the present technical report presents the Robotics CTF (RCTF), an online playground to challenge robot security from any browser. We describe the architecture of the RCTF and provide 9 scenarios where hackers can challenge the security of different robotic setups. Our work empowers security researchers to a) reproduce virtual robotic scenarios locally and b) change the networking setup to mimic real robot targets. We advocate for hacker powered security in robotics and contribute by open sourcing our scenarios.


title: Social Commerce as a Driver to Enhance Trust and Intention to Use Cryptocurrencies for Electronic Payments

abstract: The deployment of cryptocurrencies in e-commerce has reached a significant number of transactions and continuous increases in monetary circulation; nevertheless, they face two impediments: a lack of awareness of the technological utility, and a lack of trust among consumers. E-commerce carried out through social networks expands its application to a new paradigm called social commerce. Social commerce uses the content generated within social networks to attract new consumers and influence their behavior. The objective of this paper is to analyze the role played by social media in increasing trust and intention to use cryptocurrencies in making electronic payments. It develops a model that combines constructs from social support theory, social commerce, and the technology acceptance model. This model is evaluated using the partial least square analysis. The obtained results show that social commerce increases the trust and intention to use cryptocurrencies. However, mutual support among participants does not generate sufficient trust to adequately promote the perceived usefulness of cryptocurrencies. This research provides a practical tool for analyzing how collaborative relationships that emerge in social media can influence or enhance the adoption of a new technology in terms of perceived trust and usefulness. Furthermore, it provides a significant contribution to consumer behavior research by applying the social support theory to the adoption of new information technologies. These theoretical and practical contributions are detailed in the final section of the paper.


title: Quantifying the Security Posture of Containerized Mission Critical Systems

abstract: Determining the security posture of containerized mission-critical systems is difficult given the vast number of parameters that determine a system's ability to withstand cyber-attacks. In many cases, technical audits can be performed to determine a system's security posture and to evaluate how well they are configured to protect against known cyber-threats. Properly configuring systems can lead to higher security, however, the configuration and auditing process can be time-consuming and error-prone. In addition, the results obtained from these audits can be difficult to summarize into one meaningful metric that accurately characterizes system's security posture as guided by customer needs. In this work, we propose an approach for computing a security-posture metric for containerized systems that supports operators during the sense-making process that follows traditional security audits. The results of this work can be used on a per-deployment case, taking into account what matters to operators of containerized mission-critical systems.


title: Dealing with non-functional requirements: three experimental studies of a process-oriented approach

abstract: Quality characteristics are vital for the success of software systems. To remedy the problems inherent in ad hoc development, a framework has been developed to deal with non-functional requirements (quality requirements or NFRs). Taking the premise that the quality of a product depends on the quality of the process that leads from high-Ievel NFRs to the product, the framework's objectives are to represent NFR-specific requirements, consider design tradeoffs, relate design decisions to IYFRs, justify the decisions, and assist defect detection. The purpose of this paper is to give an initial evaluation of the extent to which the framework's objectives are met. Three small portions of information systems were studied by the authors using the framework. The framework and empirical studies are evaluated herein, both from the viewpoint of domain experts who have reviewed the framework and studies, and ourselves as framework developers and users. The systems studied have a variety of characteristics, reflecting a variety of real application domains, and the studies deal with three important classes of NFRs for systems, namely, accuracy, security, and performance. The studies provide preliminary support for the usefulness of certain aspects of the framework, while raising some open issues.


title: Multi-tier multi-access edge computing: The role for the fourth industrial revolution

abstract: The fifth-generation (5G) mobile systems are considered as a promising infrastructure to provide connectivity for massive Internet-of-Things (IoT) devices leading to the fourth industrial revolution (Industry 4.0), i.e., smart manufacturing. The smart manufacturing is characterized by capabilities of digitization, smartization, and connectivity for manufacturing operations and machinery where a huge amount of data has to be processed within ultralow latency. Since the machinery is limited in computing performance, the data generated by the machinery should to be offloaded to the network for process and storage. In this circumstance, the multi-tier multi-access edge computing (mMEC), one of the most important 5G technologies, demonstrate its advantages for data handling within the aforementioned smart-manufacturing requirements. In this paper, we present the mMEC architecture and investigate its role in the Industry 4.0. Moreover, the challenges of utilizing the mMEC in the smart factory are discussed in order to identify future research trends.


title: Evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm

abstract: The Markov Blanket Bayesian Classifier is a recently-proposed algorithm for construction of probabilistic classifiers. This paper presents an empirical comparison of the MBBC algorithm with three other Bayesian classifiers: Naive Bayes, Tree-Augmented Naive Bayes and a general Bayesian network. All of these are implemented using the K2 framework of Cooper and Herskovits. The classifiers are compared in terms of their performance (using simple accuracy measures and ROC curves) and speed, on a range of standard benchmark data sets. It is concluded that MBBC is competitive in terms of speed and accuracy with the other algorithms considered.


title: Real-time collision avoidance in human-robot interaction based on kinetostatic safety field

abstract: This paper addresses the problem of collision avoidance in human-robot interaction. To this end, we introduce the concept of kinetostatic safety field, a novel safety assessment about the risk in the vicinity of a rigid body (including a robot link or a human body part). The safety field depends on the position and velocity of the body but it is also influenced by its real shape and size. Since all the computation can be performed in closed form, the safety field is suitable for real-time applications. Moreover, we present a safety-oriented control strategy for redundant manipulators, based on safety field and developed entirely on the kinematic level, where the kinematic redundancy is exploited for simultaneous task performance and collision avoidance, such as self-collision avoidance and human-robot coexistence. The proposed control strategy is validated through experiments performed on ABB's FRIDA dual arm robot.


title: A Survey of RDF Data Management Systems

abstract: RDF is increasingly being used to encode data for the semantic web and for data exchange. There have been a large number of works that address RDF data management. In this paper we provide an overview of these works.


title: Transfer Learning in Collaborative Filtering with Uncertain Ratings

abstract: To solve the sparsity problem in collaborative filtering, researchers have introduced transfer learning as a viable approach to make use of auxiliary data. Most previous transfer learning works in collaborative filtering have focused on exploiting point-wise ratings such as numerical ratings, stars, or binary ratings of likes/dislikes. However, in many real-world recommender systems, many users may be unwilling or unlikely to rate items with precision. In contrast, practitioners can turn to various non-preference data to estimate a range or rating distribution of a user's preference on an item. Such a range or rating distribution is called an uncertain rating since it represents a rating spectrum of uncertainty instead of an accurate point-wise score. In this paper, we propose an efficient transfer learning solution for collaborative filtering, known as transfer by integrative factorization (TIF), to leverage such auxiliary uncertain ratings to improve the performance of recommendation. Inparticular, we integrate auxiliary data of uncertain ratings as additional constraints in the target matrix factorization problem, and learn an expected rating value for each uncertain rating automatically. The advantages of our proposed approach include the efficiency and the improved effectiveness of collaborative filtering, showing that incorporating the auxiliary data of uncertain ratings can really bring a benefit. Experimental results on two movie recommendation tasks show that our TIF algorithm performs significantly better over a state-of-the-art non-transfer learning method.


title: Benefits of SVC and STATCOM for electric utility application

abstract: Examination of the behavior of SVCs and STATCOMs in electric power systems is presented. The paper is based on analytical and simulation analysis, and conclusions can be used as power industry guidelines. We explain the principle structures of SVCs and STATCOMs, the models for dynamic studies, and the impact of these devices on steady state voltage and transient voltage stability. Sensitivity analysis is provided which shows the impact of SVCs and STATCOMs with regard to network strength. Harmonic issues, space requirements, and price discussions are also briefly addressed.


title: Ultra-broadband low axial ratio corrugated quad-ridge polarizer

abstract: The design and fabrication of an Ultra broadband square quad-ridge polarizer is discussed here. The principal advantages of this topology relay on both the instantaneous bandwidth and the axial ratio improvement. Experimental measurements exhibit very good agreement with the predicted results given by Mode Matching techniques. The structure provides an extremely flat axial ratio (AR 25dB at both square ports over the extended Ku band (= 60%). Moreover, yield analysis and scaling properties demonstrate the robustness of this design against fabrication tolerances.


title: Distraction-based neural networks for modeling documents

abstract: Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences. Whether and how such an approach can be extended to help model larger spans of text, e.g., documents, is intriguing, and further investigation would still be desirable. This paper aims to enhance neural network models for such a purpose. A typical problem of document-level modeling is automatic summarization, which aims to model documents in order to generate summaries. In this paper, we propose neural models to train computers not just to pay attention to specific regions and content of input documents with attention models, but also distract them to traverse between different content of a document so as to better grasp the overall meaning for summarization. Without engineering any features, we train the models on two large datasets. The models achieve the state-of-the-art performance, and they significantly benefit from the distraction modeling, particularly when input documents are long.


title: Online Object Tracking: A Benchmark

abstract: Object tracking is one of the most important components in numerous applications of computer vision. While much progress has been made in recent years with efforts on sharing code and datasets, it is of great importance to develop a library and benchmark to gauge the state of the art. After briefly reviewing recent advances of online object tracking, we carry out large scale experiments with various evaluation criteria to understand how these algorithms perform. The test image sequences are annotated with different attributes for performance evaluation and analysis. By analyzing quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.


title: A 0.45-V Low-Power OOK/FSK RF Receiver in 0.18 $\mu\text{m}$ CMOS Technology for Implantable Medical Applications

abstract: A 0.45-V low-power 0.18 μm CMOS OOK/FSK RF receiver for implantable medical applications is proposed. The receiver utilizes a wake-up mechanism to adjust its power consumption automatically by reading the amplitude of the input wireless OOK/FSK modulated RF signal directly. No additional wireless wake-up commands are required. Such a normally-off and instanton scheme reduces the power consumption of this receiver significantly. The power consumption is 129 μW in sleep mode and 352 μW in wake-up mode. Other techniques, such as third-orderharmonic cancellation, subharmonic mixing, and forward body biasing are also adopted for better linearity, higher LO-to-RF isolation, and lower VDD, respectively. The measurement results show that the proposed receiver consumes only 2.6 nJ/bit (OOK) and 1 nJ/bit (FSK) to achieve the sensitivities of -55 dBm (OOK) and -53.5 dBm (FSK) in BER <; 10-3 constraint. The proposed receiver is designed for low-power and short-distance MICS band applications.


title: A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

abstract: Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain methods.


COMPASS: a CS program assessment project


title: A NEW COLOR CORRECTION METHOD FOR UNDERWATER IMAGING

abstract: Abstract. Recovering correct or at least realistic colors of underwater scenes is a very challenging issue for imaging techniques, since illumination conditions in a refractive and turbid medium as the sea are seriously altered. The need to correct colors of underwater images or videos is an important task required in all image-based applications like 3D imaging, navigation, documentation, etc. Many imaging enhancement methods have been proposed in literature for these purposes. The advantage of these methods is that they do not require the knowledge of the medium physical parameters while some image adjustments can be performed manually (as histogram stretching) or automatically by algorithms based on some criteria as suggested from computational color constancy methods. One of the most popular criterion is based on gray-world hypothesis, which assumes that the average of the captured image should be gray. An interesting application of this assumption is performed in the Ruderman opponent color space lαβ, used in a previous work for hue correction of images captured under colored light sources, which allows to separate the luminance component of the scene from its chromatic components. In this work, we present the first proposal for color correction of underwater images by using lαβ color space. In particular, the chromatic components are changed moving their distributions around the white point (white balancing) and histogram cutoff and stretching of the luminance component is performed to improve image contrast. The experimental results demonstrate the effectiveness of this method under gray-world assumption and supposing uniform illumination of the scene. Moreover, due to its low computational cost it is suitable for real-time implementation.


title: Feature extraction and classification of EEG signals for mapping motor area of the brain

abstract: This paper presents the study of open source electroencephalogram (EEG) data from 30 subjects performing actual motor tasks, for localizing brain motor areas responsible for the tasks. The extracted features from independent component analysis (ICA) of the EEG data are Gaussian weighted to obtain feature vectors. Two dimensional scalp maps are used for task based selection of features belonging to the primary and sensory motor regions of the brain. The final feature vectors thus obtained are given as input to two classifiers, viz. linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). Classification using LDA gives localization accuracies of 68.42% for right fist movement, 67.16% for left fist movement and 84.40% for both feet movement respectively. The corresponding classification accuracies for QDA were 92.98% for right fist movement, 70.15% for left fist movement and 98.58% for both feet tasks respectively. The average accuracy for motor task classification is 73.33% for LDA and 87.24% for QDA.


s personal copy Contradictory deceptive behavior in online dating


title: E-FFC: an enhanced form-focused crawler for domain-specific deep web databases

abstract: A key problem of retrieving, integrating and mining rich and high quality information from massive Deep Web Databases (WDBs) online is how to automatically and effectively discover and recognize domain-specific WDBs' entry points, i.e., forms, in the Web. It has been a challenging task because domain-specific WDBs' forms with dynamic and heterogeneous properties are very sparsely distributed over several trillion Web pages. Although significant efforts have been made to address the problem and its special cases, more effective solutions remain to be further explored towards achieving both the satisfactory harvest rate and coverage rate of domain-specific WDBs' forms simultaneously. In this paper, an Enhanced Form-Focused Crawler for domain-specific WDBs (E-FFC) has been proposed as a novel framework to address existing solutions' limitations. The E-FFC, based on the divide and conquer strategy, employs a series of novel and effective strategies/algorithms, including a two-step page classifier, a link scoring strategy, classifiers for advanced searchable and domain-specific forms, crawling stopping criteria, etc. to its end achieving the optimized harvest rate and coverage rate of domain-specific WDBs' forms simultaneously. Experiments of the E-FFC over a number of real Web pages in a set of representative domains have been conducted and the results show that the E-FFC outperforms the existing domain-specific Deep Web Form-Focused Crawlers in terms of the harvest rate, coverage rate and crawling robustness.


title: Hospital processes within an integrated system view: A hybrid simulation approach

abstract: Processes in hospitals or in other healthcare institutions are usually analyzed and optimized isolated for enclosed organizations like single hospital wards or certain clinical pathways. However, many workflows should be considered in a broader scope in order to better represent the reality, i.e., in combination with other processes and in contexts of macro structures. Therefore, an integrated view is necessary which enables to combine different coherences. This can be achieved by hybrid simulation. In this case, processes can be modeled and simulated by discrete simulation techniques (i.e., DES or ABS) at the meso-level. However, holistic structures can be comfortably implemented using continuous methods (i.e., SD). This paper presents a theoretical approach that enables to consider reciprocal influences between processes and higher level entities, but also to combine hospital workflows with other subjects (e.g., ambulance vehicles).


title: Container-Managed ETL Applications for Integrating Data in Near Real-Time

abstract: As the analytical capabilities and applications of e-business systems expand, providing real-time access to critical business performance indicators to improve the speed and effectiveness of business operations has become crucial. The monitoring of business activities requires focused, yet incremental enterprise application integration (EAI) efforts and balancing information requirements in real-time with historical perspectives. The decision-making process in traditional data warehouse environments is often delayed because data cannot be propagated from the source system to the data warehouse in a timely manner. In this paper, we present an architecture for a container-based ETL (extraction, transformation, loading) environment, which supports a continual near real-time data integration with the aim of decreasing the time it takes to make business decisions and to attain minimized latency between the cause and effect of a business decision. Instead of using vendor proprietary ETL solutions, we use an ETL container for managing ETLets (pronounced “et-lets”) for the ETL processing tasks. The architecture takes full advantage of existing J2EE (Java 2 Platform, Enterprise Edition) technology and enables the implementation of a distributed, scalable, near real-time ETL environment. We have fully implemented the proposed architecture. Furthermore, we compare the ETL container to alternative continuous data integration approaches.


title: Bag-of-Entities Representation for Ranking

abstract: This paper presents a new bag-of-entities representation for document ranking, with the help of modern knowledge bases and automatic entity linking. Our system represents query and documents by bag-of-entities vectors constructed from their entity annotations, and ranks documents by their matches with the query in the entity space. Our experiments with Freebase on TREC Web Track datasets demonstrate that current entity linking systems can provide sufficient coverage of the general domain search task, and that bag-of-entities representations outperform bag-of-words by as much as 18% in standard document ranking tasks.


title: Shoulder impingement in tennis/racquetball players treated with subscapularis myofascial treatments.

abstract: Conservative care of the athlete with shoulder impingement includes activity modification, application of ice, nonsteroidal anti-inflammatory drugs, subacromial corticosteroid injections, and physiotherapy. This case report describes the clinical treatment and outcome of three patients with shoulder impingement syndrome who did not respond to traditional treatment. Two of the three were previously referred for arthroscopic surgery. All three were treated with subscapularis trigger point dry needling and therapeutic stretching. They responded to treatment and had returned to painless function at follow-up 2 years later.


Temporal stability and validity of two personality disorder inventories.


title: Predicting Parking Lot Occupancy in Vehicular Ad Hoc Networks

abstract: The search for free parking places is a promising application for vehicular ad hoc networks (VANETs). In order to guide drivers to a free parking place at their destination, it is necessary to estimate the occupancy state of the parking lots within the destination area at time of arrival. In this paper, we present a model to predict parking lot occupancy based on information exchanged among vehicles. In particular, our model takes the age of received parking lot information and the time needed to arrive at a certain parking lot into account and estimates the future parking situation at time of arrival. It is based on queueing theory and uses a continuous-time homogeneous Markov model. We have evaluated the model in a simulation study based on a detailed model of the city of Brunswick, Germany.


title: Emotion Enhances Learning via Norepinephrine Regulation of AMPA-Receptor Trafficking

abstract: Emotion enhances our ability to form vivid memories of even trivial events. Norepinephrine (NE), a neuromodulator released during emotional arousal, plays a central role in the emotional regulation of memory. However, the underlying molecular mechanism remains elusive. Toward this aim, we have examined the role of NE in contextual memory formation and in the synaptic delivery of GluR1-containing alpha-amino-3-hydroxy-5-methyl-4-isoxazoleproprionic acid (AMPA)-type glutamate receptors during long-term potentiation (LTP), a candidate synaptic mechanism for learning. We found that NE, as well as emotional stress, induces phosphorylation of GluR1 at sites critical for its synaptic delivery. Phosphorylation at these sites is necessary and sufficient to lower the threshold for GluR1 synaptic incorporation during LTP. In behavioral experiments, NE can lower the threshold for memory formation in wild-type mice but not in mice carrying mutations in the GluR1 phosphorylation sites. Our results indicate that NE-driven phosphorylation of GluR1 facilitates the synaptic delivery of GluR1-containing AMPARs, lowering the threshold for LTP, thereby providing a molecular mechanism for how emotion enhances learning and memory.


title: Software development for the evaluation of the lightning performance of overhead transmission lines

abstract: A useful tool for the evaluation of the lightning performance of overhead transmission lines has been developed in Matlab. The software (LPTL) runs as a Microsoft Windows application and features a user-friendly graphics interface. For given overhead transmission line parameters, ground flash density and lightning crest current distribution the lightning performance of the line can be easily evaluated according to several lightning attachment models. Through an application to a typical 400 kV double circuit line of the Hellenic transmission system the effects of lightning attachment model and lightning crest current distribution on the lightning performance of the line are quantified. Generally, electrogeometric models yield the highest shielding failure flashover rate whereas the lowest backflashover rate. As the median value of the lightning crest current distribution increases the shielding failure flashover rate decreases whereas backflashover rate increases. The application software is a useful tool for utilities and can also be used for educational purposes in high voltage engineering courses.


title: Design and Optimization of Wide and Dual Band Waveguide Polarizer

abstract: The design of a wide band and dual band square waveguide iris polarizer covering two separate bands is discussed here. The relation between bandwidth of the polarizer and the dimension of the square waveguide are analyzed and deduced. Based on this analysis, concluded the requirement of the waveguide's dimension for design of a wide band/dual band polarizer. Finally introduce a procedure for designing this kind of polarizer. A compact polarizer with six irises and a shorter electrical length was developed and optimized within the band of 24 GHz-36 GHz which is over 40% bandwidth. Simulation results exhibited excellent performances: the axial ratio is less than 1.2 dB and the VSWR is less than 1.1 over the whole continuous frequency band, axial ratio is less than 0.4 dB over the two separate bands which are 25 GHz-26 GHz and 34 GHz-35 GHz.


title: IP-Based IoT Device Detection

abstract: Recent IoT-based DDoS attacks have exposed how vulnerable the Internet can be to millions of insufficiently secured IoT devices. To understand the risks of these attacks requires learning about these IoT devices---where are they, how many are there, how are they changing? In this paper, we propose a new method to find IoT devices in Internet to begin to assess this threat. Our approach requires observations of flow-level network traffic and knowledge of servers run by the manufacturers of the IoT devices. We have developed our approach with 10 device models by 7 vendors and controlled experiments. We apply our algorithm to observations from 6 days of Internet traffic at a college campus and partial traffic from an IXP to detect IoT devices.


title: Leveraging Large Face Recognition Data for Emotion Classification

abstract: In this paper we describe a solution to our entry for the emotion recognition challenge EmotiW 2017. We propose an ensemble of several models, which capture spatial and audio features from videos. Spatial features are captured by convolutional neural networks, pretrained on large face recognition datasets. We show that usage of strong industry-level face recognition networks increases the accuracy of emotion recognition. Using our ensemble we improve on the previous year's best result on the test set by about 1%, achieving a 60.03% classification accuracy without any use of visual temporal information, showing a top-2 result in this challenge.


CryptoDL : Towards Deep Learning over Encrypted Data


title: Discovering value in academic social networks: A case study in ResearchGate

abstract: The research presented in this paper is about detecting collaborative networks inside the structure of a research social network. As case study we consider ResearchGate and SEE University academic staff. First we describe the methodology used to crawl and create an academic-academic network depending from their fields of interest. We then calculate and discuss four social network analysis centrality measures (closeness, betweenness, degree, and PageRank) for entities in this network. In addition to these metrics, we have also investigated grouping of individuals, based on automatic clustering depending from their reciprocal relationships.


Argumentation-based negotiation


title: Mechatronics design of multi-finger robot hand

abstract: The author reports the development of a multi-finger robot hand with the mechatronics approach. The proposed robot hand has 4 fingers with 14 under-actuated joints driven by 10 linear actuators with linkages. Each of the 10 nodes in the distributed control system uses position and current feedback to monitor the contact stiffness and control the grasping force according to the motor current change rate. The combined force and position control loop enable the robot hand to grasp an object with the unknown shape. Pre-defined tasks, such as grasping and pinching are stored as scripts in the hand controller to provide a high-level programming interface for the upstream robot controller. The mechanical design, controller design and co-simulation are performed in an integrated model-based software environment, and also for the real time code generation and for mechanical parts manufacturing with a 3D printer. Based on the same model for design, a virtual robot hand interface is developed to provide off-line simulation tool and user interface to the robot hand to reduce the programming effort in fingers' motion planning. In the development of the robot hand, the mechatronics approach has been proven to be an indispensable tool for such a complex system.


title: Multi-volume occupancy grids: An efficient probabilistic 3D mapping model for micro aerial vehicles

abstract: Advancing research into autonomous micro aerial vehicle navigation requires data structures capable of representing indoor and outdoor 3D environments. The vehicle must be able to update the map structure in real time using readings from range-finding sensors when mapping unknown areas; it must also be able to look up occupancy information from the map for the purposes of localization and path-planning. Mapping models that have been used for these tasks include voxel grids, multi-level surface maps, and octrees. In this paper, we suggest a new approach to 3D mapping using a multi-volume occupancy grid, or MVOG. MVOGs explicitly store information about both obstacles and free space. This allows us to correct previous potentially erroneous sensor readings by incrementally fusing in new positive or negative sensor information. In turn, this enables extracting more reliable probabilistic information about the occupancy of 3D space. MVOGs outperform existing probabilistic 3D mapping methods in terms of memory usage, due to the fact that observations are grouped together into continuous vertical volumes to save space. We describe the techniques required for mapping using MVOGs, and analyze their performance using indoor and outdoor experimental data.


title: Evolution of artificial terrains for video games based on accessibility

abstract: Diverse methods have been developed to generate terrains under constraints to control terrain features, but most of them use strict restrictions. However, there are situations were more flexible restrictions are sufficient, such as ensuring that terrains have enough accessible area, which is an important trait for video games. The Genetic Terrain Program technique, based on genetic programming, was used to automatically evolve Terrain Programs (TPs - which are able to generate terrains procedurally) for the desired accessibility parameters. Results showed that the accessibility parameters have negligible influence on the evolutionary system and that the terminal set has a major role on the terrain look. TPs produced this way are already being used on Chapas video game.


title: Fractionating theory of mind: A meta-analysis of functional brain imaging studies

abstract: Abstract We meta-analyzed imaging studies on theory of mind and formed individual task groups based on stimuli and instructions. Overlap in brain activation between all task groups was found in the mPFC and in the bilateral posterior TPJ. This supports the idea of a core network for theory of mind that is activated whenever we are reasoning about mental states, irrespective of the task- and stimulus-formats (Mar, 2011). In addition, we found a number of task-related activation differences surrounding this core-network. ROI based analyses show that areas in the TPJ, the mPFC, the precuneus, the temporal lobes and the inferior frontal gyri have distinct profiles of task-related activation. Functional accounts of these areas are reviewed and discussed with respect to our findings.


title: Elementary: Large-scale Knowledge-base Construction via Machine Learning and Statistical Inference

abstract: Researchers have approached knowledge-base construction KBC with a wide range of data resources and techniques. The authors present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge bases. Using Elementary, they have implemented a solution to the TAC-KBP challenge with quality comparable to the state of the art, as well as an end-to-end online demonstration that automatically and continuously enriches Wikipedia with structured data by reading millions of webpages on a daily basis. The authors describe several challenges and their solutions in designing, implementing, and deploying Elementary. In particular, the authors first describe the conceptual framework and architecture of Elementary to integrate different data resources and KBC techniques in a principled manner. They then discuss how they address scalability challenges to enable Web-scale deployment. The authors empirically show that this decomposition-based inference approach achieves higher performance than prior inference approaches. To validate the effectiveness of Elementary's approach to KBC, they experimentally show that its ability to incorporate diverse signals has positive impacts on KBC quality.


title: Spring Embedders and Force Directed Graph Drawing Algorithms

abstract: Force-directed algorithms are among the most flexible methods for calculating layouts of simple undirected graphs. Also known as spring embedders, such algorithms calculate the layout of a graph using only information contained within the structure of th eg raph itself, rather than relying on domain-specific knowledge. Graphs drawn with these algorithms tend to be aesthetically pleasing, exhibit symmetries, and tend to produce crossing-free layouts for planar graphs. In this survey we consider several classical algorithms, starting from Tutte’s 1963 barycentric method, and including recent scalable multiscale methods for large and dynamic graphs.


title: 3.3kW on board charger for electric vehicle

abstract: In this paper 3.3 ㎾ on-board charger is proposed for electric vehicles or plug-in hybrid electric vehicles. Considering the life time of charger, new topology which is not using the high-voltage electrolytic capacitors is proposed. This charger is comprised of two stages that one is boost converter stage for PFC control and the other is uncontrolled LLC resonant converter for galvanic isolation and step-down of voltage. Proposed charger is implemented and it is 7.1 liters in volume and 6.8㎏ in weight. The experimental result indicates 92.5% of the efficiency at input voltage 220Vac and 88.3% at input voltage 110V ac under 3.3㎾ of output power. And the output voltage ranges is from 150V to 450V.


title: Single-Image Super-Resolution: A Benchmark

abstract: Single-image super-resolution is of great importance for vision applications, and numerous algorithms have been proposed in recent years. Despite the demonstrated success, these results are often generated based on different assumptions using different datasets and metrics. In this paper, we present a systematic benchmark evaluation for state-of-the-art single-image super-resolution algorithms. In addition to quantitative evaluations based on conventional full-reference metrics, human subject studies are carried out to evaluate image quality based on visual perception. The benchmark evaluations demonstrate the performance and limitations of state-of-the-art algorithms which sheds light on future research in single-image super-resolution.


title: Spectral–Spatial Classification and Shape Features for Urban Road Centerline Extraction

abstract: This letter presents a two-step method for urban main road extraction from high-resolution remotely sensed imagery by integrating spectral-spatial classification and shape features. In the first step, spectral-spatial classification segments the imagery into two classes, i.e., the road class and the nonroad class, using path openings and closings. The local homogeneity of the gray values obtained by local Geary's C is then fused with the road class. In the second step, the road class is refined by using shape features. The experimental results indicated that the proposed method was able to achieve a comparatively good performance in urban main road extraction.


title: Handbook of Writing for the Mathematical Sciences

abstract: Preface 1. General principles 2. Writer's tools and recommended reading 3. Mathematical writing 4. English usage 5. When English is a foreign language 6. Writing a paper 7. Revising a draft 8. Publishing a paper 9. Writing and defending a thesis 10. Writing a talk 11. Giving a talk 12. Preparing a poster 13. TeX and LaTeX 14. Aids and resources for writing and research Appendices Glossary Bibliography Name index Subject index.


title: A Behavioral Model of Digital Music Piracy

abstract: The increasing pervasiveness of the internet, broadband connections and the emergence of digital compression technologies have dramatically changed the face of digital music piracy. Digitally compressed music files are essentially a perfect public economic good, and illegal copying of these files has increasingly become rampant. This paper presents a study on the behavioral dynamics which impact the piracy of digital audio files, and provides a contrast with software piracy. Our results indicate that the general ethical model of software piracy is also broadly applicable to audio piracy. However, significant enough differences with software underscore the unique dynamics of audio piracy. Practical implications that can help the recording industry to effectively combat piracy, and future research directions are highlighted.


title: Multi-task Sparse Metric Learning for Monitoring Patient Similarity Progression

abstract: A clinically meaningful distance metric, which is learned from measuring patient similarity, plays an important role in clinical decision support applications. Several metric learning approaches have been proposed to measure patient similarity, but they are mostly designed for learning the metric at only one time point/interval. It leads to a problem that those approaches cannot reflect the similarity variations among patients with the progression of diseases. In order to capture similarity information from multiple future time points simultaneously, we formulate a multi-task metric learning approach to identify patient similarity. However, it is challenging to directly apply traditional multi-task metric learning methods to learn such similarities due to the high dimensional, complex and noisy nature of healthcare data. Besides, the disease labels often have clinical relationships, which should not be treated as independent. Unfortunately, traditional formulation of the loss function ignores the degree of labels' similarity. To tackle the aforementioned challenges, we propose mtTSML, a multi-task triplet constrained sparse metric learning method, to monitor the similarity progression of patient pairs. In the proposed model, the distance for each task can be regarded as the combination of a common part and a task-specific one in the transformed low-rank space. We then perform sparse feature selection for each individual task to select the most discriminative information. Moreover, we use triplet constraints to guarantee the margin between similar and less similar pairs according to the ordered information of disease severity levels (i.e. labels). The experimental results on two real-world healthcare datasets show that the proposed multi-task metric learning method significantly outperforms the state-of-the-art baselines, including both single-task and multi-task metric learning methods.


title: A monolithic current-mode CMOS DC-DC converter with on-chip current-sensing technique

abstract: A monolithic current-mode CMOS DC-DC converter with integrated power switches and a novel on-chip current sensor for feedback control is presented in this paper. With the proposed accurate on-chip current sensor, the sensed inductor current, combined with the internal ramp signal, can be used for current-mode DC-DC converter feedback control. In addition, no external components and no extra I/O pins are needed for the current-mode controller. The DC-DC converter has been fabricated with a standard 0.6-/spl mu/m CMOS process. The measured absolute error between the sensed signal and the inductor current is less than 4%. Experimental results show that this converter with on-chip current sensor can operate from 300 kHz to 1 MHz with supply voltage from 3 to 5.2 V, which is suitable for single-cell lithium-ion battery supply applications. The output ripple voltage is about 20 mV with a 10-/spl mu/F off-chip capacitor and 4.7-/spl mu/H off-chip inductor. The power efficiency is over 80% for load current from 50 to 450 mA.


title: Learning Deployment Trade-offs for Self-Optimization of Internet of Things Applications

abstract: The Internet of Things (IoT) is the next big wave in computing characterized by large scale open ended heterogeneous network of things, with varying sensing, actuating, computing and communication capabilities. Compared to the traditional field of autonomic computing, the IoT is characterized by an open ended and highly dynamic ecosystem with variable workload and resource availability. These characteristics make it difficult to implement self-awareness capabilities for IoT to manage and optimize itself. In this work, we introduce a methodology to explore and learn the trade-offs of different deployment configurations to autonomously optimize the QoS and other quality attributes of IoT applications. Our experiments demonstrate that our proposed methodology can automate the efficient deployment of IoT applications in the presence of multiple optimization objectives and variable operational circumstances.


title: Evolving Gene Expression Programming Classifiers for Ensemble Prediction of Movements on the Stock Market

abstract: Forecasting applications on the stock market attract much interest from researchers in the artificial intelligence field. The problem tackled in this study concerns predicting the direction of change of stock price indices, formulated in terms of binary classification. We use gene expression programming to evolve pools of binary classifiers and investigate several approaches to construct ensembles based on them. We compare the performance of the obtained classifiers with those of Naive Bayes, Support Vector Machines, Multilayer Perceptron, Decision Table and Random Forrest. The experiments performed on real-world stock market data show that the ensembles of GEP-evolved classifier models are competitive to classifiers trained by state-of-the-art machine learning methods.


title: EVENT STUDIES IN INFORMATION SYSTEMS RESEARCH: PAST, PRESENT AND FUTURE

abstract: Event studies, which gauge stock market reactions to specific events or happenings that may impact a business's operations, represent a relatively new but fast growing research approach in the information systems field. Building on four earlier literature reviews, in this paper we examine seventy-three event studies dealing with information technology. The purpose of this review is to assess the current state of information technology related event studies and to identify needs and opportunities for future research.


title: A survey of procedural terrain generation techniques using evolutionary algorithms

abstract: This paper provides a review of existing approaches to using evolutionary algorithms (EA) during procedural terrain generation (PTG) processes in video games. A reliable PTG algorithm would allow game maps to be created partially or completely autonomously, reducing the development cost of a game and providing players with more content. Specifically, the use of EA raises possibilities of more control over the terrain generation process, as well as the ability to tailor maps for individual users. In this paper we outline the prominent algorithms that use EA in terrain generation, describing their individual advantages and disadvantages. This is followed by a comparison of the core features of these approaches and an analysis of their appropriateness for generating game terrain. This survey concludes with open challenges for future research.


title: 27.12 MHz large voltage gain resonant converter with low voltage stress

abstract: This paper presents the design and implementation of a high frequency switching power converter with a large voltage gain and air core inductors. Specifically, we demonstrate a 40 W, 30 V to 300 V dc-dc converter switching at 27.12 MHz. Operating at this frequency reduces bulk energy storage over conventional low frequency designs and allows for fast transient response. We implement a resonant converter based on the Φ2 inverter of [1]. In this paper, we evaluate and compare two rectifier topologies with lower voltage stress than the class E rectifiers described in [2], to get large voltage ratios. Unique to this design is the ability to provide short bursts of high voltage while promising excellent performance and high power density.


title: Blended Coarse Gradient Descent for Full Quantization of Deep Neural Networks

abstract: Quantized deep neural networks (QDNNs) are attractive due to their much lower memory storage and faster inference speed than their regular full-precision counterparts. To maintain the same performance level especially at low bit-widths, QDNNs must be retrained. Their training involves piecewise constant activation functions and discrete weights; hence, mathematical challenges arise. We introduce the notion of coarse gradient and propose the blended coarse gradient descent (BCGD) algorithm, for training fully quantized neural networks. Coarse gradient is generally not a gradient of any function but an artificial ascent direction. The weight update of BCGD goes by coarse gradient correction of a weighted average of the full-precision weights and their quantization (the so-called blending), which yields sufficient descent in the objective value and thus accelerates the training. Our experiments demonstrate that this simple blending technique is very effective for quantization at extremely low bit-width such as binarization. In full quantization of ResNet-18 for ImageNet classification task, BCGD gives 64.36% top-1 accuracy with binary weights across all layers and 4-bit adaptive activation. If the weights in the first and last layers are kept in full precision, this number increases to 65.46%. As theoretical justification, we show convergence analysis of coarse gradient descent for a two-linear-layer neural network model with Gaussian input data and prove that the expected coarse gradient correlates positively with the underlying true gradient.


title: Adapting a Spanish microtext normalization system to English.

abstract: In this article we describe the microtext normalization system we have used to participate in the Normalization of Noisy Text Task of the ACL W-NUT 2015 Workshop. Our normalization system was originally developed for text mining tasks on Spanish tweets. Our main goals during its development were flexibility, scalability and maintainability, in order to test a wide variety of approximations to the problem at hand with minimum effort. We will pay special attention to the process of adapting the components of our system to deal with English tweets which, as we will show, was achieved without major modifications of its base structure.


title: Design of a nonlinear variable-gain fuzzy controller for FACTS devices

abstract: The paper presents the design of a nonlinear variable-gain fuzzy controller for a flexible ac transmission systems (FACTS) device like the unified power flow controller (UPFC) to enhance the transient stability performance of power systems. The fuzzy controller uses a numerical consequent rule base of the Takagi-Sugeno type, which can be either linear- or nonlinear-producing control-gain variation over a very wide range. This type of fuzzy control is expected to be more robust and effective in damping electromechanical oscillations of the power systems in comparison with the conventional PI regulators used for UPFC control. Computer simulation results validate the superior performance of this controller.


title: Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review

abstract: With the advances in information technology (IT) criminals are using cyberspace to commit numerous cyber crimes. Cyber infrastructures are highly vulnerable to intrusions and other threats. Physical devices and human intervention are not sufficient for monitoring and protection of these infrastructures; hence, there is a need for more sophisticated cyber defense systems that need to be flexible, adaptable and robust, and able to detect a wide variety of threats and make intelligent real-time decisions. Numerous bio-inspired computing methods of Artificial Intelligence have been increasingly playing an important role in cyber crime detection and prevention. The purpose of this study is to present advances made so far in the field of applying AI techniques for combating cyber crimes, to demonstrate how these techniques can be an effective tool for detection and prevention of cyber attacks, as well as to give the scope for future work.


title: Object Detection and Viewpoint Estimation with Auto-masking Neural Network

abstract: Simultaneously detecting an object and determining its pose has become a popular research topic in recent years. Due to the large variances of the object appearance in images, it is critical to capture the discriminative object parts that can provide key information about the object pose. Recent part-based models have obtained state-of-the-art results for this task. However, such models either require manually defined object parts with heavy supervision or a complicated algorithm to find discriminative object parts. In this study, we have designed a novel deep architecture, called Auto-masking Neural Network (ANN), for object detection and viewpoint estimation. ANN can automatically learn to select the most discriminative object parts across different viewpoints from training images. We also propose a method of accurate continuous viewpoint estimation based on the output of ANN. Experimental results on related datasets show that ANN outperforms previous methods.


title: A Supervisory Control Approach to Dynamic Cyber-Security

abstract: An analytical approach for a dynamic cyber-security problem that captures progressive attacks to a computer network is presented. We formulate the dynamic security problem from the defender’s point of view as a supervisory control problem with imperfect information, modeling the computer network’s operation by a discrete event system. We consider a min-max performance criterion and use dynamic programming to determine, within a restricted set of policies, an optimal policy for the defender. We study and interpret the behavior of this optimal policy as we vary certain parameters of the supervisory control problem.


title: A Comparative Study of Histogram Equalization Based Image Enhancement Techniques for Brightness Preservation and Contrast Enhancement

abstract: Histogram Equalization is a contrast enhancement technique in the image processing which uses the histogram of image. However histogram equalization is not the best method for contrast enhancement because the mean brightness of the output image is significantly different from the input image. There are several extensions of histogram equalization has been proposed to overcome the brightness preservation challenge. Contrast enhancement using brightness preserving bi-histogram equalization (BBHE) and Dualistic sub image histogram equalization (DSIHE) which divides the image histogram into two parts based on the input mean and median respectively then equalizes each sub histogram independently. This paper provides review of different popular histogram equalization techniques and experimental study based on the absolute mean brightness error (AMBE), peak signal to noise ratio (PSNR), Structure similarity index (SSI) and Entropy.


title: Manifest variable path analysis: Potentially serious and misleading consequences due to uncorrected measurement error.

abstract: Despite clear evidence that manifest variable path analysis requires highly reliable measures, path analyses with fallible measures are commonplace even in premier journals. Using fallible measures in path analysis can cause several serious problems: (a) As measurement error pervades a given data set, many path coefficients may be either over- or underestimated. (b) Extensive measurement error diminishes power and can prevent invalid models from being rejected. (c) Even a little measurement error can cause valid models to appear invalid. (d) Differential measurement error in various parts of a model can change the substantive conclusions that derive from path analysis. (e) All of these problems become increasingly serious and intractable as models become more complex. Methods to prevent and correct these problems are reviewed. The conclusion is that researchers should use more reliable measures (or correct for measurement error in the measures they do use), obtain multiple measures for use in latent variable modeling, and test simpler models containing fewer variables.


Anatomical guidelines for miniscrew insertion: palatal sites.


title: Supplier Selection Problems in Fashion Business Operations with Sustainability Considerations

abstract: In recent years, sustainability has been a major focus of fashion business operations because fashion industry development causes harmful effects to the environment, both indirectly and directly. The sustainability of the fashion industry is generally based on several levels and this study focuses on investigating the optimal supplier selection problem for sustainable materials supply in fashion clothing production. Following the ground rule that sustainable development is based on the Triple Bottom Line (TBL), this paper has framed twelve criteria from the economic, environmental and social perspectives for evaluating suppliers. The well-established multi-criteria decision making tool Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed for ranking potential suppliers among the pool of suppliers. Through a real case study, the proposed approach has been applied and some managerial implications are derived.


title: Intrusion detection via system call traces

abstract: Unusual behavior in computer systems can be detected by monitoring the system calls being executed by programs. Analysis of the temporal ordering of these calls reveals that such anomalies are localized within traces and that normal program behavior can be described compactly using deterministic finite automata. This article presents preliminary work in analyzing system call traces, particularly their structure during normal and anomalous behavior.


title: A novel paradigm for calculating Ramsey number via Artificial Bee Colony Algorithm

abstract: The Ramsey number is of vital importance in Ramsey's theorem. This paper proposed a novel methodology for constructing Ramsey graphs about R(3,10), which uses Artificial Bee Colony optimization(ABC) to raise the lower bound of Ramsey number R(3,10). The r(3,10)-graph contains two limitations, that is, neither complete graphs of order 3 nor independent sets of order 10. To resolve these limitations, a special mathematical model is put in the paradigm to convert the problems into discrete optimization whose smaller minimizers are correspondent to bigger lower bound as approximation of inf R(3,10). To demonstrate the potential of the proposed method, simulations are done to to minimize the amount of these two types of graphs. For the first time, four r(3,9,39) graphs with best approximation for inf R(3,10) are reported in simulations to support the current lower bound for R(3,10). The experiments' results show that the proposed paradigm for Ramsey number's calculation driven by ABC is a successful method with the advantages of high precision and robustness.


title: Cricoid pressure training using simulation: a systematic review and meta-analysis

abstract: Cricoid pressure (CP) is commonly applied during rapid sequence intubation and may be protective during induction of anaesthesia; however, CP application by untrained practitioners may not be performed optimally. The objective of this systematic review was to synthesize the evidence regarding effectiveness of technology-enhanced simulation training to improve efficacy of CP application. Electronic databases from inception through May 11, 2011 were searched. Eligible studies evaluated CP simulation training. Independent reviewers working in duplicate extracted study characteristics, validity, and outcomes data. Pooled effect size (ES) with 95% confidence intervals (CIs) were estimated from each study that compared technology-enhanced simulation with no intervention or with other methods of CP training using random-effects model. Twelve studies (772 trainees) evaluated CP training as an outcome. Nine studies reported information on baseline skill, with 23% of providers being able to achieve the target CP before training. In a meta-analysis of 10 studies (570 trainees), CP training resulted in a large favourable impact on skills among trainees compared with no intervention (pooled ES 1.18; 95% CI 0.85-1.51; P<0.0001). Four studies found evidence of skills retention for CP application after training, but for a limited time (<4 weeks). Comparative effectiveness research shows beneficial effects to force feedback training over training without feedback. Simulation training significantly improves the efficacy of CP application. Future studies might evaluate the clinical impact of training on CP application during rapid sequence intubation, and the comparative effectiveness of different training approaches.


title: State of health monitoring of Li-ion batteries using dynamic resistance mapping and regression

abstract: The increase in speed and accuracy of computation methods and the advancement in sensor technologies has shifted the focus towards health monitoring systems of vehicles. Li-ion batteries which are used in electric vehicles, tend to get damaged and pose potential safety threats when ill-used. An adaptive control system called battery management system is needed to ensure the safety and life optimization of batteries in electric vehicles. This system takes care of both diagnosis and prognosis, giving the user an idea of current status as well as remaining lifetime of the battery. A novel algorithm based on dynamic resistance mapping is used to develop a data driven computational model for estimating the state of health of battery. A model with a correlation co-efficient of 99.45% is developed for state of health estimation to find the health of the battery during run time.


title: OctNetFusion: Learning Depth Fusion from Data

abstract: In this paper, we present a learning based approach to depth fusion, i.e., dense 3D reconstruction from multiple depth images. The most common approach to depth fusion is based on averaging truncated signed distance functions, which was originally proposed by Curless and Levoy in 1996. While this method is simple and provides great results, it is not able to reconstruct (partially) occluded surfaces and requires a large number frames to filter out sensor noise and outliers. Motivated by the availability of large 3D model repositories and recent advances in deep learning, we present a novel 3D CNN architecture that learns to predict an implicit surface representation from the input depth maps. Our learning based method significantly outperforms the traditional volumetric fusion approach in terms of noise reduction and outlier suppression. By learning the structure of real world 3D objects and scenes, our approach is further able to reconstruct occluded regions and to fill in gaps in the reconstruction. We demonstrate that our learning based approach outperforms both vanilla TSDF fusion as well as TV-L1 fusion on the task of volumetric fusion. Further, we demonstrate state-of-the-art 3D shape completion results.


Convolutional Encoders for Neural Machine Translation


title: Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification

abstract: Abstract Previous studies have demonstrated that the use of integrated information from multi-modalities could significantly improve diagnosis of Alzheimer's Disease (AD). However, feature selection, which is one of the most important steps in classification, is typically performed separately for each modality, which ignores the potentially strong inter-modality relationship within each subject. Recent emergence of multi-task learning approach makes the joint feature selection from different modalities possible. However, joint feature selection may unfortunately overlook different yet complementary information conveyed by different modalities. We propose a novel multi-task feature selection method to preserve the complementary inter-modality information. Specifically, we treat feature selection from each modality as a separate task and further impose a constraint for preserving the inter-modality relationship, besides separately enforcing the sparseness of the selected features from each modality. After feature selection, a multi-kernel support vector machine (SVM) is further used to integrate the selected features from each modality for classification. Our method is evaluated using the baseline PET and MRI images of subjects obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our method achieves a good performance, with an accuracy of 94.37% and an area under the ROC curve (AUC) of 0.9724 for AD identification, and also an accuracy of 78.80% and an AUC of 0.8284 for mild cognitive impairment (MCI) identification. Moreover, the proposed method achieves an accuracy of 67.83% and an AUC of 0.6957 for separating between MCI converters and MCI non-converters (to AD). These performances demonstrate the superiority of the proposed method over the state-of-the-art classification methods.


title: A Stacked-FET Linear SOI CMOS Cellular Antenna Switch With an Extremely Low-Power Biasing Strategy

abstract: A stacked field-effect transistor (FET) linear cellular antenna switch adopting a transistor layout with odd-symmetrical drain-source metal wiring and an extremely low-power biasing strategy has been implemented in silicon-on-insulator CMOS technology. A multi-fingered switch-FET device with odd-symmetrical drain-source metal wiring is adopted herein to improve the insertion loss (IL) and isolation of the antenna switch by minimizing the product of the on-resistance and off-capacitance. To remove the spurious emission and digital switching noise problems from the antenna switch driver circuits, an extremely low-power biasing scheme driven by only positive bias voltage has been devised. The proposed antenna switch that employs the new biasing scheme shows almost the same power-handling capability and harmonic distortion as a conventional version based on a negative biasing scheme, while greatly reducing long start-up time and wasteful active current consumption in a stand-by mode of the conventional antenna switch driver circuits. The implemented single-pole four-throw antenna switch is perfectly capable of handling a high power signal up to +35 dBm with suitably low IL of less than 1 dB, and shows second- and third-order harmonic distortion of less than -45 dBm when a 1-GHz RF signal with a power of +35 dBm and a 2-GHz RF signal with a power of +33 dBm are applied. The proposed antenna switch consumes almost no static power.


title: The multilayer perceptron approach to lateral motion prediction of surrounding vehicles for autonomous vehicles

abstract: For safe and reliable autonomous driving systems, prediction of surrounding vehicles' future behavior and potential risks are critical. The state-of-the-art prediction algorithms tend to show limited performance on long-term predictions due to their deterministic nature. In this paper, a probabilistic lateral motion prediction algorithm is proposed based on multilayer perceptron (MLP) approach. The MLP model consists of two parts; target lane and trajectory models. In order to develop an intuitive and accurate prediction algorithm, a lane-based trajectory prediction model is introduced based on the fact that vehicles drive within a lane except for during lane changes. More specifically, a set of three representative trajectories with different levels of lane-change positions are generated for each target lane, and real-world traffic data is categorized by each trajectory for MLP training. These target lane and trajectory models enable the stochastic MLP modeling and training. The proposed MLP model outputs probabilities of how likely a vehicle will follow each trajectory and each lane for a given input of vehicle position history including current position. For training the MLP model, Next Generation Simulation traffic data are used. Simulation results show that the proposed algorithm detects lane-changes one to one and a half second earlier than existing methods and three seconds before lane crossing with about ninety percentages accuracy.


title: Detection of Double-Compression in JPEG Images for Applications in Steganography

abstract: This paper presents a method for the detection of double JPEG compression and a maximum-likelihood estimator of the primary quality factor. These methods are essential for construction of accurate targeted and blind steganalysis methods for JPEG images. The proposed methods use support vector machine classifiers with feature vectors formed by histograms of low-frequency discrete cosine transformation coefficients. The performance of the algorithms is compared to selected prior art.


title: Exploration and apprenticeship learning in reinforcement learning

abstract: We consider reinforcement learning in systems with unknown dynamics. Algorithms such as E3 (Kearns and Singh, 2002) learn near-optimal policies by using "exploration policies" to drive the system towards poorly modeled states, so as to encourage exploration. But this makes these algorithms impractical for many systems; for example, on an autonomous helicopter, overly aggressive exploration may well result in a crash. In this paper, we consider the apprenticeship learning setting in which a teacher demonstration of the task is available. We show that, given the initial demonstration, no explicit exploration is necessary, and we can attain near-optimal performance (compared to the teacher) simply by repeatedly executing "exploitation policies" that try to maximize rewards. In finite-state MDPs, our algorithm scales polynomially in the number of states; in continuous-state linear dynamical systems, it scales polynomially in the dimension of the state. These results are proved using a martingale construction over relative losses.


title: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

abstract: Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Manually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain. Specifically, we learn domain-invariant features by learning to counter an adversarial network, which attempts to classify the domain of the input data by observing the activations of the segmentation network. Furthermore, we propose a multi-connected domain discriminator for improved adversarial training. Our system is evaluated using two MR databases of subjects with traumatic brain injuries, acquired using different scanners and imaging protocols. Using our unsupervised approach, we obtain segmentation accuracies which are close to the upper bound of supervised domain adaptation.


title: Using mixture design and neural networks to build stock selection decision support systems

abstract: There are three disadvantages of weighted scoring stock selection models. First, they cannot identify the relations between weights of stock-picking concepts and performances of portfolios. Second, they cannot systematically discover the optimal combination for weights of concepts to optimize the performances. Third, they are unable to meet various investors' preferences. This study aimed to more efficiently construct weighted scoring stock selection models to overcome these disadvantages. Since the weights of stock-picking concepts in a weighted scoring stock selection model can be regarded as components in a mixture, we used the simplex-centroid mixture design to obtain the experimental sets of weights. These sets of weights are simulated with US stock market historical data to obtain their performances. Performance prediction models were built with the simulated performance data set and artificial neural networks. Furthermore, the optimization models to reflect investors' preferences were built up, and the performance prediction models were employed as the kernel of the optimization models, so that the optimal solutions can now be solved with optimization techniques. The empirical values of the performances of the optimal weighting combinations generated by the optimization models showed that they can meet various investors' preferences and outperform those of S&P's 500 not only during the training period but also during the testing period.


title: A client-side web application for interactive environmental simulation modeling

abstract: Recent developments in web technologies including evolution of web standards, improvements in browser performance, and the emergence of free and open-source software (FOSS) libraries are driving a general shift from server-side to client-side web applications where a greater share of the computational load is transferred to the browser. Modern client-side approaches allow for improved user interfaces that rival traditional desktop software, as well as the ability to perform simulations and visualizations within the browser. We demonstrate the use of client-side technologies to create an interactive web application for a simulation model of biochemical oxygen demand and dissolved oxygen in rivers called the Web-based Interactive River Model (WIRM). We discuss the benefits, limitations and potential uses of client-side web applications, and provide suggestions for future research using new and upcoming web technologies such as offline access and local data storage to create more advanced client-side web applications for environmental simulation modeling. A client-side web application of an environmental simulation model is presented.Client-side computation and visualization support high interactivity and usability.URL-based model access and a commenting system promote user collaboration.Web-based simulation models are more accessible to non-technical users.


title: Neural Multi-Step Reasoning for Question Answering on Semi-Structured Tables

abstract: We explore neural network models for answering multi-step reasoning questions that operate on semi-structured tables. Challenges arise from deep logical compositionality and domain openness. Our approach is weakly supervised, trained on question-answer-table triples. It generates human readable logical forms from natural language questions, which are then ranked based on word and character convolutional neural networks. A model ensemble achieved at the moment of publication state-of-the-art score on the WikiTableQuestions dataset.


title: Individual teeth segmentation in CBCT and MSCT dental images using watershed

abstract: Teeth segmentation is an important step in human identification and Content Based Image Retrieval (CBIR) systems. This paper proposes a new approach for teeth segmentation using morphological operations and watershed algorithm. In Cone Beam Computer Tomography (CBCT) and Multi Slice Computer Tomography (MSCT) each tooth is an elliptic shape region that cannot be separated only by considering their pixels' intensity values. For segmenting a tooth from the image, some enhancement is necessary. We use morphological operators such as image filling and image opening to enhance the image. In the proposed algorithm, a Maximum Intensity Projection (MIP) mask is used to separate teeth regions from black and bony areas. Then each tooth is separated using the watershed algorithm. Anatomical constraints are used to overcome the over segmentation problem in watershed method. The results show a high accuracy for the proposed algorithm in segmenting teeth. Proposed method decreases time consuming by considering only one image of CBCT and MSCT for segmenting teeth instead of using all slices.


title: Security Metrics for Object-Oriented Class Designs

abstract: Measuring quality attributes of object-oriented designs (e.g. maintainability and performance) has been covered by a number of studies. However, these studies have not considered security as much as other quality attributes. Also, most security studies focus at the level of individual program statements. This approach makes it hard and expensive to discover and fix vulnerabilities caused by design errors. In this work, we focus on the security design of an object-oriented application and define a number of security metrics. These metrics allow designers to discover and fix security vulnerabilities at an early stage, and help compare the security of various alternative designs. In particular, we propose seven security metrics to measure Data Encapsulation (accessibility) and Cohesion (interactions) of a given object-oriented class from the point of view of potential information flow.


title: Machine Learning: A Probabilistic Perspective

abstract: Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.


title: The dynamic impedance modeling and simulation analysis of lithium battery cell in micro power grid

abstract: The lithium battery cell model plays a key role in research of high precision micro grid energy storage system. Taking lithium battery cell as the research object, combined with the equivalent circuit method, second-order RC equivalent circuit model which is based on dynamic impedance is established. According to the electric relationship model, SOC is viewed as state variables, OCV-SOC curve is obtained by the HPPC experiment, battery model parameters is identified by using exponential curve fitting based least squares, then get the function relation between parameter values and SOC. Finally, MATLAB/Simulink simulation of the model is used to verify the accuracy of model. Results show that second-order RC equivalent circuit model is of high precision, dynamic impedance can track the change of SOC and accurately reflect the external characteristic of the lithium battery energy storage system, these results are of great significance in micro power grid simulation and research.


title: Nonparametric Technique Based High-Speed Road Surface Detection

abstract: It has been well recognized that detecting road surface in a realistic environment is a challenging problem that is also computationally intensive. Existing road surface detection methods attempt to fit the road surface into rigid models (e.g., planar, clothoid, or B-Spline), thereby restricting to road surfaces that match specific models. In addition, the curve-fitting strategies employed in such techniques incur high computational complexity, making them unsuitable for in-vehicle deployments. In this paper, we propose an efficient nonparametric road surface detection algorithm that exploits the depth cue. The proposed method relies on four intrinsic road scene attributes observed under stereo geometry and has been shown to reliably detect both planar and nonplanar road surfaces efficiently. Extensive evaluations are performed on three widely used benchmarks (i.e., enpeda, KITTI, and Daimler), encompassing many complex road scenarios. The experimental results show that the proposed algorithm significantly outperforms the well-known techniques both in terms of detection accuracy and runtime performance.


title: Quantum circuits with mixed states

abstract: We define the model of quantum circuits with density matrices, where non-unitary gates are allowed. Measurements in the middle of the computation, noise and decoherence are implemented in a natural way in this model, which is shown to be equivalent in computational power to standard quantum circuits. ::: The main result in this paper is a solution for the subroutine problem: The general function that a quantum circuit outputs is a probabilistic function, but using pure state language, such a function can not be used as a black box in other computations. We give a natural definition of using general subroutines, and analyze their computational power. ::: We suggest convenient metrics for quantum computing with mixed states. For density matrices we analyze the so called ``trace metric'', and using this metric, we define and discuss the ``diamond metric'' on superoperators. These metrics enable a formal discussion of errors in the computation. ::: Using a ``causality'' lemma for density matrices, we also prove a simple lower bound for probabilistic functions.


title: Dynamic Word Embeddings for Evolving Semantic Discovery

abstract: Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution. By studying word evolution, we can infer social trends and language constructs over different periods of human history. However, traditional techniques such as word representation learning do not adequately capture the evolving language structure and vocabulary. In this paper, we develop a dynamic statistical model to learn time-aware word vector representation. We propose a model that simultaneously learns time-aware embeddings and solves the resulting alignment problem. This model is trained on a crawled NYTimes dataset. Additionally, we develop multiple intuitive evaluation strategies of temporal word embeddings. Our qualitative and quantitative tests indicate that our method not only reliably captures this evolution over time, but also consistently outperforms state-of-the-art temporal embedding approaches on both semantic accuracy and alignment quality.


title: Compact Multifrequency Slot Antenna Design Incorporating Embedded Arc-Strip

abstract: A compact coplanar waveguide (CPW)-fed multifrequency circular slot antenna design loaded with arc-shaped metallic strips is investigated and verified in this letter. The proposed design implemented on a 0.8-mm FR-4 substrate shows that the arc-shaped metallic strips yield the multiple resonant modes for multiband vision. Unlike other multifrequency antennas whose sizes are determined by the lower resonant frequency, the compact size of the proposed slot is determined by the upper resonant frequency. Other operating frequencies are obtained by inserting arc-shaped metallic strips without increasing the overall antenna area. The prototype for different applications examined by modifying the angles of the arc-shaped metallic strips reveals that the input impedance bandwidths of the operating frequencies range from 5.0% to 64.7%. Moreover, these operating resonant bands exhibit the broadside radiation patterns with efficiency better than 45%.


title: Spectral fingerprints of large-scale neuronal interactions

abstract: Cognition results from large-scale interactions among widely distributed brain regions. Siegel and colleagues review studies showing that these interactions are reflected by correlated neuronal oscillations. They propose that correlated oscillations in large-scale cortical networks may be 'fingerprints' of canonical neuronal computations underlying cognitive processes.


title: Discussion trend of domestic large hoisting equipments for deep shafts of thousands meters

abstract: The paper described the current status of domestic large hoisting equipments for deep shafts of thousands meters,and analyzed the factors affecting the development of the large hoisting equipments for deep shafts of thousands meters from the policy at home and abroad,safety regulation,volume coefficient of hoisting container,as well as safety coefficient,winding layer number and nominal tensile strength of the wire rope.It also indicated the development trend of domestic large hoisting equipments for deep shafts of thousands meters.


title: Circulating tumour DNA sequence analysis as an alternative to multiple myeloma bone marrow aspirates

abstract: The requirement for bone-marrow aspirates for genomic profiling of multiple myeloma poses an obstacle to enrolment and retention of patients in clinical trials. We evaluated whether circulating cell-free DNA (cfDNA) analysis is comparable to molecular profiling of myeloma using bone-marrow tumour cells. We report here a hybrid-capture-based Liquid Biopsy Sequencing (LB-Seq) method used to sequence all protein-coding exons of KRAS, NRAS, BRAF, EGFR and PIK3CA in 64 cfDNA specimens from 53 myeloma patients to >20,000 × median coverage. This method includes a variant filtering algorithm that enables detection of tumour-derived fragments present in cfDNA at allele frequencies as low as 0.25% (median 3.2%, range 0.25-46%). Using LB-Seq analysis of 48 cfDNA specimens with matched bone-marrow data, we detect 49/51 likely somatic mutations, with subclonal hierarchies reflecting tumour profiling (96% concordance), and four additional mutations likely missed by bone-marrow testing (>98% specificity). Overall, LB-Seq is a high fidelity adjunct to genetic profiling of bone-marrow in multiple myeloma.


title: A 15-mV Inductor-Less Start-up Converter Using a Piezoelectric Transformer for Energy Harvesting Applications

abstract: This paper presents an inductor-less start-up converter for a sub-100-mV energy harvester based on an Armstrong oscillator topology using a piezoelectric transformer and a normally ON MOSFET. Two models of the converter have been detailed and validated experimentally for the start-up phase and steady-state operation to, respectively, determine the minimum start-up input voltage and the voltage gain. The models have been validated experimentally in a setup associating the converter and a thermoelectric generator. Based on a Rosen-type piezoelectric transformer and off-the-shelf components, the proposed start-up topology begins to oscillate at 15 mV and achieves a 1-V output voltage at only 43 mV. Compared to the literature, the topology needs no inductive component and achieves self-starting operation with a smaller input voltage.


title: Democrats, republicans and starbucks afficionados: user classification in twitter

abstract: More and more technologies are taking advantage of the explosion of social media (Web search, content recommendation services, marketing, ad targeting, etc.). This paper focuses on the problem of automatically constructing user profiles, which can significantly benefit such technologies. We describe a general and robust machine learning framework for large-scale classification of social media users according to dimensions of interest. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business.