title: Network intrusion detection

abstract: Intrusion detection is a new, retrofit approach for providing a sense of security in existing computers and data networks, while allowing them to operate in their current "open" mode. The goal of intrusion detection is to identify unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators. The intrusion detection problem is becoming a challenging task due to the proliferation of heterogeneous computer networks since the increased connectivity of computer systems gives greater access to outsiders and makes it easier for intruders to avoid identification. Intrusion detection systems (IDSs) are based on the beliefs that an intruder's behavior will be noticeably different from that of a legitimate user and that many unauthorized actions are detectable. Typically, IDSs employ statistical anomaly and rulebased misuse models in order to detect intrusions. A number of prototype IDSs have been developed at several institutions, and some of them have also been deployed on an experimental basis in operational systems. In the present paper, several host-based and network-based IDSs are surveyed, and the characteristics of the corresponding systems are identified. The host-based systems employ the host operating system's audit trails as the main source of input to detect intrusive activity, while most of the network-based IDSs build their detection mechanism on monitored network traffic, and some employ host audit trails as well. An outline of a statistical anomaly detection algorithm employed in a typical IDS is also included. >


title: Tracking Benchmark Databases for Video-Based Sign Language Recognition

abstract: A survey of video databases that can be used within a continuous sign language recognition scenario to measure the performance of head and hand tracking algorithms either w.r.t. a tracking error rate or w.r.t. a word error rate criterion is presented in this work. ::: ::: Robust tracking algorithms are required as the signing hand frequently moves in front of the face, may temporarily disappear, or cross the other hand. ::: ::: Only few studies consider the recognition of continuous sign language, and usually special devices such as colored gloves or blue-boxing environments are used to accurately track the regions-of-interest in sign language processing. ::: ::: Ground-truth labels for hand and head positions have been annotated for more than 30k frames in several publicly available video databases of different degrees of difficulty, and preliminary tracking results are presented.


Attention Solves Your TSP, Approximately


Web Content Extraction Through Machine Learning


title: Instagram at the museum: communicating the museum experience through social photo sharing

abstract: The everyday use of smartphones with high quality built-in cameras has lead to an increase in museum visitors' use of these devices to document and share their museum experiences. In this paper, we investigate how one particular photo sharing application, Instagram, is used to communicate visitors' experiences while visiting a museum of natural history. Based on an analysis of 222 instagrams created in the museum, as well as 14 interviews with the visitors who created them, we unpack the compositional resources and concerns contributing to the creation of instagrams in this particular context. By re-categorizing and re-configuring the museum environment, instagrammers work to construct their own narratives from their visits. These findings are then used to discuss what emerging multimedia practices imply for the visitors' engagement with and documentation of museum exhibits. Drawing upon these practices, we discuss the connection between online social media dialogue and the museum site.


title: Scanner hunter: understanding HTTP scanning traffic

abstract: This paper focuses on detecting and studying HTTP scanners, which are malicious entities that explore a website selectively for "opportunities" that can potentially be used for subsequent intrusion attempts. Interestingly, there is practically no prior work on the detection of these entities, which are different from web crawlers or machines performing network-level reconnaissance activities such as port scanning. Detecting HTTP scanners is challenging as they are stealthy and often only probe a few key places on a website, so finding them is a needle-in-the-haystack problem. At the same time, they pose serious risk because they perform the first, exploratory step to provide the seed information that may allow hackers to compromise a website. Our work makes two main contributions. First, we propose Scanner Hunter, arguably the first method to detect HTTP scanners efficiently. The novelty and success of the method lies in the use of community structure, in an appropriately constructed bipartite graph, in order to expose groups of HTTP scanners. The rationale is that the aggregated behavior makes identifying groups of scanners easier than attempting to profile and label IP addresses individually. Scanner Hunter achieves an impressive 96.5% detection precision, which is roughly twice as high as the precision of the Machine Learning-based methods that we use as reference. Second, we provide an extensive study of HTTP scanners in an effort to understand: (a) their spatial and temporal properties, (b) the techniques and tools used by the scanners, and (c) the types of resources they are looking for, which can provides hints as to what the subsequent penetration attempt may target. We use six months worth of web traffic logs collected in 2012 from a University campus, the websites hosted by which received over 1.9 billion requests from 12.8 million IPs. We found that the number of HTTP scanners is non-trivial with roughly 4,000 IPs engaging in this type of activity per week. Our work will hopefully raise the awareness of the community regarding this problem while at the same time provide a promising detection technique that can provide the basis for mitigating the risk posed by HTTP scanners.


title: Effective Audience Extension in Online Advertising

abstract: In digital advertising, advertisers want to reach the right audience over media channels such as display, mobile, video, or social at the appropriate cost. The right audience for an advertiser consists of existing customers as well as valuable prospects, those that can potentially be turned into future customers. Identifying valuable prospects is called the audience extension problem because advertisers find new customers by extending the desirable criteria for their starting point, which is their existing audience or customers. The complexity of the audience extension problem stems from the difficulty of defining desirable criteria objectively, the number of desirable criteria (such as similarity, diversity, performance) to simultaneously satisfy, and the expected runtime (a few minutes) to find a solution over billions of cookie-based users. In this paper, we formally define the audience extension problem, propose an algorithm that extends a given audience set efficiently under multiple desirable criteria, and experimentally validate its performance. Instead of iterating over individual users, the algorithm takes in Boolean rules that define the seed audience and returns a new set of Boolean rules that corresponds to the extended audience that satisfy the multiple criteria.


title: Use of Communal Nests by Male and Female Trachypus petiolatus (Hymenoptera: Sphecidae)

abstract: Abstract The nesting behavior of Trachypus petiolatus is described from central Panama. Females nested within aggregations in bare soil; they re-used nests from a prior generation, and provisioned their nests with small bees, mainly Apidae and Halictidae. These nests had a communal social organization among resident females, and also included one or more males. At least some males left the nest in the morning on a daily basis, and then returned later in the day. Based on limited nest excavations, we did not observe any parasitized cells, which may be associated with communal nesting.


title: Effective Phrase Prediction

abstract: Autocompletion is a widely deployed facility in systems that require user input. Having the system complete a partially typed "word" can save user time and effort. ::: ::: In this paper, we study the problem of autocompletion not just at the level of a single "word", but at the level of a multi-word "phrase". There are two main challenges: one is that the number of phrases (both the number possible and the number actually observed in a corpus) is combinatorially larger than the number of words; the second is that a "phrase", unlike a "word", does not have a well-defined boundary, so that the autocompletion system has to decide not just what to predict, but also how far. ::: ::: We introduce a FussyTree structure to address the first challenge and the concept of a significant phrase to address the second. We develop a probabilistically driven multiple completion choice model, and exploit features such as frequency distributions to improve the quality of our suffix completions. We experimentally demonstrate the practicability and value of our technique for an email composition application and show that we can save approximately a fifth of the keystrokes typed.


title: miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades

abstract: microRNAs (miRNAs) are a large class of small non-coding RNAs which post-transcriptionally regulate the expression of a large fraction of all animal genes and are important in a wide range of biological processes. Recent advances in high-throughput sequencing allow miRNA detection at unprecedented sensitivity, but the computational task of accurately identifying the miRNAs in the background of sequenced RNAs remains challenging. For this purpose, we have designed miRDeep2, a substantially improved algorithm which identifies canonical and non-canonical miRNAs such as those derived from transposable elements and informs on high-confidence candidates that are detected in multiple independent samples. Analyzing data from seven animal species representing the major animal clades, miRDeep2 identified miRNAs with an accuracy of 98.6–99.9% and reported hundreds of novel miRNAs. To test the accuracy of miRDeep2, we knocked down the miRNA biogenesis pathway in a human cell line and sequenced small RNAs before and after. The vast majority of the >100 novel miRNAs expressed in this cell line were indeed specifically downregulated, validating most miRDeep2 predictions. Last, a new miRNA expression profiling routine, low time and memory usage and user-friendly interactive graphic output can make miRDeep2 useful to a wide range of researchers.


title: A Comparative Study on K Means and PAM Algorithm using Physical Characters of Different Varieties of

abstract: is the most important and popular technique for finding pattern and relationships in databases. In this paper a comparative study has been done on the clustering techniques like k-means and k-mediod (PAM) with difference distance measures to classify the different varieties of mango based on physical characters of fruit. As the purity of result of a clustering algorithm depend upon the distance measure technique used in that algorithm we have validate the result using different distance measure also. Classification of agricultural data is still remains a challenge due to its high dimension and noise. This type of study may be helpful for the agricultural research as well as for the field of science and technology.


Spiking Neural Networks Encoding & Decoding Algorithms for Time Series Estimation & System Identification


title: MadaBoost: A Modification of AdaBoost

abstract: We propse a new boosting algorithm that mends some of the problems that have been detected in the so far most successful boosting algorithm, AdaBoost due to Freund and Schapire [FS97]. These problems are: (1) AdaBoost cannot be used in the boosting by filtering framework, and (2) AdaBoost does not seem to be noise resistant. In order to solve them, we propose a new boosting algorithm MadaBoost by modifying the weighting system of AdaBoost. We prove that one version of MadaBoost is in fact a boosting algorithm, and we show how our algorithm can be used in detail. We then prove that our new boosting algorithm can be casted in the statistical query learning model [Kea93] and thus, it is robust to random classification noise [AL88].


title: Design Procedure of Dual-Stator Spoke-Array Vernier Permanent-Magnet Machines

abstract: The dual-stator spoke-array vernier permanent-magnet (DSSA VPM) machines proposed in the previous papers have been proven to be with high torque density and high power factor. However, the design procedure on the DSSA VPM machines has not been well established, and there is little design experience to be followed, which makes the DSSA VPM machine design quite difficult. This paper presents the detailed DSSA VPM machine design procedure including decision of design parameter initial values, analytical sizing equation, geometric size relationship, and so on. In order to get reasonable design parameter initial values which can reduce the number of design iteration loop, the influence of the key parameters, such as rotor/stator pole combination, slot opening, magnet thickness, etc., on the performances is analyzed based on the finite-element algorithm (FEA) in this paper, and the analysis results can be regarded as design experience during the selection process of the initial values. After that, the analytical sizing equation and geometric relationship formulas are derived and can be used to obtain and optimize the size data of the DSSA VPM machines with little time consumption. The combination of the analytical and FEA methods makes the design procedure time-effective and reliable. Finally, the design procedure is validated by experiments on a DSSA VPM prototype with 2000 $\hbox{N}\cdot\hbox{m}$ .


title: Software clone detection: A systematic review

abstract: Abstract Context Reusing software by means of copy and paste is a frequent activity in software development. The duplicated code is known as a software clone and the activity is known as code cloning . Software clones may lead to bug propagation and serious maintenance problems. Objective This study reports an extensive systematic literature review of software clones in general and software clone detection in particular. Method We used the standard systematic literature review method based on a comprehensive set of 213 articles from a total of 2039 articles published in 11 leading journals and 37 premier conferences and workshops. Results Existing literature about software clones is classified broadly into different categories. The importance of semantic clone detection and model based clone detection led to different classifications. Empirical evaluation of clone detection tools/techniques is presented. Clone management, its benefits and cross cutting nature is reported. Number of studies pertaining to nine different types of clones is reported. Thirteen intermediate representations and 24 match detection techniques are reported. Conclusion We call for an increased awareness of the potential benefits of software clone management, and identify the need to develop semantic and model clone detection techniques. Recommendations are given for future research.


title: Distributed Privacy-Preserving Collaborative Intrusion Detection Systems for VANETs

abstract: Vehicular ad hoc network (VANET) is an enabling technology in modern transportation systems for providing safety and valuable information, and yet vulnerable to a number of attacks from passive eavesdropping to active interfering. Intrusion detection systems (IDSs) are important devices that can mitigate the threats by detecting malicious behaviors. Furthermore, the collaborations among vehicles in VANETs can improve the detection accuracy by communicating their experiences between nodes. To this end, distributed machine learning is a suitable framework for the design of scalable and implementable collaborative detection algorithms over VANETs. One fundamental barrier to collaborative learning is the privacy concern as nodes exchange data among them. A malicious node can obtain sensitive information of other nodes by inferring from the observed data. In this paper, we propose a privacy-preserving machine-learning-based collaborative IDS (PML-CIDS) for VANETs. The proposed algorithm employs the alternating direction method of multipliers to a class of empirical risk minimization problems and trains a classifier to detect the intrusions in the VANETs. We use the differential privacy to capture the privacy notation of the PML-CIDS and propose a method of dual-variable perturbation to provide dynamic differential privacy. We analyze theoretical performance and characterize the fundamental tradeoff between the security and privacy of the PML-CIDS. We also conduct numerical experiments using the network security laboratory-knowledge discovery and data mining (NSL-KDD) dataset to corroborate the results on the detection accuracy, security-privacy tradeoffs, and design.


Towards Automatic Generation of Product Reviews from Aspect-Sentiment Scores


title: Correcting Sensor Drift and Intermittency Faults With Data Fusion and Automated Learning

abstract: Many fault detection algorithms deal with fault signatures that manifest themselves as step changes. While detection of these step changes can be difficult due to noise and other complicating factors, detecting slowly developing faults is usually even more complicated. Tradeoffs between early detection and false positive avoidance are more difficult to establish. Often times, slow drift faults go completely undetected because the monitoring systems assume that they are ordinary system changes and some monitoring schemes may adapt to the changes. Where redundant sensors are used, a drifting sensor may cause the logic to latch on to the ldquobadrdquo sensor. Another problem may be intermittent sensors faults where the detection logic is too sluggish to recognize a problem before the sensor has returned to seemingly normal behavior. To address these classes of problems, we introduce here a set of algorithms that learns to avoid the bad sensor, thus indirectly recognizing the aberrant sensor. We combine advanced sensor validation techniques with learning. The sensor validation is inspired by fuzzy principles. The parameters of this algorithm are learned using competing optimization approaches. We compare the results from a particle swarm optimization approach with those obtained from genetic algorithms. Results are shown for an application in the transportation industry.


title: Mechanical constraints as computational constraints in tabletop tangible interfaces

abstract: This paper presents a new type of human-computer interface called Pico (Physical Intervention in Computational Optimization) based on mechanical constraints that combines some of the tactile feedback and affordances of mechanical systems with the abstract computational power of modern computers. The interface is based on a tabletop interaction surface that can sense and move small objects on top of it. The positions of these physical objects represent and control parameters inside a software application, such as a system for optimizing the configuration of radio towers in a cellular telephone network. The computer autonomously attempts to optimize the network, moving the objects on the table as it changes their corresponding parameters in software. As these objects move, the user can constrain their motion with his or her hands, or many other kinds of physical objects. The interface provides ample opportunities for improvisation by allowing the user to employ a rich variety of everyday physical objects as mechanical constraints. This approach leverages the user's mechanical intuition for how objects respond to physical forces. As well, it allows the user to balance the numerical optimization performed by the computer with other goals that are difficult to quantify. Subjects in an evaluation were more effective at solving a complex spatial layout problem using this system than with either of two alternative interfaces that did not feature actuation.


title: Cartesian stiffness evaluation of a novel 2 DoF parallel wrist under redundant and antagonistic actuation

abstract: In this paper, we present an experimental evaluation of the Cartesian stiffness of a novel parallel wrist under redundant and antagonistic actuation. The mechanism in consideration is Omni-Wrist V (OW5), a two degrees-of-freedom (DoF) parallel mechanism redundantly actuated by three subchains. We first give a brief review of its kineto-statics and derive its reduced Cartesian stiffness model. To illustrate the stiffness enhancement of OW5 under redundant and antagonistic actuation, its Cartesian stiffness is measured and evaluated under four control schemes: the non-redundant control, the minimum 2-norm torque control without or with redundant encoder, and the antagonistic actuation control. Measurement data are represented using stiffness matrices and stiffness ellipses. Our study offers a quick quantitative evaluation of stiffness enhancement of OW5 under redundant and antagonistic actuation.


title: Comparison of high-speed switched reluctance machines with conventional and toroidal windings

abstract: This paper presents designs of 50,000 rpm 6/4 switched reluctance motors (SRM's) with a focus of the comparative study on conventional and toroidal windings. There are four different machines compared in this paper, while the first is a conventional SRM, and the other three are toroidal winding machines. The first toroidal SRM (TSRM1) employs the conventional asymmetric convert and the same switching sequence as conventional SRM (CSRM). Therefore, an equivalent magnetic performance is observed. The second toroidal SRM (TSRM2) introduces a 12-switch converter topology. With a proper coil connection and switching sequence, all the coils are active and contribute to the flux and torque generation at the same time. The analysis shows that for the same amount of copper losses, TSRM2 yields a 50% higher output torque and power at rated speed than CSRM, while TSRM1 only generates half the torque of CSRM. The third toroidal SRM is a resized TSRM2, which is presented with the same envelope dimension of CSRM (same volumetric comparison). The comparison shows it's competitive to CSRM, especially as toroidal-winding can achieve higher filling factor during the manufacture process of winding.


title: Shaft Friction from Instrumented Displacement Piles in an Uncemented Calcareous Sand

abstract: AbstractThe behavior of displacement piles in uncemented calcareous sand is investigated using field piles instrumented with a sensor that simultaneously records the radial stress and shear stress at specific locations on the pile shafts. These tests are interpreted with the assistance of data from adjacent self-boring pressuremeter tests and from monotonic and cyclic direct shear interface tests performed on reconstituted samples. The existence of extremely low radial stresses on the pile shafts is verified. Although dilation during shear is seen to compensate for such low radial stresses, short-term shaft capacities are much lower than capacities of equivalent piles in siliceous sands. The development of a bonded or welded crust to the pile shaft was seen to be the primary contributor to the setup observed at the test site; this crust forced failure to take place at a sand-sand rather than a sand-steel interface and also gave rise to higher levels of dilation during monotonic loading. The welded sand cr...


title: A low cost smart irrigation system using MQTT protocol

abstract: In India agriculture plays a very important role. The Indian economy is also greatly affected by agricultural, as about 50 percent of total population is directly or indirectly depend on the agricultural related activities. A farmer has to go to the farm to check the water level in the field and to turn on and turn off the water pump, sometimes even in the middle of the night. This problem can be overcome by improving old methods of farming. A new system can be developed or designed which transform the old traditional farming into the smart farming. This paper tries to design a simple water pump controller by using a soil moisture sensor and Esp8266 NodeMCU-12E. A Message Queue Telemetry Transport protocol is used for transmitting and receiving sensor information. Depending on a status of soil moisture content NodeMCU-12E controls a water pump action and displays the soil moisture sensor data and water pump status on a web page or mobile application. In this way, a secure, flexible, trust-able and economical system is developed to solve above mentioned agricultural irrigation problem.


title: Safety issues in human-robot interactions

abstract: Safety is an important consideration in human-robot interactions (HRI). Robots can perform powerful movements that can cause hazards to humans surrounding them. To prevent accidents, it is important to identify sources of potential harm, to determine which of the persons in the robot's vicinity may be in greatest peril and to assess the type of injuries the robot may cause to this person. This survey starts with a review of the safety issues in industrial settings, where robots manipulate dangerous tools and move with extreme rapidity and force. We then move to covering issues related to the growing numbers of autonomous mobile robots that operate in crowded (human-inhabited) environments. We discuss the potential benefits of fully autonomous cars on safety on roads and for pedestrians. Lastly, we cover safety issues related to assistive robots.


title: Development of Quantum Device Simulator, NEMO-VN2

abstract: We develop some versions of quantum devices simulators such as NEMO-VN, NEMO-VN1 and NEMO-VN2. The quantum device simulator – NEMO-VN2 focuses on carbon nanotube FET (CNTFET). CNTFETs have been studied in recent years as potential alternatives to CMOS devices because of their compelling properties. Studies of phonon scattering in CNTs and its influence in CNTFET have focused on metallic tubes or on long semiconducting tubes. Phonon scattering in short channel CNTFETs, which is important for nanoelectronic applications, remains unexplored. In this work the non-equilibrium Green function (NEGF) is used to perform a comprehensive study of CNT transistors. The program has been written by using graphic user interface (GUI) of Matlab. We find that the effect of scattering on current-voltage characteristics of CNTFET is significant. The degradation of drain current due to scattering has been observed. Some typical simulation results have been presented for illustration.


title: A daily behavior enabled hidden Markov model for human behavior understanding

abstract: This paper presents a Hierarchical Context Hidden Markov Model (HC-HMM) for behavior understanding from video streams in a nursing center. The proposed HC-HMM infers elderly behaviors through three contexts which are spatial, activities, and temporal context. By considering the hierarchical architecture, HC-HMM builds three modules composing the three components, reasoning in the primary and the secondary relationship. The spatial contexts are defined from the spatial structure, so that it is placed as the primary inference contexts. The temporal duration is associated to elderly activities, so activities are placed in the following of spatial contexts and the temporal duration is placed after activities. Between the spatial context reasoning and behavior reasoning of activities, a modified duration HMM is applied to extract activities. According to this design, human behaviors different in spatial contexts would be distinguished in first module. The behaviors different in activities would be determined in second module. The third module is to recognize behaviors involving different temporal duration. By this design, an abnormal signaling process corresponding to different situations is also placed for application. The developed approach has been applied for understanding of elder behaviors in a nursing center. Results have indicated the promise of the approach which can accurately interpret 85% of the elderly behaviors. For abnormal detection, the approach was found to have 90% accuracy, with 0% false alarm.


title: Analysis of motion blur with a flutter shutter camera for non-linear motion

abstract: Motion blurs confound many computer vision problems. The fluttered shutter (FS) camera [1] tackles the motion deblurring problem by emulating invertible broadband blur kernels. However, existing FS methods assume known constant velocity motions, e.g., via user specifications. In this paper, we extend the FS technique to general 1D motions and develop an automatic motion-from-blur framework by analyzing the image statistics under the FS. ::: ::: We first introduce a fluttered-shutter point-spread-function (FS-PSF) to uniformly model the blur kernel under general motions. We show that many commonly used motions have closed-form FS-PSFs. To recover the FS-PSF from the blurred image, we present a new method by analyzing image power spectrum statistics. We show that the Modulation Transfer Function of the 1D FS-PSF is statistically correlated to the blurred image power spectrum along the motion direction. We then recover the FS-PSF by finding the motion parameters that maximize the correlation. We demonstrate our techniques on a variety of motions including constant velocity, constant acceleration, and harmonic rotation. Experimental results show that our method can automatically and accurately recover the motion from the blurs captured under the fluttered shutter.


title: Global Contrast based Salient Region Detection

abstract: Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.


title: A Survey of Interactive Remote Rendering Systems

abstract: Remote rendering means rendering 3D graphics on a computing device and displaying the results on another computing device connected through a network. The concept was originally developed for sharing computing resources remotely. It has been receiving increasing attention from researchers in both academia and industry in recent years due to the proliferation of cloud computing and mobile devices. In this article, we survey the interactive remote rendering systems proposed in the literature, analyze how to improve the state of the art, and summarize the related technologies. The readers of this article will understand the history of remote rendering systems and obtain some inspirations of the future research directions in this area.


title: A Declarative Optimization Engine for Resource Provisioning of Scientific Workflows in IaaS Clouds

abstract: Resource provisioning for scientific workflows in Infrastructure-as-a-service (IaaS) clouds is an important and complicated problem for budget and performance optimizations of workflows. Scientists are facing the complexities resulting from severe cloud performance dynamics and various user requirements on performance and cost. To address those complexity issues, we propose a declarative optimization engine named Deco for resource provisioning of scientific workflows in IaaS clouds. Deco allows users to specify their workflow optimization goals and constraints of specific problems with an extended declarative language. We propose a novel probabilistic optimization approach for evaluating the declarative optimization goals and constraints in dynamic clouds. To accelerate the solution finding, Deco leverages the available power of GPUs to find the solution in a fast and timely manner. We evaluate Deco with several common provisioning problems. We integrate Deco into a popular workflow management system (Pegasus) and show that Deco can achieve more effective performance/cost optimizations than the state-of-the-art approaches.


title: Cyber security in cooperative intelligent transportation systems

abstract: Use of connected vehicles, enabled by the development of information and communication technologies, drastically changed users' experience. Those vehicles are more like a cyber controlled vehicle than a user controlled. With that comprehension and fast growing production of connected vehicles, vehicles are at full exposure to the unwanted malicious attacks. Also, possible implementation scenarios are presented for Cooperative Intelligent Transportation area, where communication between vehicles and infrastructure is essential. Through this research paper, cyber security measurements for lowering chances of unwanted malicious attacks in cooperative intelligent transportation environment are presented.


METIS System Concept : The Shape of 5 G to Come


title: From virtual to reality, how to prototype, test and evaluate new ADAS: Application to automatic car parking

abstract: Over the past decade, a lot of researches have been done on the development of advanced driver assistance systems (ADAS). Most of these ADAS are now active and need to be tested and evaluated before large deployment. In these ADAS, the prototyping and the implementation of the control stages are risky stages and not so easy to carry out. Indeed, the prototyping and the test of such reactive algorithms need heavy hardware and software supports (dedicated vehicle, actuators, hardware architecture, software architecture, sensors). To achieve such active devices, additional developments and implementation of numerous expensive embedded devices are required. Therefore, in order to reduce both time and risk, in early design stage, it becomes necessary to have a very realistic simulation environment dedicated to the development and to the evaluation of these ADAS. For such virtual platform, it is mandatory to provide physics-driven road environments, virtual embedded sensors, and physics-based vehicle models. In this publication, we present a dedicated couple of platforms with their efficient interconnection for the prototyping of such ADAS. Initially, the SiVIC simulation platform has been developed to generate the virtual world (environments, sensors, actuators, vehicles). In order to improve the real time prototyping capabilities of SiVIC, an efficient interconnection of this first platform has been done with RTMaps platform. This second one is mainly dedicated to the multi-sensors data processing (data management, fusion, flow recording and replaying). In this paper we will show the interest of such bi-directionnal interconnected platforms to prototype complex and real time embedded ADAS. This interconnection can be done as well on an only one computeur than on a distributed and distant computers architecture. The relevance of this approach will be illustrated with an automatic parking application.


Tracking the Evolution of Customer Purchase Behavior Segmentation via a Fragmentation-Coagulation Process


title: A data preprocessing framework for students' outcome prediction by data mining techniques

abstract: Data mining applied on educational data aims to find useful patterns in large volumes of data in order to transform and optimize educational paths. It involves many steps. This paper presents a case study for a data preprocessing framework for students' outcome prediction using data collected by Moodle system.


title: Biomedical Magnesium Alloys: A Review of Material Properties, Surface Modifications and Potential as a Biodegradable Orthopaedic Implant

abstract: Magnesium and magnesium based alloys are lightweight metallic materials that are extremely biocompatible and have similar mechanical properties to natural bone. These materials have the potential to function as an osteoconductive and biodegradable substitute in load bearing applications in the field of hard tissue engineering. However, the effects of corrosion and degradation in the physiological environment of the body has prevented their wide spread application to date. The aim of this review is to examine the properties, chemical stability, degradation in situ and methods of improving the corrosion resistance of magnesium and its alloys for potential application in the orthopaedic field. To be an effective implant, the surface and sub-surface properties of the material needs to be carefully selected so that the degradation kinetics of the implant can be efficiently controlled. Several surface modification techniques are presented and their effectiveness in reducing the corrosion rate and methods of controlling the degradation period are discussed. Ideally, balancing the gradual loss of material and mechanical strength during degradation, with the increasing strength and stability of the newly forming bone tissue is the ultimate goal. If this goal can be achieved, then orthopaedic implants manufactured from magnesium based alloys have the potential to deliver successful clinical outcomes without the need for revision surgery.


ACOUSTIC SCENE CLASSIFICATION USING PARALLEL COMBINATION OF LSTM AND CNN


title: Routing automated guided vehicles in container terminals through the Q-learning technique

abstract: This paper suggests a routing method for automated guided vehicles in port terminals that uses the Q-learning technique. One of the most important issues for the efficient operation of an automated guided vehicle system is to find shortest routes for the vehicles. In this paper, we determine shortest-time routes inclusive of the expected waiting times instead of simple shortest-distance routes, which are usually used in practice. For the determination of the total travel time, the waiting time must be estimated accurately. This study proposes a method for estimating for each vehicle the waiting time that results from the interferences among vehicles during travelling. The estimation of the waiting times is achieved by using the Q-learning technique and by constructing the shortest-time routing matrix for each given set of positions of quay cranes. An experiment was performed to evaluate the performance of the learning algorithm and to compare the performance of the learning-based routes with that of the shortest-distance routes by a simulation study.


title: Thesaurus-based automatic query expansion for interface-driven code search

abstract: Software engineers often resort to code search practices to support software maintenance and evolution tasks, in particular code reuse. An issue that affects code search is the vocabulary mismatch problem: while searching for a particular function, users have to guess the exact words that were chosen by original developers to name code entities. In this paper we present an automatic query expansion (AQE) approach that uses word relations to increase the chances of finding relevant code. The approach is applied on top of Test-Driven Code Search (TDCS), a promising code retrieval technique that uses test cases as inputs to formulate the search query, but can also be used with other techniques that handle interface definitions to produce queries (interface-driven code search). Since these techniques rely on keywords and types, the vocabulary mismatch problem is also relevant. AQE is carried out by leveraging WordNet, a type thesaurus for expanding types, and another thesaurus containing only software-related word relations. Our approach is general but was specifically designed for non-native English speakers, who are frequently unaware of the most common terms used to name functions in software. Our evaluation with 36 non-native subjects - including developers and senior Computer Science students - provides evidence that our approach can improve the chances of finding relevant functions by 41% (recall improvement of 30%, on average), without hurting precision.


title: A geographic mobility prediction routing protocol for Ad Hoc UAV Network

abstract: Unmanned Aerial Vehicles (UAVs) play more and more important roles in modern warfare. However, the data routing for communication among UAVs faces several challenges, such as packet loss or routing path failure etc. The main problem of UAVs data routing is caused by the high mobility of UAVs. In this paper, an effective geographic mobility prediction routing protocol is proposed to improve the performance of routing among UAVs. First, a Gaussian distribution of UAVs movement probability density function is deduced to reduce the impact of high mobility. Then, two-hop perimeter forwarding is proposed to reduce the impact of routing void. The experiment results show that the proposed approach can provide effective and reliable data routing with acceptable communication overhead in the highly dynamic environment of Ad Hoc UAV Network.


title: Handover management in SDN-based mobile networks

abstract: In this paper we discuss the evolution of mobility management mechanisms in mobile networks. We emphasize problems with current mobility management approaches in case of very high dense and heterogeneous networks. The main contribution of the paper is a discussion on how the Software-Defined Networking (SDN) technology can be applied in mobile networks in order to efficiently handle mobility in the context of future mobile networks (5G) or evolved LTE. The discussion addresses the most important problems related to mobility management like preservation of session continuity and scalability of handovers in very dense mobile networks. Three variants of SDN usage in order to handle mobility are described and compared in this paper. The most advanced of these variants shows how mobility management mechanisms can be easily integrated with autonomie management mechanisms, providing much more advanced functionality than is provided now by the SON approach. Such mechanisms increase robustness of the handover and optimize the usage of wireless and wired mobile network resources.


title: Diagnostic Evaluation of Information Retrieval Models

abstract: Developing effective retrieval models is a long-standing central challenge in information retrieval research. In order to develop more effective models, it is necessary to understand the deficiencies of the current retrieval models and the relative strengths of each of them. In this article, we propose a general methodology to analytically and experimentally diagnose the weaknesses of a retrieval function, which provides guidance on how to further improve its performance. Our methodology is motivated by the empirical observation that good retrieval performance is closely related to the use of various retrieval heuristics. We connect the weaknesses and strengths of a retrieval function with its implementations of these retrieval heuristics, and propose two strategies to check how well a retrieval function implements the desired retrieval heuristics. The first strategy is to formalize heuristics as constraints, and use constraint analysis to analytically check the implementation of retrieval heuristics. The second strategy is to define a set of relevance-preserving perturbations and perform diagnostic tests to empirically evaluate how well a retrieval function implements retrieval heuristics. Experiments show that both strategies are effective to identify the potential problems in implementations of the retrieval heuristics. The performance of retrieval functions can be improved after we fix these problems.


title: Technologies and Architectures of the Internet-of-Things (IoT) for Health and Well-being

abstract: The emerging technology breakthrough of the Internet-of-Things (IoT) is expected to offer promising solutions for food supply chain (FSC) and in-home healthcare (IHH), which may significantly contr ...


title: Highly Maneuverable Low-Cost Underwater Glider: Design and Development

abstract: This letter presents the design and potential impact of the developed Research Oriented Underwater Glider for Hands-on Investigative Engineering (ROUGHIE). The ROUGHIE is an open-source, highly maneuverable, and low-cost vehicle that enables rapid development and testing of new hardware and software. ROUGHIE is an internally actuated glider capable of performing steady sawtooth glides in shallow water down to 3 m, tight turns with a minimum radius of 3 m, and a minimum endurance of 60 h. The novelty of this study is twofold: 1) a rail-based design to facilitate modularity and ease of assembly and 2) an effective internal rotary mass mechanism to increase maneuverability and perform tight turns. The ROUGHIE design strategically uses 3D printed plastic parts in low stress situations, which allows extreme design flexibility and enables tightly packed modules that can be easily customized.


title: Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear Regression Method

abstract: A new generalized multilinear regression model, termed the Higher-Order Partial Least Squares (HOPLS), is introduced with the aim to predict a tensor (multiway array) $\tensor{Y}$ from a tensor $\tensor{X}$ through projecting the data onto the latent space and performing regression on the corresponding latent variables. HOPLS differs substantially from other regression models in that it explains the data by a sum of orthogonal Tucker tensors, while the number of orthogonal loadings serves as a parameter to control model complexity and prevent overfitting. The low dimensional latent space is optimized sequentially via a deflation operation, yielding the best joint subspace approximation for both $\tensor{X}$ and $\tensor{Y}$. Instead of decomposing $\tensor{X}$ and $\tensor{Y}$ individually, higher order singular value decomposition on a newly defined generalized cross-covariance tensor is employed to optimize the orthogonal loadings. A systematic comparison on both synthetic data and real-world decoding of 3D movement trajectories from electrocorticogram (ECoG) signals demonstrate the advantages of HOPLS over the existing methods in terms of better predictive ability, suitability to handle small sample sizes, and robustness to noise.


title: Glenohumeral Range of Motion Deficits and Posterior Shoulder Tightness in Throwers with Pathologic Internal Impingement

abstract: BACKGROUND ::: Alterations in glenohumeral range of motion, including increased posterior shoulder tightness and glenohumeral internal rotation deficit that exceeds the accompanying external rotation gain, are suggested contributors to throwing-related shoulder injuries such as pathologic internal impingement. Yet these contributors have not been identified in throwers with internal impingement. ::: ::: ::: HYPOTHESIS ::: Throwers with pathologic internal impingement will exhibit significantly increased posterior shoulder tightness and glenohumeral internal rotation deficit without significantly increased external rotation gain. ::: ::: ::: STUDY DESIGN ::: Case control study; Level of evidence, 3. ::: ::: ::: METHODS ::: Eleven throwing athletes with pathologic internal impingement diagnosed using both clinical examination and a magnetic resonance arthrogram were demographically matched with 11 control throwers who had no history of upper extremity injury. Passive glenohumeral internal and external rotation were measured bilaterally with standard goniometry at 90 degrees of humeral abduction and elbow flexion. Bilateral differences in glenohumeral range of motion were used to calculate glenohumeral internal rotation deficit and external rotation gain. Posterior shoulder tightness was quantified as the bilateral difference in passive shoulder horizontal adduction with the scapula retracted and the shoulder at 90 degrees of elevation. Comparisons were made between groups with dependent t tests (P < .05). ::: ::: ::: RESULTS ::: The throwing athletes with internal impingement demonstrated significantly greater glenohumeral internal rotation deficit (P = .03) and posterior shoulder tightness (P = .03) compared with the control subjects. No significant differences were observed in external rotation gain between groups (P = .16). ::: ::: ::: CLINICAL RELEVANCE ::: These findings could indicate that a tightening of the posterior elements of the shoulder (capsule, rotator cuff) may contribute to impingement. The results suggest that management should include stretching to restore flexibility to the posterior shoulder.


title: Statistical fusion of multiple cues for image tampering detection

abstract: Image forensic research has resulted in a number of tampering detection techniques utilizing cues culled from understanding of various natural image characteristics and modeling of tampering artifacts. Fusion of multiple cues provides promises for improving the detection robustness, however has never been systematically studied before. By fusing multiple cues, the tampering detection process does not rely entirely on a single detector and hence can be robust in face of missing or unreliable detectors. In this paper, we propose a statistical fusion framework based on discriminative random fields (DRF) to integrate multiple cues suitable for forgery detection, such as double quantization artifacts and camera response function inconsistency. The detection results using individual cues are used as observation from which the DRF model parameters and the most likely node labels are inferred indicating whether a local block belongs to a tampered foreground or the authentic background. Such inference results also provide information about localization of the suspect spliced regions. The proposed framework is effective and general - outperforming individual detectors over systematic evaluation and easily extensible to other detectors using different cues.


title: The drinking philosophers problem

abstract: The problem of resolving conflicts between processes in distributed systems is of practical importance. A conflict between a set of processes must be resolved in favor of some (usually one) process and against the others: a favored process must have some property that distinguishes it from others. To guarantee fairness, the distinguishing property must be such that the process selected for favorable treatment is not always the same. A distributed implementation of an acyclic precedence graph, in which the depth of a process (the longest chain of predecessors) is a distinguishing property, is presented. A simple conflict resolution rule coupled with the acyclic graph ensures fair resolution of all conflicts. To make the problem concrete, two paradigms are presented: the well-known distributed dining philosophers problem and a generalization of it, the distributed drinking philosophers problem.


title: Solving Sudoku with Boolean Algebra

abstract: Sudoku is a very popular puzzle which consists of placing several numbers in a squared grid according to some simple rules. In this paper, we present a Sudoku solving technique named Boolean Sudoku Solver (BSS) using only simple Boolean algebras. Use of Boolean algebra increases the execution speed of the Sudoku solver. Simulation results show that our method returns the solution of the Sudoku in minimum number of iterations and outperforms the existing popular approaches.


title: Spine dynamics as a computational resource in spine-driven quadruped locomotion

abstract: Recent results suggest that compliance and non-linearity in physical bodies of soft robots may not be disadvantageous properties with respect to control, but rather of advantage. In the context of morphological computation one could see such complex structures as potential computational resources. In this study, we implement and exploit this view point in a spine-driven quadruped robot called Kitty by using its flexible spine as a computational resource. The spine is an actuated multi-joint structure consisting of a sequence of soft silicone blocks. Its complex dynamics are captured by a set of force sensors and used to construct a closed-loop to drive the motor commands. We use simple static, linear readout weights to combine the sensor values to generate multiple gait patterns (bounding, trotting, turning behavior). In addition, we demonstrate the robustness of the setup by applying strong external perturbations in form of additional loads. The system is able to fully recover to its nominal gait patterns (which are encoded in the linear readout weights) after the perturbation has vanished.


title: pARnorama: 360 degree interactive video for augmented reality prototyping

abstract: Designing novel and meaningful interactions in the domain of Augmented Reality (AR) requires an efficient and appropriate methodology. A user centered design process requires the construction and evaluation of several prototypes with increasing technical fidelity. Although the main content of the application can already be conveyed with prerendered video, one of the main interactions in AR - the user-selected viewpoint - is only available in a very late stage. We propose the use of panoramic 360° video for scenario based user evaluation, where the user can select his point of view during playback. Initial users report a high degree of immersion in the constructed scenario, even for handheld AR.


title: Emotions of Musical Instruments

abstract: paper describes a research regarding emotions of musical instruments. The goal is to find out whether it is possible to alter a communicated emotion to a listener by using various musical instruments. These instruments are the piano, marimba, alt sax and cello. After performing an experiment clear differences are visible, especially on the emotions joy and sadness.


title: Living with the past: evolution, development, and patterns of disease

abstract: Epidemiological observations have led to the hypothesis that the risk of developing some chronic noncommunicable diseases in adulthood is influenced not only by genetic and adult life-style factors but also by environmental factors acting in early life. Research in evolutionary biology, developmental biology, and animal and human physiology provides support for this idea and suggests that environmental processes influencing the propensity to disease in adulthood operate during the periconceptual, fetal, and infant phases of life. This "developmental origins of health and disease" concept may have important biological, medical, and socioeconomic implications.


Character-Aware Neural Morphological Disambiguation


title: A novel bridgeless buck-boost PFC converter

abstract: Conventional cascade buck-boost PFC (CBB-PFC) converter suffers from the high conduction loss in the input rectifier bridge. To resolve the above problem, a novel bridgeless buck-boost PFC topology is proposed in this paper. The proposed PFC converter which removes the input rectifier bridge has three conduction semiconductors at every moment. Comparing with CBB-PFC topology, the proposed topology reduces the conduction semiconductors, reduces conduction losses effectively, improves the efficiency of converter and is suitable for use in the wide input voltage range. In this paper, the average current mode control was implemented with UC3854, the theoretical analysis and design of detection circuits was presented. The experimental prototype with 400 V/600 W output and line input voltage range from 220 VAC to 380 VAC was built. Experimental results show that the proposed converter can improve 0.8% efficiency comparing CBB-PFC converter.


title: High flow priapism: diagnosis and treatment in pediatric population

abstract: Purpose ::: To present three cases of arterial high flow priapism (HFP) and propose a management algorithm for this condition.


title: Many-Objective Software Remodularization Using NSGA-III

abstract: Software systems nowadays are complex and difficult to maintain due to continuous changes and bad design choices. To handle the complexity of systems, software products are, in general, decomposed in terms of packages/modules containing classes that are dependent. However, it is challenging to automatically remodularize systems to improve their maintainability. The majority of existing remodularization work mainly satisfy one objective which is improving the structure of packages by optimizing coupling and cohesion. In addition, most of existing studies are limited to only few operation types such as move class and split packages. Many other objectives, such as the design semantics, reducing the number of changes and maximizing the consistency with development change history, are important to improve the quality of the software by remodularizing it. In this article, we propose a novel many-objective search-based approach using NSGA-III. The process aims at finding the optimal remodularization solutions that improve the structure of packages, minimize the number of changes, preserve semantics coherence, and reuse the history of changes. We evaluate the efficiency of our approach using four different open-source systems and one automotive industry project, provided by our industrial partner, through a quantitative and qualitative study conducted with software engineers.


title: Strategic Market Choice: Frequent Call Markets vs. Continuous Double Auctions for Fast and Slow Traders

abstract: Frequent call markets have been proposed as a market design solution to the latency arms race perpetuated by high-frequency traders in continuous markets, but the path to widespread adoption of such markets is unclear. If such trading mechanisms were available, would anyone want to use them? This is ultimately a question of market choice, thus we model it as a game of strategic market selection, where agents choose to participate in either a frequent call market or a continuous double auction. Our market environment is populated by fast and slow traders, who reenter to trade at different rates. We employ empirical game-theoretic methods to determine the market types and trading strategies selected in equilibrium. We also analyze best-response patterns to characterize the frequent call market’s basin of attraction. Our findings show that in equilibrium, welfare of slow traders is generally higher in the call market. We also find strong evidence of a predator-prey relation between fast and slow traders: the fast traders chase agents into either market, and slow traders under pursuit seek the protection of the frequent call market.


title: Bursty research topic detection from scholarly data using dynamic Co-word networks: A preliminary investigation

abstract: Discovering emerging research topics from scholarly data is crucial to facilitate the understanding of trends and history of a target field. In a traditional science mapping approach, a co-word network, which shows the co-occurrence relationship between words, is depicted using a set of papers published in each of time periods. To identify bursty research topics in dynamic co-word networks, as a preliminary investigation, this paper proposes to introduce a scheme that identifies sudden increase in frequency of each word pair. Specifically, we first apply a burst detection model to a time-series of edge weights in the networks, and then reconstruct a network that consists of bursty word co-occurrences for each time period. To show the effectiveness of the proposed framework, we present a case study on conference papers in the fields of information retrieval, data mining, and WWW. The results of the experiments demonstrate that our method can reflect the research trend as compared with original co-word networks.


title: Regularized offpolicy TD-learning

abstract: We present a novel l1 regularized off-policy convergent TD-learning method (termed RO-TD), which is able to learn sparse representations of value functions with low computational complexity. The algorithmic framework underlying RO-TD integrates two key ideas: off-policy convergent gradient TD methods, such as TDC, and a convex-concave saddle-point formulation of non-smooth convex optimization, which enables first-order solvers and feature selection using online convex regularization. A detailed theoretical and experimental analysis of RO-TD is presented. A variety of experiments are presented to illustrate the off-policy convergence, sparse feature selection capability and low computational cost of the RO-TD algorithm.


title: A Survey of Passive Image Tampering Detection

abstract: Digital images can be easily tampered with image editing tools. The detection of tampering operations is of great importance. Passive digital image tampering detection aims at verifying the authenticity of digital images without any a prior knowledge on the original images. There are various methods proposed in this filed in recent years. In this paper, we present an overview of these methods in three levels, that is low level, middle level, and high level in semantic sense. The main ideas of the proposed approaches at each level are described in detail, and some comments are given.


Automatic Evaluation of Machine Translation Quality


title: Robotics and artificial intelligence: A perspective on deliberation functions

abstract: Despite a very strong synergy between Robotics and AI at their early beginning, the two fields progressed widely apart in the following decades. However, we are witnessing a revival of interest in the fertile domain of embodied machine intelligence. This is due in particular to the dissemination of more mature techniques from both areas, to more accessible robot platforms with advanced sensory motor capabilities, and to a better understanding of the scientific challenges of the AI-Robotics intersection.The ambition of this paper is to contribute to this revival. It proposes an overview of problems and approaches to autonomous deliberate action in robotics. The paper advocates for a broad understanding of deliberation functions. It presents a synthetic perspective on planning, acting, perceiving, monitoring, goal reasoning and their integrative architectures, which is illustrated through several contributions that addressed deliberation from the AI-Robotics point of view.


Gather-Excite : Exploiting Feature Context in Convolutional Neural Networks


title: A Long-Term Analysis of Polarization on Twitter

abstract: Social media has played an important role in shaping political discourse over the last decade. At the same time, it is often perceived to have increased political polarization, thanks to the scale of discussions and their public nature. In this paper, we try to answer the question of whether political polarization in the US on Twitter has increased over the last eight years. We analyze a large longitudinal Twitter dataset of 679,000 users and look at signs of polarization in their (i) network - how people follow political and media accounts, (ii) tweeting behavior - whether they retweet content from both sides, and (iii) content - how partisan the hashtags they use are. Our analysis shows that online polarization has indeed increased over the past eight years and that, depending on the measure, the relative change is 10%-20%. Our study is one of very few with such a long-term perspective, encompassing two US presidential elections and two mid-term elections, providing a rare longitudinal analysis.


title: GrammarViz 3.0: Interactive Discovery of Variable-Length Time Series Patterns

abstract: The problems of recurrent and anomalous pattern discovery in time series, e.g., motifs and discords, respectively, have received a lot of attention from researchers in the past decade. However, since the pattern search space is usually intractable, most existing detection algorithms require that the patterns have discriminative characteristics and have its length known in advance and provided as input, which is an unreasonable requirement for many real-world problems. In addition, patterns of similar structure, but of different lengths may co-exist in a time series. Addressing these issues, we have developed algorithms for variable-length time series pattern discovery that are based on symbolic discretization and grammar inference—two techniques whose combination enables the structured reduction of the search space and discovery of the candidate patterns in linear time. In this work, we present GrammarViz 3.0—a software package that provides implementations of proposed algorithms and graphical user interface for interactive variable-length time series pattern discovery. The current version of the software provides an alternative grammar inference algorithm that improves the time series motif discovery workflow, and introduces an experimental procedure for automated discretization parameter selection that builds upon the minimum cardinality maximum cover principle and aids the time series recurrent and anomalous pattern discovery.


title: Multidisciplinary design optimization of mechanisms

abstract: The optimization of mechanisms is usually done in the field of mechanism itself. As a result the structural safety of the mechanism is neglected. To ensure the safety and improve the dynamic characteristics of mechanisms, a multidisciplinary design optimization procedure is proposed in this paper to synthesize optimum mechanisms. Two disciplines are involved in the multidisciplinary design optimization. They are the mechanism and the structure. The multi-level decomposition approach is chosen to generate optimum mechanisms. The optimized mechanisms not only satisfy mechanism and structural constraints but also have optimum objective function values in the two disciplines. In order to solve general mechanism design problems two widely used commercial softwares MSC/NASTRAN and MSC/ADAMS are integrated in the procedure to do the structural and the mechanism analysis, respectively. When the structural optimization is performed, a compromised approach is introduced to treat multiple configurations of mechanisms during operation. Two mechanism design problems are given to test the proposed method.


title: Separation of Manga Line Drawings and Screentones

abstract: Screentones are unique expressions of Japanese comics (manga), which enrich their visual expression. However, such screentones have a very different visual nature from that of line drawing areas; this prevents us from applying various kinds of image processing techniques to manga. We propose a method for extracting line drawings and removing screentones. We employ Laplacians of Gaussian filters and flow-based differences of Gaussian filters, one for removing screentones and the other for preserving lines, and make a binary mask for separating line drawings from manga by merging the results of the two filters. We show that the proposed method successfully separates line drawings and is better than existing methods in comparative studies.


title: The Use of Celecoxib for Reduction of Pain After Subpectoral Breast Augmentation

abstract: A study was conducted to determine whether a single 400-mg dose of oral celecoxib administered 30 min before surgery reduces the opioid requirement for patients undergoing aesthetic subpectoral breast augmentation. A total of 695 patients undergoing breast augmentation were randomly selected into either a placebo or a treatment group. The findings showed that patients who received 400 mg of celecoxib 30 min before surgery required significantly fewer opioid analgesics after the operation than those given a placebo (p < 0.001). It also was found that nonsmokers and multiparous women required significantly fewer opioids than smokers and nulliparous women (p < 0.001). On the basis of this prospective study, the authors recommend a single 400-mg dose of celecoxib administered 30 min before surgery to decrease opioid analgesic requirements after subpectoral breast augmentation.


title: Filter-free AC direct LED driver with unity power factor and low input current THD using binary segmented switched LED strings and linear current regulator

abstract: A compact LED driver with high power factor and low harmonic distortion is proposed for the AC direct LED lighting. For the compact implementation of LED lamp, all the bulky passive components such as inductors and electrolytic capacitors are eliminated. To meet power factor and total harmonic distortion regulations, the current flows through LED string is controlled by a linear current regulator. To minimize the power loss of the linear current regulator, the voltage drop of the whole LED string is adjusted by means of binary switching. The drain voltage of the linear current regulator is controlled by simple digital logics and comparators to limit the current within predefined range. The proposed LED driver was verified by simulations and a prototype experiment kit.


title: A Novel Riemannian Metric Based on Riemannian Structure and Scaling Information for Fixed Low-Rank Matrix Completion

abstract: Riemannian optimization has been widely used to deal with the fixed low-rank matrix completion problem, and Riemannian metric is a crucial factor of obtaining the search direction in Riemannian optimization. This paper proposes a new Riemannian metric via simultaneously considering the Riemannian geometry structure and the scaling information, which is smoothly varying and invariant along the equivalence class. The proposed metric can make a tradeoff between the Riemannian geometry structure and the scaling information effectively. Essentially, it can be viewed as a generalization of some existing metrics. Based on the proposed Riemanian metric, we also design a Riemannian nonlinear conjugate gradient algorithm, which can efficiently solve the fixed low-rank matrix completion problem. By experimenting on the fixed low-rank matrix completion, collaborative filtering, and image and video recovery, it illustrates that the proposed method is superior to the state-of-the-art methods on the convergence efficiency and the numerical performance.


title: Anti-Forensics and the Digital Investigator

abstract: Viewed generically, anti-forensics (AF) is that set of tactics and measures taken by someone who wants to thwart the digital investigation process. This paper describes some of the many AF tools and methods, under the broad classifications of data hiding, artefact wiping, trail obfuscation, and attacks on the forensics tools themselves. The concept of AF is neither new nor solely intended to be used by the criminal class; it also has legitimate use by those who wish to protect their privacy. This paper also introduces the concept of time-sensitive antiforensics, noting that AF procedures might be employed for the sole purpose of delaying rather than totally preventing the discovery of digital information.


title: STOPP & START criteria: A new approach to detecting potentially inappropriate prescribing in old age

abstract: Potentially inappropriate prescribing (IP) is an issue of major importance in the pharmacotherapy of older people globally. To date, Beers’ criteria have been in common research usage for defining potentially inappropriate medications (PIMs) in older people. However, Beers’ criteria have a number of serious flaws and are of doubtful relevance to routine geriatric pharmacotherapy. For these reasons, new geriatric IP criteria have been devised and validated, called screening tool of older persons’ prescriptions (STOPP) and screening tool to alert to right treatment (START) for detection of potential errors of prescribing commission and omission. Prevalence studies in older people in southern Ireland show substantial rates of prescription of one or more PIMs in the primary care setting (22%), in acute hospital care (35%) and in nursing home care (60%) using STOPP criteria. Similarly, prevalence rates of potentially serious prescribing omission are high both in hospital (58%) and in primary care (23%). Prospective data show that STOPP criteria detect adverse drug effects (ADEs) that are causal or contributory to acute hospitalisation in older people 2.8 times more frequently than Beers’ criteria. This suggests that ADEs are likely associated with PIMs in older people, contrary to recent research data showing no significant association on the basis of defining PIMs using Beers’ criteria. We have recently shown that STOPP/START criteria as an intervention significantly improves medication appropriateness in hospitalised older people. Whether STOPP/START can prevent ADEs and reduce drug costs remains to be elucidated by means of further randomised controlled trials.


title: Learning Empathy Through Virtual Reality: Multiple Strategies for Training Empathy-Related Abilities Using Body Ownership Illusions in Embodied Virtual Reality

abstract: Several disciplines have investigated the interconnected empathic abilities behind the proverb “to walk a mile in someone else’s shoes” to determine how the presence, and absence, of empathy-related phenomena affect prosocial behavior and intergroup relations. Empathy enables us to learn from others’ pain and to know when to offer support. Similarly, virtual reality (VR) appears to allow individuals to step into someone else’s shoes, through a perceptual illusion called embodiment, or the body ownership illusion. Considering these perspectives, we propose a theoretical analysis of different mechanisms of empathic practices in order to define a possible framework for the design of empathic training in VR. This is not intended to be an extensive review of all types of practices, but an exploration of empathy and empathy-related phenomena. Empathy-related training practices are analyzed and categorized. We also identify different variables used by pioneer studies in VR to promote empathy-related responses. Finally, we propose strategies for using embodied VR technology to train specific empathy-related abilities.


title: Facial Attributes Classification Using Multi-task Representation Learning

abstract: This paper presents a new approach for facial attribute classification using a multi-task learning approach. Unlike other approaches that uses hand engineered features, our model learns a shared feature representation that is wellsuited for multiple attribute classification. Learning a joint feature representation enables interaction between different tasks. For learning this shared feature representation we use a Restricted Boltzmann Machine (RBM) based model, enhanced with a factored multi-task component to become Multi-Task Restricted Boltzmann Machine (MT-RBM). Our approach operates directly on faces and facial landmark points to learn a joint feature representation over all the available attributes. We use an iterative learning approach consisting of a bottom-up/top-down pass to learn the shared representation of our multi-task model and at inference we use a bottom-up pass to predict the different tasks. Our approach is not restricted to any type of attributes, however, for this paper we focus only on facial attributes. We evaluate our approach on three publicly available datasets, the Celebrity Faces (CelebA), the Multi-task Facial Landmarks (MTFL), and the ChaLearn challenge dataset. We show superior classification performance improvement over the state-of-the-art.


title: Linear Volumetric Focus for Light Field Cameras

abstract: We demonstrate that the redundant information in light field imagery allows volumetric focus, an improvement of signal quality that maintains focus over a controllable range of depths. To do this, we derive the frequency-domain region of support of the light field, finding it to be the 4D hyperfan at the intersection of a dual fan and a hypercone, and design a filter with correspondingly shaped passband. Drawing examples from the Stanford Light Field Archive and images captured using a commercially available lenslet-based plenoptic camera, we demonstrate that the hyperfan outperforms competing methods including planar focus, fan-shaped antialiasing, and nonlinear image and video denoising techniques. We show the hyperfan preserves depth of field, making it a single-step all-in-focus denoising filter suitable for general-purpose light field rendering. We include results for different noise types and levels, through murky water and particulate matter, in real-world scenarios, and evaluated using a variety of metrics. We show that the hyperfan's performance scales with aperture count, and demonstrate the inclusion of aliased components for high-quality rendering.


title: Connectedness in Action

abstract: An insightful ability to connect distinguishes Professor Stohl’s remarkable work. Her scholarship has played a pivotal role in moving the organizational communication discipline into the global arena, connecting organizational communication with both new scholars and new realms of inquiry befitting the world stage. Cynthia has helped connect organizational communication to the broader global arena by going global herself. Her work is marked by her engagement of globalization issues and validated by her experiences as a scholar working both outside and inside the United States. More importantly, Cynthia’s prominence in our field grows from her commitment to values of scholarship and practice, a commitment you will find connected throughout her address. And it is that commitment to our shared values of scholarship that connects us all as organizational scholars. It is the same value commitment that inspires us to work together to make organizations a more positive force in the global world.


title: Parallel-input series-output interleaved flyback based solar PV module integrated micro-inverter

abstract: This paper proposes a parallel input, series output interleaved flyback principle based micro-inverter for solar PV module integrated converter application. In the proposed topology, inputs of two flyback transformers are connected in parallel while the outputs are connected in series by using interleaving concept. This topology is helpful in achieving high output voltage (ac voltage) from low voltage input of PV module. The salient features of the proposed topology include (a) individual transformers with lower turn's ratio leading to reduced leakage inductance; (b) reduced current stress on primary side switches and (c) lower peak inverse voltage across secondary side diodes. These features lead to higher efficiency. Operating modes, control scheme and simulation results of the proposed topology are included. Theoretical results are validated by hardware experiments and these results are also included.


title: CROWDMOS: An approach for crowdsourcing mean opinion score studies

abstract: MOS (mean opinion score) subjective quality studies are used to evaluate many signal processing methods. Since laboratory quality studies are time consuming and expensive, researchers often run small studies with less statistical significance or use objective measures which only approximate human perception. We propose a cost-effective and convenient measure called crowdMOS, obtained by having internet users participate in a MOS-like listening study. Workers listen and rate sentences at their leisure, using their own hardware, in an environment of their choice. Since these individuals cannot be supervised, we propose methods for detecting and discarding inaccurate scores. To automate crowdMOS testing, we offer a set of freely distributable, open-source tools for Amazon Mechanical Turk, a platform designed to facilitate crowdsourcing. These tools implement the MOS testing methodology described in this paper, providing researchers with a user-friendly means of performing subjective quality evaluations without the overhead associated with laboratory studies. Finally, we demonstrate the use of crowdMOS using data from the Blizzard text-to-speech competition, showing that it delivers accurate and repeatable results.


title: Supporting trust in virtual communities

abstract: At any given time, the stability of a community depends on the right balance of trust and distrust. Furthermore, we face information overload, increased uncertainty and risk taking as a prominent feature of modern living. As members of society, we cope with these complexities and uncertainties by relying trust, which is the basis of all social interactions. Although a small number of trust models have been proposed for the virtual medium, we find that they are largely impractical and artificial. In this paper we provide and discuss a trust model that is grounded in real-world social trust characteristics, and based on a reputation mechanism, or word-of-mouth. Our proposed model allows agents to decide which other agents' opinions they trust more and allows agents to progressively tune their understanding of another agent's subjective recommendations.


Framing Tension for Game Generation


title: CacheDOCS: A Dynamic Key-Value Object Caching Service

abstract: Caching plays an important role in many domains, as it can lead to important performance improvements. A key-value based caching system typically stores the results of popular queries in efficient storage locations. While caching enjoys widespread usage in the context of dynamic web applications, most mainstream caching systems store static binary items, which makes them impractical for many real-world applications that would benefit from storing dynamic items. In this paper, we propose CacheDOCS, a dynamic key-value object caching service that allows for caching arbitrary objects. As part of our model, CacheDOCS provides an API that supports the execution of operations against cached objects, and allows for clients to seamlessly subscribe to keep their local copies in sync with cached remote objects. CacheDOCS supports multiple update dissemination strategies in order to optimize performance, and proposes a versioning mechanism to ensure consistency. We implemented a full version of CacheDOCS and we ran several performance-related experiments under three use-case scenarios.


MIURA FOLDING : APPLYING ORIGAMI TO SPACE EXPLORATION


Video Stabilization and Face Saliency-based Retargeting


title: Learning with a Strong Adversary

abstract: The robustness of neural networks to intended perturbations has recently attracted significant attention. In this paper, we propose a new method, \emph{learning with a strong adversary}, that learns robust classifiers from supervised data. The proposed method takes finding adversarial examples as an intermediate step. A new and simple way of finding adversarial examples is presented and experimentally shown to be efficient. Experimental results demonstrate that resulting learning method greatly improves the robustness of the classification models produced.


title: At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on

abstract: Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. We present an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. We use machine learning techniques to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. We tested our system in one home for several weeks and in five homes for one week each to evaluate the system performance over time and in different types of houses. Results indicate that we can learn and classify various electrical events with accuracies ranging from 85-90%.


1 Common Features of the Sea Level Records from Baltic Tide Gauges


title: Developing a multiple-document-processing performance assessment for epistemic literacy

abstract: The LAK15 theme "shifts the focus from data to impact", noting the potential for Learning Analytics based on existing technologies to have scalable impact on learning for people of all ages. For such demand and potential in scalability to be met the challenges of addressing higher-order thinking skills should be addressed. This paper discuses one such approach--the creation of an analytic and task model to probe epistemic cognition in complex literacy tasks. The research uses existing technologies in novel ways to build a conceptually grounded model of trace-indicators for epistemic-commitments in information seeking behaviors. We argue that such an evidence centered approach is fundamental to realizing the potential of analytics, which should maintain a strong association with learning theory.


AUTOMATIC CONTROL PROCESS OF SOLENOID VALVE PRODUCTION LINE BASED ON PLC AND TOUCH SCREEN


title: Phylogenomic Insights into Animal Evolution

abstract: Animals make up only a small fraction of the eukaryotic tree of life, yet, from our vantage point as members of the animal kingdom, the evolution of the bewildering diversity of animal forms is endlessly fascinating. In the century following the publication of Darwin's Origin of Species, hypotheses regarding the evolution of the major branches of the animal kingdom - their relationships to each other and the evolution of their body plans - was based on a consideration of the morphological and developmental characteristics of the different animal groups. This morphology-based approach had many successes but important aspects of the evolutionary tree remained disputed. In the past three decades, molecular data, most obviously primary sequences of DNA and proteins, have provided an estimate of animal phylogeny largely independent of the morphological evolution we would ultimately like to understand. The molecular tree that has evolved over the past three decades has drastically altered our view of animal phylogeny and many aspects of the tree are no longer contentious. The focus of molecular studies on relationships between animal groups means, however, that the discipline has become somewhat divorced from the underlying biology and from the morphological characteristics whose evolution we aim to understand. Here, we consider what we currently know of animal phylogeny; what aspects we are still uncertain about and what our improved understanding of animal phylogeny can tell us about the evolution of the great diversity of animal life.


title: Facial Sculpting and Tissue Augmentation

abstract: BACKGROUND. Until recently, deep facial sculpting was exclusively the domain of surgical interventions. Recent advances in the available array of dermal and subdermal fillers combined with an esthetic appreciation by both surgeons and nonsurgeons alike of the positive effect of filling the volume-depleted face have led to an expansion in the indications for the use of soft tissue augmenting agents. METHOD. Subdermal support of the lateral two-thirds of the brow, the nasojugal fold, the malar and buccal fat pads, the lateral lip commissures, and the perioral region, including the prejowl sulcus, all restore youthful facial contour and harmony. An important advance in technique is the subdermal rather than the intradermal injection plane. RESULTS. “Instant” facial sculpting giving a brow-lift, cheek-lift, lip expansion, and perioral augmentation is possible using modern soft tissue augmenting agents. The softer, more relaxed appearance contrasts to the somewhat “pulled” appearance of subjects who have had surgical overcorrections. Treatments can be combined with botulinum toxin and other procedures if required. CONCLUSION. Newer advances in the use of fillers include the use of fillers injected in the subdermal plane for “lunchtime” facial sculpting. Using the modern esthetic filler compounds, which are biodegradable but longer lasting, subjects can have a “rehearsal” treatment or make it ongoing. Some individuals, such as those with human immunodeficiency virus (HIV)-related lipoatrophy or those who desire to obtain a longer-lasting effect, may elect to use a nonbiodegradable filling agent.


title: A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure

abstract: We consider a non-projective class of inhomogeneous random graph models with interpretable parameters and a number of interesting asymptotic properties. Using the results of Bollob\'as et al. [2007], we show that i) the class of models is sparse and ii) depending on the choice of the parameters, the model is either scale-free, with power-law exponent greater than 2, or with an asymptotic degree distribution which is power-law with exponential cut-off. We propose an extension of the model that can accommodate an overlapping community structure. Scalable posterior inference can be performed due to the specific choice of the link probability. We present experiments on five different real-world networks with up to 100,000 nodes and edges, showing that the model can provide a good fit to the degree distribution and recovers well the latent community structure.


title: AutomaDeD: Automata-based debugging for dissimilar parallel tasks

abstract: Today's largest systems have over 100,000 cores, with million-core systems expected over the next few years. This growing scale makes debugging the applications that run on them a daunting challenge. Few debugging tools perform well at this scale and most provide an overload of information about the entire job. Developers need tools that quickly direct them to the root cause of the problem. This paper presents AutomaDeD, a tool that identifies which tasks of a large-scale application first manifest a bug at a specific code region and specific program execution point. AutomaDeD statistically models the application's control-flow and timing behavior, grouping tasks and identifying deviations from normal execution, which significantly reduces debugging effort. In addition to a case study in which AutomaDeD locates a bug that occurred during development of MVAPICH, we evaluate AutomaDeD on a range of bugs injected into the NAS parallel benchmarks. Our results demonstrate that AutomaDeD detects the time period when a bug first manifested with 90% accuracy for stalls and hangs and 70% accuracy for interference faults. It identifies the subset of processes first affected by the fault with 80% accuracy and 70% accuracy, respectively and the code region where the fault first manifested with 90% and 50% accuracy, respectively.


title: Classification of Organic Solvents based on Correlation between Dielectric /? Parameter and Empirical Solvent Polarity Parameter i $ t

abstract: It is proposed that the ratio µ2/V, from electrostatic solvation theory, be used to designate solvent polarity; µ is the dipole moment and V the molar volume. The 101 solvents analysed can be divided into four classes (and two subclasses) by means of the correlation between the dielectric β parameter (proportional to µ2/V) and the empirical solvent polarity parameter ENT. Although the method proposed is very simple, the results agree very well with those obtained by a much more complex multivariate statistical method (M. Chastrette, M. Rajzmann, M. Chanon and K. F. Purcell, J. Am. Chem. Soc., 1985, 107, 1). Moreover some solvents, that are apparently non-conformist under the Chastrette criteria, can be classified in line with their molecular properties under the method described here.


title: Mobility of an in-pipe robot with screw drive mechanism inside curved pipes

abstract: This paper presents motion analyses and experiments of an in-pipe robot with screw drive mechanism while it moves inside curved pipes. This robot is driven by only one motor and composed of two units. One unit works as a rotator and the another one works as a stator. Screw drive mechanism is a better way to travel inside pipelines of small diameter for inspection, because the area for moving of the robot is narrow and the number of actuators can be reduced (minimum is just one). Therefore, the robot can be smaller and lighter. Furthermore, the control becomes easier. Although many kinds of in-pipe robots with screw drive mechanism have been reported to date, the most important problem of such drive mechanism is the difficulty of traveling in pipes other than straight ones. Examples of such pipes are curved pipes like elbow and bent, branch pipes like a T-shape and the pipe where diameter changes. A concrete analysis has not been done yet. In this paper, we concentrate on a helical driving motion of the robot inside the curved pipe and finally perform experiments to determine the characteristics of it.


title: Open-Domain Semantic Parsing with Boxer

abstract: Boxer is a semantic parser for English texts with many input and output possibilities, and various ways to perform meaning analysis based on Discourse Representation Theory. This involves the various ways that meaning representations can be computed, as well as their possible semantic ingredients.


title: Channel estimation in massive MIMO systems using 1-bit quantization

abstract: Massive MIMO plays an important role for future cellular networks since the large number of antennas deployed at the base station (BS) is capable of increasing the spectral efficiency and the amount of usable spectrum. Using 1-bit analog-to-digital converters can drastically reduce the resulting complexity and power consumption. Therefore, we investigate the channel estimation in 1-bit massive MIMO, where several single-antenna mobile stations (MSs) transmit training sequences to the BS, whose antennas acquire only 1-bit measurements. The channels between the MSs and the BS antennas are described by their impulse responses. In particular, we consider sparse channel impulse responses having only a few non-zero taps. By combining the Expectation-Maximization algorithm for Maximum A Posteriori estimation with the sparse recovery method Iterative Hard Thresholding, we exploit the a priori knowledge of this sparsity and take the 1-bit quantization into account. Since the resulting channel estimation methods combine a good channel estimation performance demonstrated by numerical results with a small computational complexity, they are promising methods for channel estimation in 1-bit massive MIMO.


Isolation and characterization of the S-glucuronide of 4-nitrothiophenol formed by microsomal glucuronyl transferase.


title: The reconstruction of a mobile agent computation and its validation

abstract: We propose a new representation to describe mobile agents computations, where the underlying control structures are diffusing computations. Usually, distributed computations are abstracted as sets of receiving, sending and internal events. Instead of using this representation, we consider a diffusing computation as a set of leaves of a control tree associated to the different threads of control generated by the computation. We introduce so-called "Visit tags" to capture such a representation. These tags exhibit several nice properties, in particular, the whole computation tree can be reconstructed from the knowledge of tags labeling termination events. This tree is useful to assess global properties or evaluate state predicates, as well as to replay the computation. We present a formal development of the reconstruction algorithm as a sequence of refinement steps.


title: Reduced size planar Log-Periodic Dipole Arrays (LPDAs) using rectangular meander line elements

abstract: Two different designs for planar LPDAs were designed and measured. Both designs showed low return loss and high F/B ratio, accompanied by an acceptable value of gain. A comparison between them showed that the meander design has similar properties to the Euclidean design, while it occupies less space in the antenna system and thus less weight.


title: Usability of LoRaWAN Technology in a Central Business District

abstract: LoRaWAN is a relatively recent Low-Power Wide Area Network specification intended to support wireless battery operated devices for low data rate applications such as infrastructure monitoring, transportation, asset tracking, security, and health care. While it is clear that heavily built- up areas such as the Central Business District (CBD) of typical cities offer the greatest density of potential applications, the operation of its physical RF protocol, LoRa, has not been comprehensively studied in these environments. This paper presents a preliminary set of measurements evaluating the performance of LoRa in a high-density urban area (Melbourne''s CBD). Our results shows that loss-free communication is assured only within a radius of approximately 200 meters from the base station and total loss of transmission occurs at around 600 meters.


title: A 5-Gb/s 2.67-mW/Gb/s Digital Clock and Data Recovery With Hybrid Dithering Using a Time-Dithered Delta–Sigma Modulator

abstract: A digital clock and data recovery (CDR) employing a time-dithered delta–sigma modulator (TDDSM) is presented. By enabling hybrid dithering of a sampling period as well as an output bit of the TDDSM, the proposed CDR enhances the resolution of digitally controlled oscillator, removes a low-pass filter in the integral path, and reduces jitter generation. Fabricated in a 65-nm CMOS process, the proposed CDR operates at 5-Gb/s data rate with $\textrm {BER} for PRBS 31. The CDR consumes 13.32 mW at 5 Gb/s and achieves 2.14 and 29.7 ps of a long-term rms and peak-to-peak jitter, respectively.