title: Design of a smart 3D-printed wristed robotic surgical instrument with embedded force sensing and modularity

abstract: This paper introduces the design and characterization of a robotic surgical instrument produced mainly with rapid prototyping techniques. Surgical robots have generally complex structures and have therefore an elevated cost. The proposed instrument was designed to incorporate minimal number of components to simplify the assembly process by leveraging the unique strength of rapid prototyping for producing complex, assemble-free components. The modularity, cost-effectiveness and fast manufacturing and assembly features offer the possibility of producing patient or task specific instruments. The proposed robot incorporates an integrated force measurement system, thus allowing the determination of the force exchanged between the instrument and the environment. Detailed experiments were performed to validate the functionality and force sensing capability of the instrument.


Approaches to architecture-aware parallel scientific computation


title: Batteryless, Wireless Sensor Powered by a Sediment Microbial Fuel Cell

abstract: Sediment microbial fuel cells (SMFCs) are considered to be an alternative renewable power source for remote monitoring. There are two main challenges to using SMFCs as power sources: 1) a SMFC produces a low potential at which most sensor electronics do not operate, and 2) a SMFC cannot provide continuous power, so energy from the SMFC must be stored and then used to repower sensor electronics intermittently. In this study, we developed a SMFC and a power management system (PMS) to power a batteryless, wireless sensor. A SMFC operating with a microbial anode and cathode, located in the Palouse River, Pullman, Washington, U.S.A., was used to demonstrate the utility of the developed system. The designed PMS stored microbial energy and then started powering the wireless sensor when the SMFC potential reached 320 mV. It continued powering until the SMFC potential dropped below 52 mV. The system was repowered when the SMFC potential increased to 320 mV, and this repowering continued as long as microbial reacti...


title: Fast Haze Removal for Nighttime Image Using Maximum Reflectance Prior

abstract: In this paper, we address a haze removal problem from a single nighttime image, even in the presence of varicolored and non-uniform illumination. The core idea lies in a novel maximum reflectance prior. We first introduce the nighttime hazy imaging model, which includes a local ambient illumination item in both direct attenuation term and scattering term. Then, we propose a simple but effective image prior, maximum reflectance prior, to estimate the varying ambient illumination. The maximum reflectance prior is based on a key observation: for most daytime haze-free image patches, each color channel has very high intensity at some pixels. For the nighttime haze image, the local maximum intensities at each color channel are mainly contributed by the ambient illumination. Therefore, we can directly estimate the ambient illumination and transmission map, and consequently restore a high quality haze-free image. Experimental results on various nighttime hazy images demonstrate the effectiveness of the proposed approach. In particular, our approach has the advantage of computational efficiency, which is 10-100 times faster than state-of-the-art methods.


title: The origins and the future of microfluidics

abstract: The manipulation of fluids in channels with dimensions of tens of micrometres--microfluidics--has emerged as a distinct new field. Microfluidics has the potential to influence subject areas from chemical synthesis and biological analysis to optics and information technology. But the field is still at an early stage of development. Even as the basic science and technological demonstrations develop, other problems must be addressed: choosing and focusing on initial applications, and developing strategies to complete the cycle of development, including commercialization. The solutions to these problems will require imagination and ingenuity.


title: Intolerance of sexy peers: intrasexual competition among women

abstract: Intrasexual competition among males of different species, including humans, is well documented. Among females, far less is known. Recent nonexperimental studies suggest that women are intolerant of attractive females and use indirect aggression to derogate potential rivals. In Study 1, an experimental design was used to test the evolutionary-based hypothesis that women would be intolerant of sexy women and would censure those who seem to make sex too readily available. Results provide strong empirical support for intrasexual competition among women. Using independent raters, blind to condition, we found that almost all women were rated as reacting negatively ("bitchy") to an attractive female confederate when she was dressed in a sexually provocative manner. In contrast, when she was dressed conservatively, the same confederate was barely noticed by the participants. In Study 2, an experimental design was used to assess whether the sexy female confederate from Study 1 was viewed as a sexual rival by women. Results indicated that as hypothesized, women did not want to introduce her to their boyfriend, allow him to spend time alone with her, or be friends with her. Findings from both studies are discussed in terms of evolutionary theory. Aggr. Behav. 37:1-9, 2011. © 2011 Wiley-Liss, Inc. Language: en


title: Aesthetic preformed paediatric crowns: systematic review

abstract: Background ::: Different aesthetic preformed crowns (APC) are proposed to restore decayed and damaged primary teeth because the stainless steel crowns (SSCs) do not satisfy parents concerned about aesthetic. ::: ::: Aim ::: The objective was to evaluate the clinical effectiveness of all types of APC for restoring primary teeth compared with conventional filling materials or other types of crowns. ::: ::: Design ::: Relevant articles were searched in electronic databases of PubMed via MEDLINE and the Cochrane central register of controlled trials. Two review authors independently assessed the risk of bias in the included articles and extracted data. ::: ::: Results ::: From 555 potentially eligible articles, seven relevant articles were included. The overall risk of bias was high for all the studies. SCCs cannot be replaced by APC, such as the open-faced SSCs and the pre-veneered SCCs, for restoring badly decayed primary molars. Zircon crowns were assessed only in primary incisors and compared with pre-veneered SCCs and resin composite strip crowns. After a follow-up of only 6 months, Zircon crowns gave significantly better results than the others with regard to gingival health and crown fractures. ::: ::: Conclusion ::: Due to the small number of RCTs on this topic and their risk of bias, future RCTs should be carried out in primary teeth.


title: A Variant of Genetic Algorithm Based Categorical Data Clustering for Compact Clusters and an Experimental Study on Soybean Data for Local and Global Optimal Solutions

abstract: Almost all partitioning clustering algorithms getting stuck to the local optimal solutions. Using Genetic algorithms (GA) the results can be find globally optimal. This piece of work offers and investigates a new variant of the Genetic algorithm (GA) based k-Modes clustering algorithm for categorical data. A statistical analysis have been done on the popular categorical dataset which shows the user specified cluster centres stuck at local optimal solution in K-modes algorithm even in all the higher iterations and the proposed algorithm overcome this problem of local optima. To the best of our knowledge, such comparison has been reported here for the first time for the case of categorical data. The obtained results, shows that the proposed algorithm is better over the conventional k-Modes algorithm in terms of optimal solutions and within cluster variation measure.


title: Improving GPS-based indoor-outdoor detection with moving direction information from smartphone

abstract: This paper presents an improvement for GPS-based indoor-outdoor detection with moving direction information. First, we considered several features based on raw GPS satellite informationas the baseline. From the evaluation with GPS data from 19 persons, the SVM-based classifier using a combination of elevation and S/N ratio achieved over 96% accuracy for the simple 'clear' situation. Then we introduced direction information from a compass sensor to increase the detection robustness in the canyon of buildings. The second experimentation result showed the proposed method considering direction information kept almost the same accuracy of indoor-outdoor detectionin a 'canyon' situation, whereas the baseline classifier worsened accuracy by 10%.


title: Unsupervised Color-Texture Image Segmentation Based on A New Clustering Method

abstract: Image segmentation is a classical problem in the area of image processing, motion estimation, and soon. Although there exist a lot of clustering based approaches to perform image segmentation, few of them study how to obtain more accurate image segmentation results by designing a suitable clustering method. In this paper, we select an appropriate distance measure in the composite feature space of color and texture. Then the distance measure is incorporated in a clustering method that utilizes the spatial information of each feature vector. Finally, the proposed scheme performs morphology filtering to obtain the final segmented regions. Experimental results show that proposed scheme can constantly achieve higher segmentation accuracy compared to some state-of-art image segmentation algorithms.


A review and an approach for object detection in images


title: Evaluating Software Engineers' Acceptance of a Technique and Tool for Web Usability Inspection

abstract: Usability is related to software quality, improving its ability to be understood, operated and attractive to users. We proposed the Design Usability Evaluation (DUE) technologies to allow identifying usability problems earlier in the development of Web applications, through the inspection of mockups. While we found that the DUE technique and tool were effective and efficient in the identification of usability problems, we saw the need to investigate their acceptance in practitioners' work environment. This paper reports the results from a study evaluating the acceptance of the DUE technologies from the point of view of software engineers. We asked questions based on the indicators from the Technology Acceptance Model and identified that a majority of the software engineers who participated in the study: (a) found the DUE technologies useful and easy to use for supporting the usability inspection process; and (b) would regularly use the DUE technologies for future inspections in their job. Nevertheless, the practitioners indicated that the technique should be refined in order to reduce the ambiguity and repetition of some of its items, while the tool should become more intuitive.


title: Design and demonstration of a pumpless 14 compartment microphysiological system.

abstract: We describe a human "Body-on-a-chip" device (or microphysiological system) that could be used to emulate drug distribution, metabolism, and action in the body. It is based upon a physiologically based pharmacokinetic-pharmacodynamic (PBPK-PD) model, where multiple chambers representing different organs are connected with fluidic channels to mimic multi-organ interactions within the body. Here we describe a pumpless 14 chamber (13 organs) microfluidic cell culture device that provides a separation between barrier and nonbarrier types of cell cultures. Our barrier chamber layer (skin, GI tract, and lung) allows for direct access and/or exposures to chemical or biological reagents forcing these reagents to pass through a barrier of cells established on a microfabricated membrane before exposing the nonbarrier tissue chambers (fat, kidney, heart, adrenal glands, liver, spleen, pancreas, bone marrow, brain, muscle) or entering the microfluidic circulation within the device. Our nonbarrier tissue chambers were created as three-dimensional configurations by resuspending cells in hydrogel (PGMatrix). We used cell lines to represent five of these organs (barrier lines-A549 [lung] and Caco2 [GI]) (nonbarrier lines-HepG2 C3A [liver], Meg01 [bone marrow], and HK2 [kidney]). The dimensions of our straight duct-like channels to each organ chamber were designed to provide the appropriate flow of a culture medium. The organ volumes and organ flow rates that have been reported for an average human male were used to estimate the desired fluid retention times in each organ chamber. The flow through the channels was induced by gravity on a custom programmed rocker platform which enabled pumpless operation and minimized bubble entrapment. The purpose of this paper is to describe the design and operation of a 14 chamber multi-organ system representing 13 tissues/organs with both barrier and nonbarrier tissue chambers and to study the interactive responses among the various cell lines. We demonstrate that five different cell lines survived with high viability (above 85%) for 7 days. We compared the individual observed flow rates to the compartments to the desired or estimated flow rates. This work demonstrates the feasibility of constructing, operating and maintaining a simple, gravity-driven, multi-organ microphysiological system with the capability of measuring cellular functions such as CYP1A1 and CYP3A4 activities, albumin release, urea, maintenance of tight junctions, and presence of surfactant for a sustained period. Biotechnol. Bioeng. 2016;113: 2213-2227. © 2016 Wiley Periodicals, Inc.


title: Self-Organization and Artificial Life: A Review

abstract: Self-organization has been an important concept within a number of disciplines, which Artificial Life (ALife) also has heavily utilized since its inception. The term and its implications, however, ...


title: A Bayesian Model for False Information Belief Impact, Optimal Design, and Fake News Containment

abstract: This work is a technical approach to modeling false information nature, design, belief impact and containment in multi-agent networks. We present a Bayesian mathematical model for source information and viewer's belief, and how the former impacts the latter in a media (network) of broadcasters and viewers. Given the proposed model, we study how a particular information (true or false) can be optimally designed into a report, so that on average it conveys the most amount of the original intended information to the viewers of the network. Consequently, the model allows us to study susceptibility of a particular group of viewers to false information, as a function of statistical metrics of the their prior beliefs (e.g. bias, hesitation, open-mindedness, credibility assessment etc.). In addition, based on the same model we can study false information"containment"strategies imposed by network administrators. Specifically, we study a credibility assessment strategy, where every disseminated report must be within a certain distance of the truth. We study the trade-off between false and true information-belief convergence using this scheme which leads to ways for optimally deciding how truth sensitive an information dissemination network should operate.


title: 1600 faults in 100 projects: automatically finding faults while achieving high coverage with EvoSuite

abstract: Automated unit test generation techniques traditionally follow one of two goals: Either they try to find violations of automated oracles (e.g., assertions, contracts, undeclared exceptions), or they aim to produce representative test suites (e.g., satisfying branch coverage) such that a developer can manually add test oracles. Search-based testing (SBST) has delivered promising results when it comes to achieving coverage, yet the use in conjunction with automated oracles has hardly been explored, and is generally hampered as SBST does not scale well when there are too many testing targets. In this paper we present a search-based approach to handle both objectives at the same time, implemented in the EvoSuite tool. An empirical study applying EvoSuite on 100 randomly selected open source software projects (the SF100 corpus) reveals that SBST has the unique advantage of being well suited to perform both traditional goals at the same time--efficiently triggering faults, while producing representative test sets for any chosen coverage criterion. In our study, EvoSuite detected twice as many failures in terms of undeclared exceptions as a traditional random testing approach, witnessing thousands of real faults in the 100 open source projects. Two out of every five classes with undeclared exceptions have actual faults, but these are buried within many failures that are caused by implicit preconditions. This "noise" can be interpreted as either a call for further research in improving automated oracles--or to make tools like EvoSuite an integral part of software development to enforce clean program interfaces.


title: Deal or No Deal? End-to-End Learning for Negotiation Dialogues

abstract: Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other's reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available (this https URL).


title: Cognitive synergy: A universal principle for feasible general intelligence

abstract: Do there exist general principles, which any system must obey in order to achieve advanced general intelligence using feasible computational resources? Here we propose one candidate: “cognitive synergy,” a principle which suggests that general intelligences must contain different knowledge creation mechanisms corresponding to different sorts of memory (declarative, procedural, sensory/episodic, attentional, intentional); and that these different mechanisms must be interconnected in such a way as to aid each other in overcoming memory-type-specific combinatorial explosions.


title: From total store order to sequential consistency: a practical reduction theorem

abstract: When verifying a concurrent program, it is usual to assume sequentially consistent memory. However, most modern multiprocessors buffer their stores, providing native sequential consistency only at a substantial performance penalty. To regain sequential consistency, a programmer has to follow an appropriate programming discipline. However, existing naive disciplines, such as protecting all shared accesses with locks to avoid data races, or flushing store buffers according to a protocol that allows arbitrary data races, are not flexible enough for building high-performance multiprocessor software. We present a new discipline for concurrent programming under TSO (total store order, with store buffer forwarding). Instead of using concurrency primitives, such as locks, it is based on maintaining ownership information in ghost state, allowing the discipline to be expressed as a state invariant and verified through conventional program reasoning. If every execution of a program in a system without store buffers follows the discipline, then every execution of the program in a system with store buffers is sequentially consistent.


An Algorithm for Automated Fractal Terrain Deformation


title: Photoplethysmography-based ambulatory heartbeat monitoring embedded into a dedicated bracelet

abstract: Ambulatory electrocardiogram (ECG) monitors have been intensely used since half century but are still associated to clinical/ambulatory cumbersome procedures. The question of this research is the following: what is the performance of a photoplethysmography (PPG)- based device located at the wrist in terms of heart rate variability (HRV) monitoring? PPG and ECG signals were recorded simultaneously on 4 subjects. Heart-beat (RR) intervals were estimated from both devices. For PPG signals, a multi-sensors approach based on the detection of local minima of the time-derivative was used to estimate RR time series. For ECG signals, an approach based on adaptive threshold was used (gold standard). The normalized differences observed on time-domain and frequency-domain HRV features were computed. Results based on 1565 minutes of recordings (N=94'000) showed an averaged correlation around 0:9 between the HRV features extracted from the PPG and ECG-based device. In view of these results, it appears that the wrist sensor opens the door towards a new generation of comfortable and easy-to-use cardiac HRV tool especially well adapted for long-term monitoring.


title: A linear MOS transconductor using source degeneration and adaptive biasing

abstract: This paper presents a new configuration for linear MOS voltage-to-current conversion (transconductance). The proposed circuit combines two previously reported linearization methods. The topology achieves 60-dB linearity for a fully balanced input dynamic range up to 1 V/sub pp/ at a 3.3-V supply voltage, with slightly decreasing performance in the unbalanced case. The linearity is preserved during the tuning process for a moderate range of transconductance values. The approach is validated by both computer simulations and experiments.


title: Social sharing through interpersonal media: Patterns and effects on emotional well-being

abstract: Social sharing is the act of communicating with others about significant emotional experiences.This is the first study to examine the use and effects of interpersonal media in social sharing.College students used the media for about 70% of their social sharing.They selected media whose affordances could meet their psychological needs.Social sharing through the media substantially impacted sharers' affect. Social sharing is the act of discussing significant emotional events with others. Using a daily diary methodology, this study investigated (1) patterns of media use for social sharing; and (2) effects of mediated social sharing on sharers' emotions. Results show that easily accessible and non-intrusive media (i.e., texting, Twitter) were more likely to be used for sharing positive than negative events, and intrusive and rich media (i.e., phone calling) were more likely to be used for sharing negative than positive events. Highly intense positive events were more likely to be shared via Twitter than low-intensity positive events, and highly intense negative events were more likely to be shared face-to-face than low-intensity negative events. Regardless of the medium used, people experienced increased positive affect after sharing positive events, and increased negative affect after sharing negative events. The results extend the social sharing framework, and advance the media use and effects literature.


title: Author ' s personal copy A case for stateful forwarding plane

abstract: In Named Data Networking (NDN), packets carry data names instead of source and destination addresses. This paradigm shift leads to a new network forwarding plane: data consumers send Interest packets to request desired data, routers forward Interest packets and maintain the state of all pending Interests, which is then used to guide Data packets back to the consumers. Maintaining the pending Interest state, together with the two-way Interest and Data exchange, enables NDN routers'forwarding process to measure performance of different paths, quickly detect failures and retry alternative paths. In this paper we describe an initial design of NDN's forwarding plane and evaluate its data delivery performance under adverse conditions. Our results show that this stateful forwarding plane can successfully circumvent prefix hijackers, avoid failed links, and utilize multiple paths to mitigate congestion. We also compare NDN's performance with that of IP-based solutions to highlight the advantages of a stateful forwarding plane.


Image-based 3 D digitizing for plant architecture analysis and phenotyping


title: The roles of vision and eye movements in the control of activities of daily living

abstract: The aim of this study was to determine the pattern of fixations during the performance of a well-learned task in a natural setting (making tea), and to classify the types of monitoring action that the eyes perform. We used a head-mounted eye-movement video camera, which provided a continuous view of the scene ahead, with a dot indicating foveal direction with an accuracy of about 1 deg. A second video camera recorded the subject's activities from across the room. The videos were linked and analysed frame by frame. Foveal direction was always close to the object being manipulated, and very few fixations were irrelevant to the task. The first object-related fixation typically led the first indication of manipulation by 0.56 s, and vision moved to the next object about 0.61 s before manipulation of the previous object was complete. Each object-related act that did not involve a waiting period lasted an average of 3.3 s and involved about 7 fixations. Roughly a third of all fixations on objects could be defin...


title: Using a HHD with a HMD for mobile AR interaction

abstract: Mobile Augmented Reality (AR) applications are typically deployed either on head mounted displays (HMD) or handheld displays (HHD). This paper explores novel interaction techniques for a combined HHD-HMD hybrid system that builds on the strengths of each type of device. We use the HMD for viewing AR content and a touch screen HHD for interacting with the content. A prototype system was developed and a user study was conducted comparing four interaction techniques for selection tasks.


title: Temporal logic control in dynamic environments with probabilistic satisfaction guarantees

abstract: Mobile robotic systems move in environments that are constantly changing due to the presence of dynamic obstacles. In this work we consider one such environment in which the dynamic nature comes from doors that can open or close during the robot's mission. We derive a solution to the automatic deployment of a robot from a temporal logic specification assuming three different levels of knowledge and sensing capabilities of the robot. Under each one of these settings, the motion of the robot is modeled either as a Markov decision process (MDP) or mixed observability MDP. The objective is to find a control strategy that maximizes the probability of satisfying a specification given in Probabilistic Computation Tree Logic (PCTL). We describe an optimal solution in one setting and sub-optimal, reactive solutions in the other two. We illustrate our methods with simulation results.


title: Fallen Person Detection for Mobile Robots Using 3D Depth Data

abstract: Falling down and not managing to get up again is one of the main concerns of elderly people living alone in their home. Robotic assistance for the elderly promises to have a great potential of detecting these critical situations and calling for help. This paper presents a feature-based method to detect fallen people on the ground by a mobile robot equipped with a Kinect sensor. Point clouds are segmented, layered and classified to detect fallen people, even under occlusions by parts of their body or furniture. Different features, originally from pedestrian and object detection in depth data, and different classifiers are evaluated. Evaluation was done using data of 12 people lying on the floor. Negative samples were collected from objects similar to persons, two tall dogs, and five real apartments of elderly people. The best feature-classifier combination is selected to built a robust system to detect fallen people.


title: Abstract BGP-RCN: improving BGP convergence through root cause notification q

abstract: This paper presents a new mechanism, called BGP with root cause notification (BGP-RCN), that provides an upper bound of O(d) on routing convergence delay for BGP, where d is the network diameter as measured by the number of AS hops. BGP-RCN lets each routing update message carry the information about the specific cause which triggered the update message. Once a node v receives the first update message triggered by a link failure, v can avoid using any paths that have been obsoleted by the same failure. The basic approach in BGP-RCN is applicable to path vector routing protocols in general. Our analysis and simulation show that BGP-RCN can achieve substantial reduction in both BGP convergence time and the total number of intermediate route changes.


title: Privacy Concerns and Behaviors of People with Visual Impairments

abstract: Various technologies have been developed to help make the world more accessible to visually impaired people, and recent advances in low-cost wearable and mobile computing are likely to drive even moreadvances. However, the unique privacy and security needs of visually impaired people remain largely unaddressed. We conducted an exploratory user study with 14 visually impaired participants to understand the techniques they currently use for protecting privacy, their remaining privacy concerns,and how new technologies may be able to help. The interviews explored privacy not only in the physical world (e.g., bystanders overhearing private conversations) and the online world (e.g., determining if a URL is legitimate), but also in the interface between the two (e.g. bystanders `shoulder-surfing' data from screens). The study revealed serious concerns that are not adequately solved by current technology, and suggested new directions for improving the privacy of this significant fraction of the population.


Word Embedding Calculus in Meaningful Ultradense Subspaces


title: A Comparison and Analysis of Name Matching Algorithms

abstract: Names are important in many societies, even in technologically oriented ones which use e.g. ID systems to identify individual people. Names such as surnames are the most important as they are used in many processes, such as identifying of people and genealogical research. On the other hand variation of names can be a major problem for the identification and search for people, e.g. web search or security reasons. Name matching presumes a-priori that the recorded name written in one alphabet reflects the phonetic identity of two samples or some transcription error in copying a previously recorded name. We add to this the lode that the two names imply the same person. This paper describes name variations and some basic description of various name matching algorithms developed to overcome name variation and to find reasonable variants of names which can be used to further increasing mismatches for record linkage and name search. The implementation contains algorithms for computing a range of fuzzy matching based on different types of algorithms, e.g. composite and hybrid methods and allowing us to test and measure algorithms for accuracy. NYSIIS, LIG2 and Phonex have been shown to perform well and provided sufficient flexibility to be included in the linkage/matching process for optimising name searching. Keywords—Data mining, name matching algorithm, nominal data, searching system.


title: Image-processing projects for an algorithms course

abstract: Courses on algorithm analysis often include little programming, and do not emphasize application of advanced techniques to practical problems. On the other hand, students usually prefer hands-on learning, and may lack motivation to study theory. We augmented an algorithms course with a series of programming tasks, which involved application of the course material to image processing. These tasks motivated the students, and led to better understanding and retention of theoretical material. They also enabled the students to learn the basics of representing and manipulating images, along with the algorithm theory.


title: Modeling of arc welding power supply

abstract: This paper gives a method of establishing small-signal model for full-bridge inverter arc welding power supply via state-space averaging treatment and linearization method. On the basis of the model, the frequency response reflecting dynamical characteristics of arc welding power supply is analyzed by means of MATLAB. The simulation result has agreement with experiment result as regards dynamical property. The small signal frequent mathematical model established in the paper can reflect basic characteristic of practical arc welding power system.


title: CarWeb: A Traffic Data Collection Platform

abstract: In this paper, we exploit the concept of sharing GPS data for estimate traffic information. Explicitly, we implemented CarWeb, a platform to collect real-time GPS data from cars. Once receiving a sufficient amount of GPS data from cars, we are able to estimate traffic information (i.e., speed information of roads). As such, users are able to obtain the update-to-date traffic information in CarWeb platform. In addition, we propose two algorithms to estimate traffic information with GPS data given. A prototype of CarWeb is implemented and functionalities of each component in CarWeb are demonstrated in this paper.


title: RGBD Salient Object Detection via Deep Fusion

abstract: Numerous efforts have been made to design various low-level saliency cues for RGBD saliency detection, such as color and depth contrast features as well as background and color compactness priors. However, how these low-level saliency cues interact with each other and how they can be effectively incorporated to generate a master saliency map remain challenging problems. In this paper, we design a new convolutional neural network (CNN) to automatically learn the interaction mechanism for RGBD salient object detection. In contrast to existing works, in which raw image pixels are fed directly to the CNN, the proposed method takes advantage of the knowledge obtained in traditional saliency detection by adopting various flexible and interpretable saliency feature vectors as inputs. This guides the CNN to learn a combination of existing features to predict saliency more effectively, which presents a less complex problem than operating on the pixels directly. We then integrate a superpixel-based Laplacian propagation framework with the trained CNN to extract a spatially consistent saliency map by exploiting the intrinsic structure of the input image. Extensive quantitative and qualitative experimental evaluations on three data sets demonstrate that the proposed method consistently outperforms the state-of-the-art methods.


title: Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal

abstract: The time-domain signals representing the heart rate variability (HRV) in the presence of an ectopic beat exhibit a sharp transient at the position of the ectopic beat, which corrupts the signal, particularly the power spectral density (PSD) of the HRV. Consequently, there is a need for correction of this type of beat prior to any HRV analysis. This paper deals with the PSD estimation of the HRV by means of the heart timing (HT) signal when ectopic beats are present. These beat occurrence times are modeled from a generalized, continuous time integral pulse frequency modulation model and, from this point of view, a specific method for minimizing the effect of the presence of ectopic beats is presented to work together with the HT signal. By using both, a white noise driven autoregressive model of the HRV signal with artificially introduced ectopic beats and actual heart rate series including ectopic beats, the more usual methods of HRV spectral estimation are compared. Results of the PSD estimation error function of the number of ectopic beats are presented. These results demonstrate that the proposed method has one order of magnitude lower error than usual ectopic beats removal strategies in preserving PSD, thus, this strategy better recovers the original clinical indexes of interest.


title: Motivations for Public Service in Corrupt States: Evidence from Post-Soviet Russia

abstract: Throughout much of the world, corruption in the civil service undermines state capacity, impedes economic development, and saps citizens’ morale. But while its pernicious effects are widely recognized, the roots of corruption remain poorly understood. Whereas most studies on corruption’s origins focus on the incentives bureaucrats face once in office, this study contributes to a line of recently emerging research that considers the role of self-selection of citizens with a propensity for corruption into bureaucracies where corruption is known to be widespread. Drawing on a survey and experimental games conducted with students at an elite university in Moscow, Russia, we compare the attitudinal, behavioral, and demographic traits of students seeking public sector employment to the traits of their peers seeking jobs in the private sector. Contrary to studies conducted in other high-corruption contexts, such as India, we find surprising evidence that students who prefer a public sector career display lss willingness to cheat or bribe in experimental games as well as higher levels of altruism. One interpretation of these findings is that corruption in Russia results from the transformation of bureaucrats’ behavior and attitudes after entering the civil service, rather than through a process of corrupt self-selection


title: A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models

abstract: This paper addresses a data-driven prognostics method for the estimation of the Remaining Useful Life (RUL) and the associated confidence value of bearings. The proposed method is based on the utilization of the Wavelet Packet Decomposition (WPD) technique, and the Mixture of Gaussians Hidden Markov Models (MoG-HMM). The method relies on two phases: an off-line phase, and an on-line phase. During the first phase, the raw data provided by the sensors are first processed to extract features in the form of WPD coefficients. The extracted features are then fed to dedicated learning algorithms to estimate the parameters of a corresponding MoG-HMM, which best fits the degradation phenomenon. The generated model is exploited during the second phase to continuously assess the current health state of the physical component, and to estimate its RUL value with the associated confidence. The developed method is tested on benchmark data taken from the “NASA prognostics data repository” related to several experiments of failures on bearings done under different operating conditions. Furthermore, the method is compared to traditional time-feature prognostics and simulation results are given at the end of the paper. The results of the developed prognostics method, particularly the estimation of the RUL, can help improving the availability, reliability, and security while reducing the maintenance costs. Indeed, the RUL and associated confidence value are relevant information which can be used to take appropriate maintenance and exploitation decisions. In practice, this information may help the maintainers to prepare the necessary material and human resources before the occurrence of a failure. Thus, the traditional maintenance policies involving corrective and preventive maintenance can be replaced by condition based maintenance.


title: Cubical Marching Squares: Adaptive Feature Preserving Surface Extraction from Volume

abstract: In this paper, we present a new method for surface extraction from volume data which preserves sharp features, maintains consistent topology and generates surface adaptively without crack patching. Our approach is based on the marching cubes algorithm, a popular method to convert volumetric data to polygonal meshes. The original marching cubes algorithm suffers from problems of topological inconsistency, cracks in adaptive resolution and inability to preserve sharp features. Most of marching cubes variants only focus on one or some of these problems. Although these techniques could be combined to solve these problems altogether, such a combination might not be straightforward. Moreover, some feature-preserving variants introduce an additional problem, inter-cell dependency. Our method provides a relatively simple and easy-to-implement solution to all these problems by converting 3D marching cubes into 2D cubical marching squares, resolving topology ambiguity with sharp features and eliminating inter-cell dependency by sampling face sharp features. We compare our algorithm with other marching cubes variants and demonstrate its effectiveness on various applications.


title: A bayesian approach to model checking biological systems

abstract: Recently, there has been considerable interest in the use of Model Checking for Systems Biology. Unfortunately, the state space of stochastic biological models is often too large for classical Model Checking techniques. For these models, a statistical approach to Model Checking has been shown to be an effective alternative. Extending our earlier work, we present the first algorithm for performing statistical Model Checking using Bayesian Sequential Hypothesis Testing. We show that our Bayesian approach outperforms current statistical Model Checking techniques, which rely on tests from Classical (aka Frequentist) statistics, by requiring fewer system simulations. Another advantage of our approach is the ability to incorporate prior Biological knowledge about the model being verified. We demonstrate our algorithm on a variety of models from the Systems Biology literature and show that it enables faster verification than state-of-the-art techniques, even when no prior knowledge is available.


title: Low-power acoustic modem for dense underwater sensor networks

abstract: Significant progress has been made in terrestrial sensor networks to revolutionize sensing and data collection. To bring the concept of long-lived, dense sensor networks to the underwater environment, there is a compelling need to develop low-cost and low-power acoustic modems for short-range communications. This paper presents our work in designing and developing such a modem. We describe our design rationale followed by details of both hardware and software development. We have performed preliminary tests with transducers for in-air communications.


title: A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions ∗

abstract: In recent years, there have been some interesting studies on predictive modeling in data streams. However, most such studies assume relatively balanced and stable data streams but cannot handle well rather skewed (e.g., few positives but lots of negatives) and stochastic distributions, which are typical in many data stream applications. In this paper, we propose a new approach to mine data streams by estimating reliable posterior probabilities using an ensemble of models to match the distribution over under-samples of negatives and repeated samples of positives. We formally show some interesting and important properties of the proposed framework, e.g., reliability of estimated probabilities on skewed positive class, accuracy of estimated probabilities, efficiency and scalability. Experiments are performed on several synthetic as well as real-world datasets with skewed distributions, and they demonstrate that our framework has substantial advantages over existing approaches in estimation reliability and predication accuracy.


title: Distraction by a monotube fixator to achieve limb lengthening: predictive factors for tibia trauma

abstract: BackgroundManagement of post trauma tibia bone gap varied with orthopedic surgeons’ experience and tools available. Study aims to determine predictive factors for distraction by a monotube fixator (DMF) outcome in post tibia trauma limb length discrepancy.MethodsA prospective descriptive cross sectional study of post traumatized tibia bone gap and limb length discrepancy patients at tertiary hospitals. Patient’s informed consent and institutional ethical committee approval were obtained. Bio-data, clinical and healing indexes were documented. DMF was applied for patient that met inclusion criteria. The Statistic tests used included the Chi-square, the Student’s two-tailed t test, and the Wilcox on rank-sum test when appropriate. Mantel-Haenszel Common Odds Ratio (OR) and 95% confidence intervals for poor outcome potential risk factors were recorded. Bivariate correlation and logistic regression were evaluated. Significance level was set at a p value <0.05.ResultsThirty-six patients with mean age, 37.2 ± 10.3 year and male/female ratio of 1:1.25 had DMF applied. Motorcycle accident accounted for 50.0% of patients and diaphyseal segment was most commonly affected 25 (69.4%). The mean bone lengthened was 10.1 ± 4.0 cm (range: 5-21 cm) and mean duration of bone transport was 105.6 ± 38.2 days. The means of rate of distraction, healing index and percentage of lengthening were 0.99 ± 0.14 mm/day, 15.6 ± 4.3 days/cm and 38.0 ± 14.3 respectively. The mean follow up was 9.7 ±4.9 months (range: 2–17.0). Per operative complications varied and outcome was satisfactory in 30 (83.3%). Obesity (p <0.0001), multiple surgery (p = 0.012) and transfusion (p = 0.001) correlated to poor outcome. Percentage lengthening ≥ 50%, bone gap >10 cm, anemia, blood transfusion, general anesthesia administration, distraction rate >1 mm/day, osteomyelitis and prolong partial weight bearing were significant predictive factors for poor outcome in post traumatic tibia distraction.ConclusionDistraction by a monotube fixator appears effective in achieving correction >38.0% original tibia lengthening following traumatic bone gap. Predictive factors for poor outcome were useful for prognostication.


title: AC 2011-609: LEARNING ROBOTICS THROUGH DEVELOPING A VIR- TUAL ROBOT SIMULATOR IN MATLAB

abstract: Due to the expensive nature of an industrial robot, not all universities are equipped with areal robots for students to operate. Learning robotics without accessing to an actual robotic system has proven to be difficult for undergraduate students. For instructors, it is also an obstacle to effectively teach fundamental robotic concepts. Virtual robot simulator has been explored by many researchers to create a virtual environment for teaching and learning. This paper presents structure of a course project which requires students to develop a virtual robot simulator. The simulator integrates concept of kinematics, inverse kinematics and controls. Results show that this approach assists and promotes better students‟ understanding of robotics.


title: Brief report: developmental change in theory of mind abilities in children with autism.

abstract: This longitudinal study investigated developmental change in theory of mind among 57 children with autism aged between 4 to 14 years at the start of the study. On an initial visit and one year later, each participant was administered a battery of tests designed to measure a broad developmental range of theory of mind abilities from early (e.g., desire) to more advanced (e.g., moral judgment) mental state understanding. Both group and individual data indicated significant developmental improvement in theory of mind ability, which was primarily related to the children's language abilities.


title: Trilinear tensor: The fundamental construct of multiple-view geometry and its applications

abstract: The topic of representation, recovery and manipulation of three-dimensional (3D) scenes from two-dimensional (2D) images thereof, provides a fertile ground for both intellectual theoretically inclined questions related to the algebra and geometry of the problem and to practical applications such as Visual Recognition, Animation and View Synthesis, recovery of scene structure and camera ego-motion, object detection and tracking, multi-sensor alignment, etc.


title: A Systematic Review of Mechanisms of Change in Body-Oriented Yoga in Major Depressive Disorders

abstract: Introduction Despite empirical evidence for the efficacy of body-oriented yoga as add-on treatment for major depressive disorder (MDD), the specific mechanisms by which yoga leads to therapeutic changes remain unclear. By means of a systematic review, we evaluate how the field is progressing in its empirical investigation of mechanisms of change in yoga for MDD. Methods To identify relevant studies, a systematic search was conducted. Results The search produced 441 articles, of which 5 were included, that empirically examined 2 psychological mechanisms (mindfulness, rumination) and 3 biological mechanisms (vagal control, heart rate variability [HRV], brain-derived neurotrophic factor [BDNF], cortisol). 2 studies found that decreased rumination and 1 study that increased mindfulness was associated with the effect of yoga on treatment outcome. In addition, preliminary studies suggest that alterations in cortisol, BDNF, and HRV may play a role in how yoga exerts its clinical effect. Discussion The results suggest that body-oriented yoga could work through some of the theoretically predicted mechanisms. However, there is a need for more rigorous designs that can assess greater levels of causal specificity.


title: Design and Implementation of a Virtual Reality System and Its Application to Training Medical First Responders

abstract: This paper presents the design and implementation of a distributed virtual reality (VR) platform that was developed to support the training of multiple users who must perform complex tasks in which situation assessment and critical thinking are the primary components of success. The system is fully immersive and multimodal, and users are represented as tracked, full-body figures. The system supports the manipulation of virtual objects, allowing users to act upon the environment in a natural manner. The underlying intelligent simulation component creates an interactive, responsive world in which the consequences of such actions are presented within a realistic, time-critical scenario. The focus of this work has been on the training of medical emergency-response personnel. BioSimMER, an application of the system to training first responders to an act of bio-terrorism, has been implemented and is presented throughout the paper as a concrete example of how the underlying platform architecture supports complex training tasks. Finally, a preliminary field study was performed at the Texas Engineering Extension Service Fire Protection Training Division. The study focused on individual, rather than team, interaction with the system and was designed to gauge user acceptance of VR as a training tool. The results of this study are presented.


title: Info-fuzzy algorithms for mining dynamic data streams

abstract: Most data-mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by dynamic processes, which may change over time, in some cases even drastically. The change in the underlying concept, also known as concept drift, causes the data-mining model generated from past examples to become less accurate and relevant for classifying the current data. Most online learning algorithms deal with concept drift by generating a new model every time a concept drift is detected. On one hand, this solution ensures accurate and relevant models at all times, thus implying an increase in the classification accuracy. On the other hand, this approach suffers from a major drawback, which is the high computational cost of generating new models. The problem is getting worse when a concept drift is detected more frequently and, hence, a compromise in terms of computational effort and accuracy is needed. This work describes a series of incremental algorithms that are shown empirically to produce more accurate classification models than the batch algorithms in the presence of a concept drift while being computationally cheaper than existing incremental methods. The proposed incremental algorithms are based on an advanced decision-tree learning methodology called ''Info-Fuzzy Network'' (IFN), which is capable to induce compact and accurate classification models. The algorithms are evaluated on real-world streams of traffic and intrusion-detection data.


title: Nordhaus-Gaddum-Type Results for Resistance Distance-Based Graph Invariants

abstract: Abstract Two decades ago, resistance distance was introduced to characterize “chemical distance” in (molecular) graphs. In this paper, we consider three resistance distance-based graph invariants, namely, the Kirchhoff index, the additive degree-Kirchhoff index, and the multiplicative degree-Kirchhoff index. Some Nordhaus-Gaddum-type results for these three molecular structure descriptors are obtained. In addition, a relation between these Kirchhoffian indices is established.


title: Diagnosis, management and treatment of orbital and periorbital cellulitis in children

abstract: Children with red swollen eyes frequently present to emergency departments. Some patients will have orbital cellulitis, a condition that requires immediate diagnosis and treatment. Orbital cellulitis can be confused with the less severe, but more frequently encountered, periorbital cellulitis, which requires less aggressive management. Delayed recognition of the signs and symptoms of orbital cellulitis can lead to serious complications such as blindness, meningitis and cerebral abscess. This article describes the clinical features, epidemiology and outcomes of the condition, and discusses management and treatment. It also includes a case study.


title: Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM

abstract: We propose a new task in the field of computational argumentation in which we investigate qualitative properties of Web arguments, namely their convincingness. We cast the problem as relation classification, where a pair of arguments having the same stance to the same prompt is judged. We annotate a large datasets of 16k pairs of arguments over 32 topics and investigate whether the relation “A is more convincing than B” exhibits properties of total ordering; these findings are used as global constraints for cleaning the crowdsourced data. We propose two tasks: (1) predicting which argument from an argument pair is more convincing and (2) ranking all arguments to the topic based on their convincingness. We experiment with feature-rich SVM and bidirectional LSTM and obtain 0.76-0.78 accuracy and 0.35-0.40 Spearman’s correlation in a cross-topic evaluation. We release the newly created corpus UKPConvArg1 and the experimental software under open licenses.


title: Specification of Guaranteed Quality of Service

abstract: This memo describes the network element behavior required to deliver a guaranteed service (guaranteed delay and bandwidth) in the Internet. Guaranteed service provides firm (mathematically provable) bounds on end-to-end datagram queueing delays. This service makes it possible to provide a service that guarantees both delay and bandwidth. This specification follows the service specification template described in [1].


title: Dependence maps, a dimensionality reduction with dependence distance for high-dimensional data

abstract: We introduce the dependence distance, a new notion of the intrinsic distance between points, derived as a pointwise extension of statistical dependence measures between variables. We then introduce a dimension reduction procedure for preserving this distance, which we call the dependence map. We explore its theoretical justification, connection to other methods, and empirical behavior on real data sets.


title: Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry

abstract: Abstract This paper is addressed to readers without advanced knowledge of remote sensing. It illustrates some current and potential uses of satellite Synthetic Aperture Radar interferometry (InSAR) for landslide assessment. Data acquired by SAR systems can provide 3D terrain models and be used to assist in regional scale investigations, e.g. aimed at evaluation of susceptibility of slopes to failure. Under favourable environmental conditions, the innovative Permanent Scatterers (PS) technique, which overcomes several limitations of conventional SAR differential interferometry (DInSAR) applications in landslide studies, is suitable for monitoring slope deformations with millimetric precision. The PS technique combines the wide-area coverage typical of satellite imagery with the capability of providing displacement data relative to individual image pixels. With the currently available radar satellites, however, only very slow ground surface displacements can be reliably detected and measured. The presented case study of a landslide from the Liechtenstein Alps indicates that the most attractive and reliable contribution provided by this remote sensing technique lies in the possibility of (i.) wide-area qualitative distinction between stable and unstable areas and (ii.) qualitative (relative) hazard zonation of large, slow landslides based on the identification of segments characterised by different movement rates. Since only the radar line of sight projection of the displacements can be detected, a quantitative exploitation of the PS data is possible only where sufficient ground truth is available. In site specific or single landslide investigations the PS data can represent a very useful complementary data source with respect to the information acquired through ground based observations and in situ surveying. However, the difficulties associated with the feasibility assessments of the applicability of SAR data to local scale problems, as well as with the interpretation of PS results, require a close collaboration between landslide experts and specialists in advanced processing of radar satellite data. The interpretation of the exact geotechnical significance of small, radar sensed ground surface deformations is challenging, especially where ground truth is lacking. Although any ground deformation is potentially of interest to an engineering geologist, detection of movements in both vertical and horizontal directions is needed in the case of landslides to evaluate slope failure mechanisms. With their high radar viewing angles, however, the current space-borne systems can detect only a fraction of the horizontal component of movement. It is expected that the upcoming SAR dedicated missions with new sensors and different acquisition geometries, combined with the rapid developments in the field of advanced radar data processing, will allow a full 3D reconstruction of deformation data and help to further reduce the current limitations of the PS and similar DInSAR approaches.


title: Fine pitch 3D interconnections with hybrid bonding technology: From process robustness to reliability

abstract: This paper presents the description of direct hybrid bonding technology for the fabrication of vertical interconnects thanks to wafer-to-wafer bonding. Process robustness is analyzed through morphological and electrical results. The electrical characterizations are discussed versus hybrid bonding pad dimensions and pitches. Electromigration study is carried out on different test vehicles with hybrid bonding interconnect dimensions below 5 μm. Experimental tests provide the parameters for lifetime extrapolation and show that the hybrid bonding module is immune to electromigration failures. Finally perspectives and key challenges for 3D interconnects scalability are given.


Learning from Semi-Supervised Weak-Label Data


title: Software Code Generator in Automotive Field

abstract: Rapid development of new technology has resulted changes in IT world affecting the way we work. Software use has increased in different fields of technology. In automation field, for example, the usage of Electronics Control Unit (ECU) has increased resulting in a different size of software. Due to persistent role of software in technology, the software cost and quality has become an important field. In automation field a number of tools are being developed which help to generate software codes. These tools can be used in software cost reduction because of reuse of software modules. In automatic code generation, the size estimates can hide the true effort of a program. In this paper we discuss the effect of using code generators in automation sector and how these tools have an effect on the cost of the software. We will compare the effort of code written manually and by using a code generator.


SmartSLAM - An Efficient Smartphone Indoor Positioning System Exploiting Machine Learning and Opportunistic Sensing


title: A Theory of Regular Queries

abstract: A major theme in relational database theory is navigating the tradeoff between expressiveness and tractability for query languages, where the query-containment problem is considered a benchmark of tractability. The query class UCQ, consisting off unions of conjunctive queries, is a fragment of first-order logic that has a decidable query containment problem, but its expressiveness is limited. Extending UCQ with recursion yields Datalog, an expressive query language that has been studied extensively and has recently become popular in application areas such as declarative networking. Unfortunately, Datalog has an undecidable query containment problem. Identifying a fragment of Datalog that is expressive enough for applications but has a decidable query-containment problem has been an open problem for several years. In the area of graph databases, there has been a similar search for a query language that combines expressiveness and tractability. Because of the need to navigate along graph paths of unspecified length, transitive closure has been considered a fundamental operation. Query classes of increasing complexity -- using the operations of disjunction, conjunction, projection, and transitive closure -- have been studied, but the classes lacked natural closure properties. The class RQ of regular queries has emerged only recently as a natural query class that is closed under all of its operations and has a decidable query-containment problem. RQ turned out to be a fragment of Datalog where recursion can be used only to express transitive closure. Furthermore, it turns out that applying this idea to Datalog, that is, restricting recursion to the expression of transitive closure, does yield the long-sought goal -- an expressive fragment of Datalog with a decidable query-optimization problem.


title: Discrete Single Vs. Multiple Stream HMMs: A Comparative Evaluation of Their Use

abstract: In this paper we study the influence of quantization on the features used in on-line handwriting recognition in order to apply discrete single and multiple stream HMMs. It turns out that the separation of the features in statisticaly independent streams influences the performance of the recognition system: using the discrete “pressure” feature as an example, we show that the statistical dependencies between the features are as important as their proper quantization. In an experimental section we show that a continuous state-of-the-art system for on-line handwritten whiteboard note recognition can be outperformed by r = 2.0 % relative in word level accuracy using a three stream discrete HMM system with well chosen streams. A relative improvement of r = 4.5 % can be achieved when comparing a single stream HMM system with the best performing mutliple stream HMM system.


title: Angiogenic imbalance in the pathophysiology of preeclampsia: newer insights.

abstract: Angiogenesis is the process of neovascularization from preexisting blood vessels, whereas vasculogenesis is the process of blood vessel generation from angioblast precursor cells. The human placenta undergoes high levels of angiogenesis and vasculogenesis during fetal development. Additionally, the placenta undergoes a process of vascular mimicry (also referred to as pseudovasculogenesis ) in which the placental cytotrophoblasts convert from an epithelial to an endothelial phenotype during normal fetal development. Failure of placental angiogenesis and pseudovasculogenesis during placental development has been linked to the pathogenesis of preeclampsia. It currently is believed that soluble factors released by the diseased placenta lead to clinical findings of preeclampsia. This article discusses placental vascular development in health and in disease, with a focus on accumulating recent evidence that the maternal clinical syndrome of preeclampsia is an antiangiogenic state resulting from an excess of anti-endothelial factors liberated by the diseased placenta.


title: Overview of SHVC: Scalable Extensions of the High Efficiency Video Coding Standard

abstract: This paper provides an overview of Scalable High efficiency Video Coding (SHVC), the scalable extensions of the High Efficiency Video Coding (HEVC) standard, published in the second version of HEVC. In addition to the temporal scalability already provided by the first version of HEVC, SHVC further provides spatial, signal-to-noise ratio, bit depth, and color gamut scalability functionalities, as well as combinations of any of these. The SHVC architecture design enables SHVC implementations to be built using multiple repurposed single-layer HEVC codec cores, with the addition of interlayer reference picture processing modules. The general multilayer high-level syntax design common to all multilayer HEVC extensions, including SHVC, MV-HEVC, and 3D HEVC, is described. The interlayer reference picture processing modules, including texture and motion resampling and color mapping, are also described. Performance comparisons are provided for SHVC versus simulcast HEVC and versus the scalable video coding extension to H.264/advanced video coding.


title: Dynamic question generation system for web-based testing using particle swarm optimization

abstract: One aim of testing is to identify weaknesses in students' knowledge. Computerized tests are now one of the most important ways to judge learning, and, selecting tailored questions for each learner is a significant part of such tests. Therefore, one current trend is that computerized adaptive tests (CATs) not only assist teachers in estimating the learning performance of students, but also facilitate understanding of problems in their learning process. These tests, must effectively and efficiently select questions from a large-scale item bank, and to cope with this problem we propose a dynamic question generation system for web-based tests using the novel approach of particle swarm optimization (PSO). The dynamic question generation system is built to select tailored questions for each learner from the item bank to satisfy multiple assessment requirements. Furthermore, the proposed approach is able to efficiently generate near-optimal questions that satisfy multiple assessment criteria. With a series of experiments, we compare the efficiency and efficacy of the PSO approach with other approaches. The experimental results show that the PSO approach is suitable for the selection of near-optimal questions from large-scale item banks.


title: Cooperative Look-Ahead Control for Fuel-Efficient and Safe Heavy-Duty Vehicle Platooning

abstract: The operation of groups of heavy-duty vehicles (HDVs) at a small inter-vehicular distance (known as platoon) allows to lower the overall aerodynamic drag and, therefore, to reduce fuel consumption and greenhouse gas emissions. However, due to the large mass and limited engine power of HDVs, slopes have a significant impact on the feasible and optimal speed profiles that each vehicle can and should follow. Therefore maintaining a short inter-vehicular distance as required by platooning without coordination between vehicles can often result in inefficient or even unfeasible trajectories. In this paper we propose a two-layer control architecture for HDV platooning aimed to safely and fuel-efficiently coordinate the vehicles in the platoon. Here, the layers are responsible for the inclusion of preview information on road topography and the real-time control of the vehicles, respectively. Within this architecture, dynamic programming is used to compute the fuel-optimal speed profile for the entire platoon and a distributed model predictive control framework is developed for the real-time control of the vehicles. The effectiveness of the proposed controller is analyzed by means of simulations of several realistic scenarios that suggest a possible fuel saving of up to 12% for the follower vehicles compared to the use of standard platoon controllers.


title: Detecting Humans in Dense Crowds Using Locally-Consistent Scale Prior and Global Occlusion Reasoning

abstract: Human detection in dense crowds is an important problem, as it is a prerequisite to many other visual tasks, such as tracking, counting, action recognition or anomaly detection in behaviors exhibited by individuals in a dense crowd. This problem is challenging due to the large number of individuals, small apparent size, severe occlusions and perspective distortion. However, crowded scenes also offer contextual constraints that can be used to tackle these challenges. In this paper, we explore context for human detection in dense crowds in the form of a locally-consistent scale prior which captures the similarity in scale in local neighborhoods and its smooth variation over the image. Using the scale and confidence of detections obtained from an underlying human detector, we infer scale and confidence priors using Markov Random Field. In an iterative mechanism, the confidences of detection hypotheses are modified to reflect consistency with the inferred priors, and the priors are updated based on the new detections. The final set of detections obtained are then reasoned for occlusion using Binary Integer Programming where overlaps and relations between parts of individuals are encoded as linear constraints. Both human detection and occlusion reasoning in proposed approach are solved with local neighbor-dependent constraints, thereby respecting the inter-dependence between individuals characteristic to dense crowd analysis. In addition, we propose a mechanism to detect different combinations of body parts without requiring annotations for individual combinations. We performed experiments on a new and extremely challenging dataset of dense crowd images showing marked improvement over the underlying human detector.


title: Exploiting Shallow Linguistic Information For Relation Extraction From Biomedical Literature

abstract: We propose an approach for extracting relations between entities from biomedical literature based solely on shallow linguistic information. We use a combination of kernel functions to integrate two different information sources: (i) the whole sentence where the relation appears, and (ii) the local contexts around the interacting entities. We performed experiments on extracting gene and protein interactions from two different data sets. The results show that our approach outperforms most of the previous methods based on syntactic and semantic information.


title: All digital phase interpolator

abstract: This paper proposes an all digital CMOS phase interpolator suitable for high-speed multi-Gigabit serial interfaces. The topology is based on the parallel combination of identical CMOS inverters grouped in eight segments and delivers two programmable orthogonal output phases (I/Q). The phase interpolator is designed to be compliant with MIPI alliance M-PHY standard in a 65nm CMOS process. Simulation results confirm 5-bit phase resolution with less than 5% worst case phase step variation, settling time less than 2 clock cycles and power consumption about 2mW from 1.2V voltage supply.


title: Validating a Deep Learning Framework by Metamorphic Testing

abstract: Deep learning has become an important tool for image classification and natural language processing. However, the effectiveness of deep learning is highly dependent on the quality of the training data as well as the net model for the learning. The training data set for deep learning normally is fairly large, and the net model is pretty complex. It is necessary to validate the deep learning framework including the net model, executing environment, and training data set before it is used for any applications. In this paper, we propose an approach for validating the classification accuracy of a deep learning framework that includes a convolutional neural network, a deep learning executing environment, and a massive image data set. The framework is first validated with a classifier built on support vector machine, and then it is tested using a metamorphic validation approach. The effectiveness of the approach is demonstrated by validating a deep learning classifier for automated classification of biology cell images. The proposed approach can be used for validating other deep learning framework for different applications.


title: Statistical Methods for Fighting Financial Crimes

abstract: Financial crimes affect millions of people every year and financial institutions must employ methods to protect themselves and their customers. The use of statistical methods to address these problems faces many challenges. Financial crimes are rare events that lead to extreme class imbalances. Criminals deliberately attempt to conceal the nature of their actions and quickly change their strategies over time, resulting in class overlap and concept drift. In some cases, legal constraints and investigation delays make it impossible to actually verify suspected crimes in a timely manner, resulting in class mislabeling or unknown labels. In addition, the volume and complexity of financial data require algorithms to be not only effective, but also efficiently trained and executed. This article focuses on two important types of financial crimes: fraud and money laundering. It discusses some of the traditional statistical techniques that have been applied as well as more recent machine learning and data mining a...


The Effects of Performance Measurement Uses on Organizational Ambidexterity and Company Performance


title: Mesoscopic representation of texts as complex networks

abstract: Texts are complex structures emerging from an intricate system consisting of syntactical constraints and semantical relationships. While the complete modeling of such structures is impractical owing to the high level of complexity inherent to linguistic constructions, under a limited domain, certain tasks can still be performed. Recently, statistical techniques aiming at analysis of texts, referred to as text analytics, have departed from the use of simple word count statistics towards a new paradigm. Text mining now hinges on a more sophisticate set of methods, including the representation of texts as complex networks. In this perspective, networks represent a set of textual elements, typically words; and links are established via adjacency relationships. While current word-adjacency (co-occurrence) methods successfully grasp syntactical and stylistic features of written texts, they are unable to represent important aspects of textual data, such as its topical structure. As a consequence, the mesoscopic structure of texts is often overlooked by current methodologies. In order to grasp mesoscopic characteristics of semantical content in written texts, we devised a network approach which is able to analyze documents in a multi-scale, mesoscopic fashion. In the proposed model, a limited amount of adjacent paragraphs are represented as nodes, which are connected whenever they share a minimum semantical content. To illustrate the capabilities of our model, we present, as a use case, a qualitative analysis of "Alice's Adventures in Wonderland", a novel by Lewis Carroll. We show that the mesoscopic structure of documents modeled as networks reveals many semantic traits of texts, a feature that could be explored in a myriad of semantic-based applications.


title: Chronic Stress Produces Persistent Increases in Plasma Corticosterone, Reductions in Brain and Cardiac Nitric Oxide Production, and Delayed Alterations in Endothelial Function in Young Prehypertensive Rats

abstract: This study was designed to investigate whether oxidative stress, nitric oxide (NO) deficiency and/or endothelial dysfunction (ED) are present in young borderline hypertensive rats (BHR) and whether these pathologies can be causally involved in the initiation of blood pressure (BP) increases. Additionally, we tested the hypothesis that crowding stress, experienced during the peripubertal period, may produce persistent or delayed disorders in corticosterone release, NO synthesis, oxidative status and/or endothelial function that could accelerate BP increases. To test these hypotheses, 5-week-old male BHR and normotensive Wistar-Kyoto rats (WKY) were either kept in control conditions (for 2 and 4 weeks, respectively) or exposed to social stress produced by crowding for 2 weeks (stress). After cessation of crowding, a group of rats of each phenotype was kept in control conditions for the next 2 weeks (post-stress). Systolic BP of 5-week-old BHR was significantly increased vs. age-matched WKY (127 ± 3 vs. 104 ± 3 mmHg, p < 0.01) and remained significantly higher throughout the course of the experiment. Despite elevated BP, no signs of oxidative damage to plasma lipids, NO deficiency or ED were observed in control BHR vs. age-matched WKY. Crowding stress elevated plasma corticosterone and accelerated BP increases only in BHR; these effects persisted 2 weeks post-stress. Crowding failed to induce oxidative damage to plasma lipids in either phenotype, but it produced persistent decreases in NO production in the hypothalamus and brainstem of both strains of rats, as well as in the hearts of BHR. In contrast, crowding failed to reduce NO production in the aortae or acetylcholine-induced relaxations of the femoral arteries in both strains investigated. However, significantly reduced aortic NO production was observed in BHR 2 weeks post-stress vs. age-matched controls, which was in agreement with reduced NO-dependent components of vasorelaxation. In conclusion, this study's data showed that oxidative stress, NO deficiency and ED are not causally involved in initiation of blood pressure increase in BHR. However, exposure to stressful environments produced persistent increases in plasma corticosterone and reductions of brain and cardiac NO production followed by a delayed decrease in the NO-dependent component of endothelium-dependent relaxation-changes that collectively accelerated BP increases only in BHR.


title: On Microtargeting Socially Divisive Ads: A Case Study of Russia-Linked Ad Campaigns on Facebook

abstract: Targeted advertising is meant to improve the efficiency of matching advertisers to their customers. However, targeted advertising can also be abused by malicious advertisers to efficiently reach people susceptible to false stories, stoke grievances, and incite social conflict. Since targeted ads are not seen by non-targeted and non-vulnerable people, malicious ads are likely to go unreported and their effects undetected. This work examines a specific case of malicious advertising, exploring the extent to which political ads1 from the Russian Intelligence Research Agency (IRA) run prior to 2016 U.S. elections exploited Facebook's targeted advertising infrastructure to efficiently target ads on divisive or polarizing topics (e.g., immigration, race-based policing) at vulnerable sub-populations. In particular, we do the following: (a) We conduct U.S. census-representative surveys to characterize how users with different political ideologies report, approve, and perceive truth in the content of the IRA ads. Our surveys show that many ads are "divisive": they elicit very different reactions from people belonging to different socially salient groups. (b) We characterize how these divisive ads are targeted to sub-populations that feel particularly aggrieved by the status quo. Our findings support existing calls for greater transparency of content and targeting of political ads. (c) We particularly focus on how the Facebook ad API facilitates such targeting. We show how the enormous amount of personal data Facebook aggregates about users and makes available to advertisers enables such malicious targeting.


title: Strategic Honeypot Game Model for Distributed Denial of Service Attacks in the Smart Grid

abstract: Advanced metering infrastructure (AMI) is an important component for a smart grid system to measure, collect, store, analyze, and operate users consumption data. The need of communication and data transmission between consumers (smart meters) and utilities make AMI vulnerable to various attacks. In this paper, we focus on distributed denial of service attack in the AMI network. We introduce honeypots into the AMI network as a decoy system to detect and gather attack information. We analyze the interactions between the attackers and the defenders, and derive optimal strategies for both sides. We further prove the existence of several Bayesian-Nash equilibriums in the honeypot game. Finally, we evaluate our proposals on an AMI testbed in the smart grid, and the results show that our proposed strategy is effective in improving the efficiency of defense with the deployment of honeypots.


title: Causal diagrams for epidemiologic research.

abstract: Causal diagrams have a long history of informal use and, more recently, have undergone formal development for applications in expert systems and robotics. We provide an introduction to these developments and their use in epidemiologic research. Causal diagrams can provide a starting point for identifying variables that must be measured and controlled to obtain unconfounded effect estimates. They also provide a method for critical evaluation of traditional epidemiologic criteria for confounding. In particular, they reveal certain heretofore unnoticed shortcomings of those criteria when used in considering multiple potential confounders. We show how to modify the traditional criteria to correct those shortcomings.


title: Automatic assessment of descriptive answers in online examination system using semantic relational features

abstract: The revolution in technology reduces the effort of manpower in many of the areas. The boon of the technology and rapid advancements in education industry has provided a good learning environment. It offers qualification and credits at the desktop through online courses and evaluation. The prevailing system has its own pause in terms of volume, staffing, variation in the strategies of assessing. As of now, the objective-type questions alone can be practiced and assessed through online examinations. Researchers strive to build systems for evaluating descriptive answer as it is challenging and could not take up its full sway for complete automation. The challenge lies in recognizing the natural language answers and extracting the precise meaning so as to appropriately evaluate the knowledge obtained by the student. The proposed method contains stages such as question classification, answer classification and answer evaluation for the answers given by the student and grade them with appropriate score. A syntactical relation-based feature extraction technique is proposed for automatic evaluation of descriptive-type answers. The system has also adopted a cognitive-based approach where the student answers are judged for its correctness based on the phrases used for answering the questions. The score and feedback are provided to make aware of the understanding level of the subject. The experimental analysis shows .85% higher precision and recall when compared to the earlier systems.


title: Neural word and entity embeddings for ad hoc retrieval

abstract: Abstract Learning low dimensional dense representations of the vocabularies of a corpus, known as neural embeddings, has gained much attention in the information retrieval community. While there have been several successful attempts at integrating embeddings within the ad hoc document retrieval task, yet, no systematic study has been reported that explores the various aspects of neural embeddings and how they impact retrieval performance. In this paper, we perform a methodical study on how neural embeddings influence the ad hoc document retrieval task. More specifically, we systematically explore the following research questions: (i) do methods solely based on neural embeddings perform competitively with state of the art retrieval methods with and without interpolation? (ii) are there any statistically significant difference between the performance of retrieval models when based on word embeddings compared to when knowledge graph entity embeddings are used? and (iii) is there significant difference between using locally trained neural embeddings compared to when globally trained neural embeddings are used? We examine these three research questions across both hard and all queries. Our study finds that word embeddings do not show competitive performance to any of the baselines. In contrast, entity embeddings show competitive performance to the baselines and when interpolated, outperform the best baselines for both hard and soft queries.


title: Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection

abstract: Identifying the authenticity and processing history of an image is an important task in multimedia forensics. By analyzing traces left by different image manipulations, researchers have been able to develop several algorithms capable of detecting targeted editing operations. While this approach has led to the development of several successful forensic algorithms, an important problem remains: creating forensic detectors for different image manipulations is a difficult and time consuming process. Furthermore, forensic analysts need general purpose forensic algorithms capable of detecting multiple different image manipulations. In this paper, we address both of these problems by proposing a new general purpose forensic approach using convolutional neural networks (CNNs). While CNNs are capable of learning classification features directly from data, in their existing form they tend to learn features representative of an image’s content. To overcome this issue, we have developed a new type of CNN layer, called a constrained convolutional layer, that is able to jointly suppress an image’s content and adaptively learn manipulation detection features. Through a series of experiments, we show that our proposed constrained CNN is able to learn manipulation detection features directly from data. Our experimental results demonstrate that our CNN can detect multiple different editing operations with up to 99.97% accuracy and outperform the existing state-of-the-art general purpose manipulation detector. Furthermore, our constrained CNN can still accurately detect image manipulations in realistic scenarios where there is a source camera model mismatch between the training and testing data.


title: Single motor actuated peristaltic wave generator for a soft bodied worm robot

abstract: This paper presents the design and development of a single motor actuated peristaltic worm robot with three segments using a bio-inspired method of locomotion with one actuator that achieves optimised worm like peristaltic motion. Each segment consists of two solid circular disks that have a tendon connected through to the drive mechanism using a Bowden cable and a soft rubber skin that deforms under the compression of the tendon. Our hypothesis that a tuned peristaltic waveform can achieve improved performance of locomotion distance and clamping strength is proven using an initial test platform capable of demonstrating varying waveform types with multiple actuators. Three experiments were undertaken: (i) moving along a flat surface, (ii) moving through a confined tunnel (iii) moving through a confined tunnel whilst pulling a payload. Results from these experiments have identified the optimal parameters for a smart gearbox capable of achieving the same optimal peristaltic waveform signal as the more complex test platform but with only one actuator driving all three worm segments. Unlike other examples of peristaltic worm robots, this example uses a control method embedded within the mechanics of the design removing excessive number of actuators which contributes to miniaturisation, reduces power consumption and simplifies the overall design.


title: Learning user-specific parameters in a multibiometric system

abstract: Biometric systems that use a single biometric trait have to contend with noisy data, restricted degrees of freedom, failure-to-enroll problems, spoof attacks, and unacceptable error rates. Multibiometric systems that use multiple traits of an individual for authentication, alleviate some of these problems while improving verification performance. We demonstrate that the performance of multibiometric systems can be further improved by learning user-specific parameters. Two types of parameters are considered here. (i) Thresholds that are used to decide if a matching score indicates a genuine user or an impostor, and (ii) weights that are used to indicate the importance of matching scores output by each biometric trait. User-specific thresholds are computed using the cumulative histogram of impostor matching scores corresponding to each user. The user-specific weights associated with each biometric are estimated by searching for that set of weights which minimizes the total verification error. The tests were conducted on a database of 50 users who provided fingerprint, face and hand geometry data, with 10 of these users providing data over a period of two months. We observed that user-specific thresholds improved system performance by /spl sim/ 2%, while user-specific weights improved performance by /spl sim/ 3%.


title: Multiview Hessian Regularization for Image Annotation

abstract: The rapid development of computer hardware and Internet technology makes large scale data dependent models computationally tractable, and opens a bright avenue for annotating images through innovative machine learning algorithms. Semi-supervised learning (SSL) has consequently received intensive attention in recent years and has been successfully deployed in image annotation. One representative work in SSL is Laplacian regularization (LR), which smoothes the conditional distribution for classification along the manifold encoded in the graph Laplacian, however, it has been observed that LR biases the classification function towards a constant function which possibly results in poor generalization. In addition, LR is developed to handle uniformly distributed data (or single view data), although instances or objects, such as images and videos, are usually represented by multiview features, such as color, shape and texture. In this paper, we present multiview Hessian regularization (mHR) to address the above two problems in LR-based image annotation. In particular, mHR optimally combines multiple Hessian regularizations, each of which is obtained from a particular view of instances, and steers the classification function which varies linearly along the data manifold. We apply mHR to kernel least squares and support vector machines as two examples for image annotation. Extensive experiments on the PASCAL VOC'07 dataset validate the effectiveness of mHR by comparing it with baseline algorithms, including LR and HR.


title: Dynamics of canopy structure and understory light in montane evergreen broadleaved forest following a natural disturbance in North Guangdong

abstract: Natural disturbance is an important factor causing dynamic changes in forest canopies.An ice storm in southern China in 2008 was responsible for extensive damage to the forest ecosystem.The considerable distribution of canopy gaps created by the ice storm caused dynamic changes in the forest canopy.Therefore,studies to reveal the effects of the ice storm on forest regeneration and to monitor the restoration of damaged forest ecosystems will have significant implications for forestry research.Following the ice storm of 2008,a montane evergreen broad-leaved forest in Chebaling National Nature Reserve in northern Guangdong was investigated and a successive 3-year(2008—2010) community study of the 2 hm2 permanent plot was launched.We analyzed the changes in canopy structure(Canopy Openness and Leaf Area Index) and understory light(Transmitted Direct Solar Radiation,Transmitted Diffuse Solar Radiation and Transmitted Total Solar Radiation) in the forest following the ice storm using hemispherical photography to acquire canopy structure and understory light indexes.A quantitative study on the temporal and spatial variations in canopy structure and understory light as well as on environmental heterogeneity in the forest canopy is of considerable importance.Hemispherical photography is an example of optical remote sensing technology used to measure the parameters of canopy structure and understory light,and has now been widely recommended in the field of ecological research.The Kruskal-Wallis test(a non-parametric alternative to one-way ANOVA) and canonical correlation analysis were employed to analyze the changes in canopy structure and understory light.The main conclusions of this study are described in the following: 1) As the canopy openness shrank and the leaf area index increased,the understory light decreased in the forest during the recovery process following the ice storm;2) A highly significant difference(P0.0001) was detected in canopy structure and understory light during the 3 years,but between-year differences showed a tendency to decrease as the forest recovered from 2008 to 2010;3) In the first three years of forest recovery,transmitted direct solar radiation demonstrated a greater spatial-temporal fluctuation and provided a greater contribution to total solar radiation than did transmitted diffuse solar radiation;4) Canopy openness was more sensitive than leaf area index as an indicator of canopy structure change,while canopy structure had a greater influence on transmitted diffuse solar radiation than transmitted direct solar radiation.Initially,the trees in the ice storm damaged forest grew rapidly during the recovery period following the storm,but the growth slowed down gradually and stabilized.There was a gradual reduction in the understory light as the forest closed due to tree growth.Canopy openness is better for the evaluation of the dynamic effects of canopy structure.The spatial and temporal variations of transmitted direct solar radiation were more complex than transmitted diffuse solar radiation.Therefore,research to study the effects of an ice storm on canopy structure of a subtropical forest to reveal dynamics of canopy structure and understory light,and to monitor the restoration of a damaged forest ecosystem,will have significant implications for both theoretical and applied forest restoration efforts.


title: Towards an ontology modeling tool. A validation in software engineering scenarios

abstract: Ontology creation and management related processes are very important to define and develop semantic services. Ontology Engineering is the research field that provides the mechanisms to manage the life cycle of the ontologies. However, the process of building ontologies can be tedious and sometimes exhaustive. OWL-VisMod is a tool designed for developing ontological engineering based on visual analytics conceptual modeling for OWL ontologies life cycle management, supporting both creation and understanding tasks. This paper is devoted to evaluate OWL-VisMod through a set of defined tasks. The same tasks also will be done with the most known tool in Ontology Engineering, Protege, in order to compare the obtained results and be able to know how is OWL-VisMod perceived for the expert users. The comparison shows that both tools have similar acceptation scores, but OWL-VisMod presents better feelings regarding user's perception tasks due to the visual analytics influence.


title: Automatic Mobile Application Traffic Identification by Convolutional Neural Networks

abstract: Mobile network security and management are becoming important issues, due to the rapid development and widespread of the mobile network. Application traffic identification is a critical technology to resolve these issues. A variety of traffic classification methods on desktop applications are no longer effective in mobile network, because the majority of mobile traffic is carried over HTTP without distinctive features. Existing approaches to identify mobile traffic simply extract obvious features like fixed strings or regular expressions, which are not effective to capture hidden structure within the HTTP headers. In this paper, we propose a novel approach, which can identify mobile application by automatically extracting abstract features from labeled packets. Our approach is mainly based on convolutional neural networks (CNNs). The CNNs can extract the abstract statistical features between characters in HTTP and thus improve the identification accuracy. It's also able to reduce the dependence on prior knowledge and human effort in designing features. To verify the effectiveness of our method, we apply it to several identification tasks. The evaluation shows that our method can accurately identify the traffic of the target mobile application.