title: High levels of psychopathic traits alters moral choice but not moral judgment

abstract: Psychopathy is a personality disorder frequently associated with immoral behaviors. Previous behavioral studies on the influence of psychopathy on moral decision have yielded contradictory results, possibly because they focused either on judgment (abstract evaluation) or on choice of hypothetical action, two processes that may rely on different mechanisms. In this study, we explored the influence of the level of psychopathic traits on judgment and choice of hypothetical action during moral dilemma evaluation. A population of 102 students completed a questionnaire with ten moral dilemmas and nine non-moral dilemmas. The task included questions targeting both judgment (“Is it acceptable to … in order to …?”) and choice of hypothetical action (“Would you … in order to …?”). The level of psychopathic traits of each participant was evaluated with the Levenson Self-Report Psychopathy (LSRP) scale. Logistic regression fitted with the generalized estimating equations method analyses were conducted using responses to the judgment and choice tasks as the dependent variables and psychopathy scores as predictor. Results show that a high level of psychopathic traits, and more specifically those related to affective deficit, predicted a greater proportion of utilitarian responses for the choice but not for the judgment question. There was no first-order interaction between the level of psychopathic traits and other potential predictors. The relation between a high level of psychopathic traits and increased utilitarianism in choice of action but not in moral judgment may explain the contradictory results of previous studies where these two processes were not contrasted. It also gives further support to the hypothesis that choice of action endorsement and abstract judgment during moral dilemma evaluation are partially distinct neural and psychological processes. We propose that this distinction should be better taken into account in the evaluation of psychopathic behaviors.


title: A MODEL FOR SOFTWARE PRODUCT QUALITY

abstract: A model for software product quality is defined, it has been formulated by associating a set of quality-carrying properties with each of the structural forms that are used to define the statements and statement components of a programming language. These quality-carrying properties are in turn linked to the high-level quality attributes of the International Standard for Software Product Evaluation ISO-9126. The model supports building quality into software, definition of language-specific coding standards, systematically classifying quality defects, and the development of automated code auditors for detecting defects in software. >


title: ACOUSTIC SCENE CLASSIFICATION: A COMPETITION REVIEW

abstract: In this paper we study the problem of acoustic scene classification, i.e., categorization of audio sequences into mutually exclusive classes based on their spectral content. We describe the methods and results discovered during a competition organized in the context of a graduate machine learning course; both by the students and external participants. We identify the most suitable methods and study the impact of each by performing an ablation study of the mixture of approaches. We also compare the results with a neural network baseline, and show the improvement over that. Finally, we discuss the impact of using a competition as a part of a university course, and justify its importance in the curriculum based on student feedback.


title: Effects of Take-Over Requests and Cultural Background on Automation Trust in Highly Automated Driving

abstract: Appropriate automation trust is a prerequisite for safe, comfortable and efficient use of highly automated driving systems (HADS). Earlier research indicates that a drivers’ nationality and Take-Over Requests (TOR) due to imperfect system reliability might affect trust, but this has never been investigated in the context of highly automated driving. A driving simulator study (N = 80) showed that TORs only temporarily lowered trust in HADSs, and revealed similarities in trust formation between German and Chinese drivers. Trust was significantly higher after experiencing the system than before, both for German and Chinese participants. However, Chinese drivers reported significantly higher automation mistrust than German drivers. Self-report measures of automation trust were not connected to behavioral measures. The results support a distinction between automation trust and mistrust as separate constructs, short- and long-term effects of TORs on automation trust, and cultural differences in automation trust.


title: Cultural Psychology: A Once and Future Discipline

abstract: Foreword by Sheldon H. White Introduction Enduring Questions and Disputes Cross-Cultural Investigations Cognitive Development, Culture, and Schooling From Cross-Cultural Psychology to the Second Psychology Putting Culture in the Middle Phylogeny and Cultural History A Cultural Approach to Ontogeny The Cognitive Analysis of Behavior in Context Creating Model Activity Systems A Multilevel Methodology for Cultural Psychology The Work in Context Notes References Acknowledgments Index


title: Optimization‐based material parameter identification in indentation testing for finite strain elasto‐plasticity

abstract: The calculation of the derivatives of the state variables with respect to material parameters using the direct differentiation method is developed for the case of a finite strain finite element analysis in corotational formulation for material behaviour including hypo-elasticity and non-linear isotropic and kinematic hardening plasticity. The sensitivity analysis for the case of flexible-to-flexible contact, required in indentation testing, has been included. The derivatives obtained in a rotating-stretching bar problem are compared to results obtained by the finite difference method in order to verify the suitability of the corotational formulation in sensitivity analysis for dealing with rigid body rotations. Material parameter identification using a gradient-based numerical optimization procedure is assessed with respect to stability by using pseudo-experimental indentation curves, obtained from modelling with known material parameters, and superposed with artificial noise. The effect of the load history on parameter identification is investigated.


title: HermitCore: A Unikernel for Extreme Scale Computing

abstract: We expect that the size and the complexity of future supercomputers will increase on their path to exascale systems and beyond. Therefore, system software has to adapt to the complexity of these systems for a simplification of the development of scalable applications. In this paper, we present a unikernel operating system design for HPC. It extends the multi-kernel approach while providing better programmability and scalability for hierarchical systems, such as HLRS' Hazel Hen, which base on multiple cluster-on-a-chip processors. We prove the scalability of the design via micro benchmarks by taking the example of HermitCore---our prototype implementation of the new design.


title: Who Shalt Not Kill? Individual Differences in Working Memory Capacity, Executive Control, and Moral Judgment

abstract: Recent findings suggest that exerting executive control influences responses to moral dilemmas. In our study, subjects judged how morally appropriate it would be for them to kill one person to save others. They made these judgments in 24 dilemmas that systematically varied physical directness of killing, personal risk to the subject, inevitability of the death, and intentionality of the action. All four of these variables demonstrated main effects. Executive control was indexed by scores on working-memory-capacity (WMC) tasks. People with higher WMC found certain types of killing more appropriate than did those with lower WMC and were more consistent in their judgments. We also report interactions between manipulated variables that implicate complex emotion-cognition integration processes not captured by current dual-process views of moral judgment.


title: 2.6 GHz 4 watt GaN-HEMT two-stage power amplifier MMIC for LTE small-cell applications

abstract: This paper presents a two-stage PA MMIC using 0.4-μm GaN-HEMT. Two-stage structure is adopted to take its high gain property and simple inter-stage matching network. The size and loss of the inter-stage matching network can be reduced by including bond wires as a part of the shunt inductors in the matching network. The two-stage PA MMIC was fabricated with a size of 2.0#x000D7;1.9 mm2 and was mounted on the 4#x000D7;4 QFN package for evaluation. Using a downlink LTE signal with a PAPR of 6.5 dB and a channel bandwidth of 10 MHz at the 2.6 GHz band, the PA MMIC exhibited a gain of 30 dB, a DE of 32%, and an ACLR of -31.4 dBc at an average output power of 36 dBm. Using two PA MMICs for carrier and peaking amplifiers, DPA was designed and evaluated. At a 6-dB back-off output power level of 39 dBm, a gain of 24.7 dB and a DE of 43.5% were achieved.


title: On ontology-driven document clustering using core semantic features

abstract: Incorporating semantic knowledge from an ontology into document clustering is an important but challenging problem. While numerous methods have been developed, the value of using such an ontology is still not clear. We show in this paper that an ontology can be used to greatly reduce the number of features needed to do document clustering. Our hypothesis is that polysemous and synonymous nouns are both relatively prevalent and fundamentally important for document cluster formation. We show that nouns can be efficiently identified in documents and that this alone provides improved clustering. We next show the importance of the polysemous and synonymous nouns in clustering and develop a unique approach that allows us to measure the information gain in disambiguating these nouns in an unsupervised learning setting. In so doing, we can identify a core subset of semantic features that represent a text corpus. Empirical results show that by using core semantic features for clustering, one can reduce the number of features by 90% or more and still produce clusters that capture the main themes in a text corpus.


title: A distributed, semiotic-inductive, and human-oriented approach to web-scale knowledge retrieval

abstract: Web-scale knowledge retrieval can be enabled by distributed information retrieval; clustering Web clients to a large-scale computing infrastructure for knowledge discovery from Web documents. Based on this infrastructure, we propose to apply semiotic (i.e., sub-syntactical) and inductive (i.e., probabilistic) methods for inferring concept associations in human knowledge. These associations can be combined to form a fuzzy (i.e., gradual) semantic net representing a map of the knowledge in the Web. Thus, we propose to provide interactive visualizations of these cognitive concept maps to end users, who can browse and search the Web in a human-oriented, visual, and associative interface.


title: Heart sound diagnosis based on DTW and MFCC

abstract: Recently, more and more intelligent algorithms are applied to biomedical, which have improved the biomedical data analysis and classification greatly. In this paper, a new method of automatic identification of heart sound by using DTW (dynamic time warping) and MFCC (Mel-frequency cepstral coefficients) of heart is introduced. MFCC of heart sound are extracted, and then DTW is used to identify heart sound. The experimental results show that the methods have good performance in heart sound recognition.


title: Increased structural connectivity in grapheme-color synesthesia

abstract: Diffusion tensor imaging allowed us to validate for the first time the hypothesis that hyperconnectivity causes the added sensations in synesthesia. Grapheme-color synesthetes (n = 18), who experience specific colors with particular letters or numbers (for example, 'R is sky blue'), showed greater anisotropic diffusion compared with matched controls. Greater anisotropic diffusion indicates more coherent white matter. Anisotropy furthermore differentiated subtypes of grapheme-color synesthesia. Greater connectivity in the inferior temporal cortex was particularly strong for synesthetes who see synesthetic color in the outside world ('projectors') as compared with synesthetes who see the color in their 'mind's eye' only ('associators'). In contrast, greater connectivity (as compared with non-synesthetes) in the superior parietal or frontal cortex did not differentiate between subtypes of synesthesia. In conclusion, we found evidence that increased structural connectivity is associated with the presence of grapheme-color synesthesia, and has a role in the subjective nature of synesthetic color experience.


title: Extracorporeal membrane oxygenation for grade 3 primary graft dysfunction after lung transplantation: Long‐term outcomes

abstract: INTRODUCTION ::: Extracorporeal membrane oxygenation (ECMO) is an efficient and innovative therapeutic tool for primary graft dysfunction (PGD). However, its effect on survival and long-term lung function is not well known. This study evaluated those parameters in patients with PGD requiring ECMO. ::: ::: ::: METHOD ::: This single-center, retrospective study included patients who underwent LTx at our institute between January 2007 and December 2013. Patients and disease characteristics, survival, and pulmonary function tests were recorded. ::: ::: ::: RESULTS ::: A total of 309 patients underwent LTx during the study period and 211 were included. The patients were predominantly male (53.5%), the median age was 39 years, and the primary pathology was suppurative disease (53.1%). ECMO for PGD was mandatory in 24 (11.7%) cases. Mortality at 3 months in the ECMO group was 50% (N = 12). However, long-term survival after PGD did not correlate with ECMO. Forced expiratory volume and vital capacity were significantly reduced in patients with PGD requiring ECMO, especially those with idiopathic pulmonary fibrosis. ::: ::: ::: CONCLUSION ::: Veno-arterial ECMO appears to be suitable for management of PGD after LTx. Patients with PGD requiring ECMO show increased initial mortality; however, long-term survival was comparable with that of other patients in the study. Lung function does not appear to be related to PGD requiring ECMO.


title: Many roads to maturity: microRNA biogenesis pathways and their regulation

abstract: MicroRNAs are important regulators of gene expression that control both physiological and pathological processes such as development and cancer. Although their mode of action has attracted great attention, the principles governing their expression and activity are only beginning to emerge. Recent studies have introduced a paradigm shift in our understanding of the microRNA biogenesis pathway, which was previously believed to be universal to all microRNAs. Maturation steps specific to individual microRNAs have been uncovered, and these offer a plethora of regulatory options after transcription with multiple proteins affecting microRNA processing efficiency. Here we review the recent advances in knowledge of the microRNA biosynthesis pathways and discuss their impact on post-transcriptional microRNA regulation during tumour development.


title: Cellular data network infrastructure characterization and implication on mobile content placement

abstract: Despite the tremendous growth in the cellular data network usage due to the popularity of smartphones, so far there is rather limited understanding of the network infrastructure of various cellular carriers. Understanding the infrastructure characteristics such as the network topology, routing design, address allocation, and DNS service configuration is essential for predicting, diagnosing, and improving cellular network services, as well as for delivering content to the growing population of mobile wireless users. In this work, we propose a novel approach for discovering cellular infrastructure by intelligently combining several data sources, i.e., server logs from a popular location search application, active measurements results collected from smartphone users, DNS request logs from a DNS authoritative server, and publicly available routing updates. We perform the first comprehensive analysis to characterize the cellular data network infrastructure of four major cellular carriers within the U.S. in our study. We conclude among other previously little known results that the current routing of cellular data traffic is quite restricted, as it must traverse a rather limited number (i.e., 4-6) of infrastructure locations (i.e., GGSNs), which is in sharp contrast to wireline Internet traffic. We demonstrate how such findings have direct implications on important decisions such as mobile content placement and content server selection. We observe that although the local DNS server is a coarse-grained approximation on the user's network location, for some carriers, choosing content servers based on the local DNS server is accurate enough due to the restricted routing in cellular networks. Placing content servers close to GGSNs can potentially reduce the end-to-end latency by more than 50% excluding the variability from air interface.


title: The hRing: A wearable haptic device to avoid occlusions in hand tracking

abstract: The wearable electronics business has powered over $14 billion in 2014 and it is estimated to power over $70 billion by 2024. However, commercially-available wearable devices still provide very limited haptic feedback, mainly focusing on vibrotactile sensations. Towards a more realistic feeling of interacting with virtual and remote objects, we propose a novel wearable cutaneous device for the proximal finger phalanx, called "hRing". It consists of two servo motors that move a belt placed in contact with the user's finger skin. When the motors spin in opposite directions, the belt presses into the user's finger, while when the motors spin in the same direction, the belt applies a shear force to the skin. Its positioning on the proximal finger phalanx improves the capability of this device to be used together with unobtrusive hand tracking systems, such as the LeapMotion controller and the Kinect sensor. The viability of the proposed approach is demonstrated through a pick-and-place experiment involving seven human subjects. Providing cutaneous feedback through the proposed device improved the performance and perceived effectiveness of the considered task of 20% and 47% with respect to not providing any force feedback, respectively. All subjects found no difference in the quality of the tracking when carrying out the task wearing the device versus barehanded.


title: Best Practices for Missing Data Management in Counseling Psychology

abstract: This article urges counseling psychology researchers to recognize and report how missing data are handled, because consumers of research cannot accurately interpret findings without knowing the amount and pattern of missing data or the strategies that were used to handle those data. Patterns of missing data are reviewed, and some of the common strategies for dealing with them are described. The authors provide an illustration in which data were simulated and evaluate 3 methods of handling missing data: mean substitution, multiple imputation, and full information maximum likelihood. Results suggest that mean substitution is a poor method for handling missing data, whereas both multiple imputation and full information maximum likelihood are recommended alternatives to this approach. The authors suggest that researchers fully consider and report the amount and pattern of missing data and the strategy for handling those data in counseling psychology research and that editors advise researchers of this expectation.


title: Visualizing Sets with Linear Diagrams

abstract: This paper presents the first design principles that optimize the visualization of sets using linear diagrams. These principles are justified through empirical studies that evaluate the impact of graphical features on task performance. Linear diagrams represent sets using straight line segments, with line overlaps corresponding to set intersections. This study builds on recent empirical research, which establishes that linear diagrams can be superior to prominent set visualization techniques, namely Euler and Venn diagrams. We address the problem of how to best visualize overlapping sets using linear diagrams. To solve the problem, we investigate which graphical features of linear diagrams significantly impact user task performance. To this end, we conducted seven crowdsourced empirical studies involving a total of 1,760 participants. These studies allowed us to identify the following design principles, which significantly aid task performance: use a minimal number of line segments, use guidelines where overlaps start and end, and draw lines that are thin as opposed to thick bars. We also evaluated the following graphical properties that did not significantly impact task performance: color, orientation, and set order. The results are brought to life through a freely available software implementation that automatically draws linear diagrams with user-controlled graphical choices. An important consequence of our research is that users are now able to create effective visualizations of sets automatically, thus improving human--computer interaction.


title: A Unified Character-Based Tagging Framework for Chinese Word Segmentation

abstract: Chinese word segmentation is an active area in Chinese language processing though it is suffering from the argument about what precisely is a word in Chinese. Based on corpus-based segmentation standard, we launched this study. In detail, we regard Chinese word segmentation as a character-based tagging problem. We show that there has been a potent trend of using a character-based tagging approach in this field. In particular, learning from segmented corpus with or without additional linguistic resources is treated in a unified way in which the only difference depends on how the feature template set is selected. It differs from existing work in that both feature template selection and tag set selection are considered in our approach, instead of the previous feature template focus only technique. We show that there is a significant performance difference as different tag sets are selected. This is especially applied to a six-tag set, which is good enough for most current segmented corpora. The linguistic meaning of a tag set is also discussed. Our results show that a simple learning system with six n-gram feature templates and a six-tag set can obtain competitive performance in the cases of learning only from a training corpus. In cases when additional linguistic resources are available, an ensemble learning technique, assistant segmenter, is proposed and its effectiveness is verified. Assistant segmenter is also proven to be an effective method as segmentation standard adaptation that outperforms existing ones. Based on the proposed approach, our system provides state-of-the-art performance in all 12 corpora of three international Chinese word segmentation bakeoffs.


title: Smart suit for horse trainers-power and skill assist based on semi-active assist and energy control

abstract: This paper describes semi-active assist control in the form of a smart suit for horse trainers. We have developed a soft and flexible power assist device named ”smart suit” for reducing user's physical fatigue. The smart suit is a semi-active and flexible power assist devices. The semi-active assist mechanism consisting of elastic materials and a small DC motor is our original technology. The assist force is gained by the elastic force of elastic materials, and it is controlled by adjusting the length of the elastic materials by using DC motor. In this study, we applied the smart suit to horse trainers for reducing their physical fatigue in horse training to prevent their injuries. Moreover we aim developing an assist force control method based on the energy control for considering not only the power assist but the skill assit. In this paper, we confirm the energy control has the effectiveness of the power assist in the situation without the skill assist.


title: Design procedure and performance potential for operational amplifier using indirect compensation

abstract: A design procedure for an operational amplifier using indirect compensation is presented in this paper. Indirect compensation has inherent benefits in regards with power to speed trade-off. The technique has been seldom used in the past because a clear methodology for designing such an amplifier has not been provided. This paper develops the mathematical and analytical insight for designing an operational amplifier with this technique. The paper also provides a design strategy and an example to illustrate the use of the proposed design procedure.


title: MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects

abstract: We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D SLAM system that goes beyond traditional systems which output a purely geometric map of a static scene. MaskFusion recognizes, segments and assigns semantic class labels to different objects in the scene, while tracking and reconstructing them even when they move independently from the camera. As an RGB-D camera scans a cluttered scene, image-based instance-level semantic segmentation creates semantic object masks that enable real-time object recognition and the creation of an object-level representation for the world map. Unlike previous recognition-based SLAM systems, MaskFusion does not require known models of the objects it can recognize, and can deal with multiple independent motions. MaskFusion takes full advantage of using instance-level semantic segmentation to enable semantic labels to be fused into an object-aware map, unlike recent semantics enabled SLAM systems that perform voxel-level semantic segmentation. We show augmented-reality applications that demonstrate the unique features of the map output by MaskFusion: instance-aware, semantic and dynamic.


title: Penile Implants among Prisoners—A Cause for Concern?

abstract: BACKGROUND ::: We report the prevalence of penile implants among prisoners and determine the independent predictors for having penile implants. Questions on penile implants were included in the Sexual Health and Attitudes of Australian Prisoners (SHAAP) survey following concerns raised by prison health staff that increasing numbers of prisoners reported having penile implants while in prison. ::: ::: ::: METHODS ::: Computer-Assisted Telephone Interviewing (CATI) of a random sample of prisoners was carried out in 41 prisons in New South Wales and Queensland (Australia). Men were asked, "Have you ever inserted or implanted an object under the skin of your penis?" If they responded Yes: "Have you ever done so while you were in prison?" Univariate logistic regression and logistic regression were used to determine the factors associated with penile implants. ::: ::: ::: RESULTS ::: A total of 2,018 male prisoners were surveyed, aged between 18 and 65 years, and 118 (5.8%) reported that they had inserted or implanted an object under the skin of their penis. Of these men, 87 (73%) had this done while they were in prison. In the multivariate analysis, a younger age, birth in an Asian country, and prior incarceration were all significantly associated with penile implants (p<0.001). Men with penile implants were also more likely to report being paid for sex (p<0.001), to have had body piercings (p<0.001) or tattoos in prison (p<0.001), and to have taken non-prescription drugs while in prison (p<0.05). ::: ::: ::: CONCLUSIONS ::: Penile implants appear to be fairly common among prisoners and are associated with risky sexual and drug use practices. As most of these penile implants are inserted in prison, these men are at risk of blood borne viruses and wound infection. Harm reduction and infection control strategies need to be developed to address this potential risk.


title: The Use of ICT in Education: a survey of schools in Europe

abstract: The Survey of Schools: ICT in education commissioned in 2011 by the European Commission took place between January 2011 and November 2012, with data collection in autumn 2011. This article presents the main findings of the Survey based on over 190,000 questionnaire answers from students, teachers and head teachers in primary, lower and upper secondary schools randomly sampled. The article details the analytical framework design and the survey methodology implemented. It then presents the main ‘state of the art’ indicators that have been built, concerning ICT infrastructure and access to it, frequency of students' ICT based activities during lessons, level of teachers' and students' confidence in their digital competences, their opinion about using ICT for teaching and learning, and the school strategies to support ICT integration in teaching and learning. The article also presents the main findings of the exploratory part of the analysis, introducing the concepts of digitally supportive school, digitally confident and supportive teacher and digitally confident and supportive student, estimating their respective proportion at EU level on average and by country and investigating whether high percentage of digitally supportive schools include high percentages of digitally confident and positive teachers and students. A few recommendations for policy making at European, national, regional/local and institutional levels conclude the article.


title: A Review of Secure Authentication based e-Payment Protocol

abstract: The growth of e-commerce platform is increasing rapidly and possesses a higher level of hazard compared to standard applications as well as it requires a more prominent level of safety.Additionally, the transaction and their data about clients are enormously sensitive, security production and privacy is exceptionally crucial. Consequently, the confirmation is generally vital towards security necessities as well as prevents the data from stolen and unauthorized person over the transaction of e-payment. At the same time, privacy strategies are essential to address the client data security. Because of this, the data protection and security ought to be viewed as a central part of e-business framework plan. Particularly enormous consideration is given to cash exchanges assurance. In the past decades, various methods were created to permit secure cash transaction using e-payment frameworks. This study will review and discuss the e-payment scheme. It uses various encryption algorithms and methods to accomplish data integrity, privacy, non-repudiation and authentication.


title: Driver hand localization and grasp analysis: A vision-based real-time approach

abstract: Extracting hand regions and their grasp information from images robustly in real-time is critical for occupants' safety and in-vehicular infotainment applications. It must however, be noted that naturalistic driving scenes suffer from rapidly changing illumination and occlusion. This is aggravated by the fact that hands are highly deformable objects, and change in appearance frequently. This work addresses the task of accurately localizing driver hands and classifying the grasp state of each hand. We use a fast ConvNet to first detect likely hand regions. Next, a pixel-based skin classifier that takes into account the global illumination changes is used to refine the hand detections and remove false positives. This step generates a pixel-level mask for each hand. Finally, we study each such masked regions and detect if the driver is grasping the wheel, or in some cases a mobile phone. Through evaluation we demonstrate that our method can outperform state-of-the-art pixel based hand detectors, while running faster (at 35 fps) than other deep ConvNet based frameworks even for grasp analysis. Hand mask cues are shown to be crucial when analyzing a set of driver hand gestures (wheel/mobile phone grasp and no-grasp) in naturalistic driving settings. The proposed detection and localization pipeline hence can act as a general framework for real-time hand detection and gesture classification.


title: New Online Ecology of Adversarial Aggregates: ISIS and beyond

abstract: Support for an extremist entity such as Islamic State (ISIS) somehow manages to survive globally online despite considerable external pressure and may ultimately inspire acts by individuals having no history of extremism, membership in a terrorist faction, or direct links to leadership. Examining longitudinal records of online activity, we uncovered an ecology evolving on a daily time scale that drives online support, and we provide a mathematical theory that describes it. The ecology features self-organized aggregates (ad hoc groups formed via linkage to a Facebook page or analog) that proliferate preceding the onset of recent real-world campaigns and adopt novel adaptive mechanisms to enhance their survival. One of the predictions is that development of large, potentially potent pro-ISIS aggregates can be thwarted by targeting smaller ones.


title: Hemispheric Asymmetry Reduction in Older Adults: The HAROLD Model

abstract: A model of the effects of aging on brain activity during cognitive performance is introduced. The model is called HAROLD (hemispheric asymmetry reduction in older adults), and it states that, under similar circumstances, prefrontal activity during cognitive performances tends to be less lateralized in older adults than in younger adults. The model is supported by functional neuroimaging and other evidence in the domains of episodic memory, semantic memory, working memory, perception, and inhibitory control. Age-related hemispheric asymmetry reductions may have a compensatory function or they may reflect a dedifferentiation process. They may have a cognitive or neural origin, and they may reflect regional or network mechanisms. The HAROLD model is a cognitive neuroscience model that integrates ideas and findings from psychology and neuroscience of aging.


title: Modeling, design, fabrication and characterization of first large 2.5D glass interposer as a superior alternative to silicon and organic interposers at 50 micron bump pitch

abstract: This paper describes the first design and fabrication of a large 2.5D glass interposer with 50 µm pitch chip-level interconnections made of 6 layers of 3 µm re-distribution (RDL) wiring. Many applications including high-performance networking and cloud computing data centers require ultra- high-bandwidth of the magnitude of 512 GB/s. Silicon-based 2.5D interposers are the only approaches being pursued by the industry to meet this need, enabled by sub-micron BEOL wiring in the wafer fabs. Such interposers, however, are too expensive for most applications. Glass interposers are superior to silicon interposers due to their high dimensional stability, low loss tangent, and large panel processing ultimately leading to lower cost. This paper presents the design, fabrication and electrical characterization, leading to the first fabrication of 2.5D glass interposers with 50 µm I/O pitch with 3 µm lines. Double-sided panel processing utilizing thin, low-loss dryfilm polymer dielectrics and SAP copper plating, with differential spray etching techniques, was used to fabricate 3 m wide transmission lines on 25mm x 30mm glass interposers processed on a 300 m thick 150mm x 150mm glass panels. A six-metal layer test vehicle with two daisy chain, 10mm x 10mm test chips at 100 µm spacing, was fabricated and assembled by thermo-compression bonding of Cu microbumps and SnAg solder caps. Ultra-fine 3 µm escape routing was demonstrated on a two-metal layer test vehicle. High frequency characterization of 3 µm lines showed low loss of 0.12 dB/mm at 2 GHz.


title: Knowledge sharing models: Do they really fit public organizations?

abstract: Literature in knowledge management reveals that there are a number of knowledge sharing (KS) models. A range of models exists as a result of differed view on the subject which is broad and subjective. Some of the models address the antecedents or factors influencing KS whilst the others address the relationship between KS and performance. The benefit of KS cannot be materialized if it does not contribute to organizational attribute. Despite the variety of models, only a few focuses on KS in public sectors. This could resulted from a situation whereby KS is at its infancy in public services.. This paper reviews the existing models of KS and upon a critical review, a KS model which is thought to be suitable for used by public sector in Malaysia is then proposed. The proposed model provides foundation for subsequent research to firstly investigate factors affecting KS in public sector and secondly, seek to identify the relationship between KS, organizational performance and service delivery.


title: Peanut polyamines may be non‐allergenic

abstract: Polyamines such as putrescine, spermidine and spermine have been implicated in preventing food allergies in early life, but they have also been reported to be able to bind to immunoglobulin E (IgE) antibodies in vitro (ie they are possibly allergenic). The objective of this study was to determine if polyamines bind in vitro to IgE antibodies from a pooled serum of five peanut-allergic individuals. Levels of polyamines were also determined by ion-exchange chromatography. Indirect and inhibition enzyme immunosorbent assays (ELISAs) were used to determine the IgE binding or allergenic properties of polyamines. Results showed that, of the three polyamines, spermidine was predominant in peanuts. In both indirect and inhibition ELISAs, IgE antibodies did not bind to the polyamines. It was concluded that polyamines from peanuts, unlike peanut proteins, are not allergenic or an additional threat to patients who are allergic to peanuts. Published in 2005 for SCI by John Wiley & Sons, Ltd.


title: Deep-based Ingredient Recognition for Cooking Recipe Retrieval

abstract: Retrieving recipes corresponding to given dish pictures facilitates the estimation of nutrition facts, which is crucial to various health relevant applications. The current approaches mostly focus on recognition of food category based on global dish appearance without explicit analysis of ingredient composition. Such approaches are incapable for retrieval of recipes with unknown food categories, a problem referred to as zero-shot retrieval. On the other hand, content-based retrieval without knowledge of food categories is also difficult to attain satisfactory performance due to large visual variations in food appearance and ingredient composition. As the number of ingredients is far less than food categories, understanding ingredients underlying dishes in principle is more scalable than recognizing every food category and thus is suitable for zero-shot retrieval. Nevertheless, ingredient recognition is a task far harder than food categorization, and this seriously challenges the feasibility of relying on them for retrieval. This paper proposes deep architectures for simultaneous learning of ingredient recognition and food categorization, by exploiting the mutual but also fuzzy relationship between them. The learnt deep features and semantic labels of ingredients are then innovatively applied for zero-shot retrieval of recipes. By experimenting on a large Chinese food dataset with images of highly complex dish appearance, this paper demonstrates the feasibility of ingredient recognition and sheds light on this zero-shot problem peculiar to cooking recipe retrieval.


title: Adversarial Data Mining: Big Data Meets Cyber Security

abstract: As more and more cyber security incident data ranging from systems logs to vulnerability scan results are collected, manually analyzing these collected data to detect important cyber security events become impossible. Hence, data mining techniques are becoming an essential tool for real-world cyber security applications. For example, a report from Gartner [gartner12] claims that "Information security is becoming a big data analytics problem, where massive amounts of data will be correlated, analyzed and mined for meaningful patterns". Of course, data mining/analytics is a means to an end where the ultimate goal is to provide cyber security analysts with prioritized actionable insights derived from big data. This raises the question, can we directly apply existing techniques to cyber security applications? One of the most important differences between data mining for cyber security and many other data mining applications is the existence of malicious adversaries that continuously adapt their behavior to hide their actions and to make the data mining models ineffective. Unfortunately, traditional data mining techniques are insufficient to handle such adversarial problems directly. The adversaries adapt to the data miner's reactions, and data mining algorithms constructed based on a training dataset degrades quickly. To address these concerns, over the last couple of years new and novel data mining techniques which is more resilient to such adversarial behavior are being developed in machine learning and data mining community. We believe that lessons learned as a part of this research direction would be beneficial for cyber security researchers who are increasingly applying machine learning and data mining techniques in practice. To give an overview of recent developments in adversarial data mining, in this three hour long tutorial, we introduce the foundations, the techniques, and the applications of adversarial data mining to cyber security applications. We first introduce various approaches proposed in the past to defend against active adversaries, such as a minimax approach to minimize the worst case error through a zero-sum game. We then discuss a game theoretic framework to model the sequential actions of the adversary and the data miner, while both parties try to maximize their utilities. We also introduce a modified support vector machine method and a relevance vector machine method to defend against active adversaries. Intrusion detection and malware detection are two important application areas for adversarial data mining models that will be discussed in details during the tutorial. Finally, we discuss some practical guidelines on how to use adversarial data mining ideas in generic cyber security applications and how to leverage existing big data management tools for building data mining algorithms for cyber security.


title: On the power efficiency of cascode compensation over Miller compensation in two-stage operational amplifiers

abstract: Optimization of power consumption is one of the main design challenges in today's low-power high-speed analog integrated circuits. In this paper, two popular techniques to stabilize two-stage operational amplifiers, namely Miller and cascode compensations are compared from power point of view. To accomplish this, the cascode-compensated structure is basically analyzed to derive the required equations for comparison. The results show that for the same specifications, cascode compensation is more power efficient than Miller compensation especially for heavy capacitive loads. This has been confirmed by circuit-level simulations in 0.25 μm CMOS technology.


title: Impact of self-heating effect on long-term reliability and performance degradation in CMOS circuits

abstract: As the technology feature size is reduced, the thermal management of high-performance very large scale integrations (VLSIs) becomes an important design issue. The self-heating effect and nonuniform power distribution in VLSIs lead to performance and long-term reliability degradation. In this paper, we analyze the self-heating effect in high-performance sub-0.18-/spl mu/m bulk and silicon-on-insulator (SOI) CMOS circuits using fast transient quasi-dc thermal simulations. The impact of the self-heating effect and technology scaling on the metallization lifetime and the gate oxide time-to-breakdown (TBD) reduction are also investigated. Based on simulation results, an optimized clock-driver design is proposed. The proposed layout reduces the hot-spot temperature by 15/spl deg/C and by 7/spl deg/C in 0.09-/spl mu/m SOI and bulk CMOS technologies, respectively.


Balance K-Means Algorithm


title: ACTRESS: domain-specific modeling of self-adaptive software architectures

abstract: A common approach for engineering self-adaptive software systems is to use Feedback Control Loops (FCLs). Advances have led to more explicit and safer design of some control architectures, however, there is a need for more integrated and systematic approaches that support end-to-end integration of FCLs into software systems. In this paper, we propose a tooled approach that enables researchers and engineers to design and integrate adaptation mechanisms into software systems through FCLs. It consists of a domain-specific modeling language that raises the level of abstraction on which FCLs are defined, making them amenable to automated analysis and implementation code synthesis. The language supports composition, distribution and reflection, thereby enabling coordination and composition of multiple distributed FCLs. Its use is facilitated by a modeling environment, Actress, that provides support for modeling, verification and complete code generation. We report on its application to a concrete adaptation case study and also discuss resulting properties.


title: Adaptive Cardinality Estimation

abstract: In this paper we address cardinality estimation problem which is an important subproblem in query optimization. Query optimization is a part of every relational DBMS responsible for finding the best way of the execution for the given query. These ways are called plans. The execution time of different plans may differ by several orders, so query optimizer has a great influence on the whole DBMS performance. We consider cost-based query optimization approach as the most popular one. It was observed that cost-based optimization quality depends much on cardinality estimation quality. Cardinality of the plan node is the number of tuples returned by it. ::: In the paper we propose a novel cardinality estimation approach with the use of machine learning methods. The main point of the approach is using query execution statistics of the previously executed queries to improve cardinality estimations. We called this approach adaptive cardinality estimation to reflect this point. The approach is general, flexible, and easy to implement. The experimental evaluation shows that this approach significantly increases the quality of cardinality estimation, and therefore increases the DBMS performance for some queries by several times or even by several dozens of times.


title: An Integrated Expert User with End User in Technology Acceptance Model for Actual Evaluation

abstract: Effective evaluation is necessary in order to ensure systems adequately meet the requirements and information processing needs of the users and scope of the system. Technology acceptance model is one of the most popular and effective models for evaluation. A number of studies have proposed evaluation frameworks to aid in evaluation work. The end users for evaluation the acceptance of new technology or system have a lack of knowledge to examine and evaluate some features in the new technology/system. This will give a fake evaluation results of the new technology acceptance. This paper proposes a novel evaluation model to evaluate user acceptance of software and system technology by modifying the dimensions of the Technology Acceptance Model (TAM) and added additional success dimensions for expert users. The proposed model has been validated by an empirical study based on a questionnaire. The results indicated that the expert users have a strong significant influence to help in evaluation and pay attention to some features that end users have lack of knowledge to evaluate it.


title: Definitions of Bullying: A Comparison of Terms Used, and Age and Gender Differences, in a Fourteen-Country International Comparison

abstract: The study of school bullying has recently assumed an international dimension, but is faced with difficulties in finding terms in different languages to correspond to the English word bullying. To investigate the meanings given to various terms, a set of 25 stick–figure cartoons was devised, covering a range of social situations between peers. These cartoons were shown to samples of 8– and 14–year–old pupils (N= 1,245; n= 604 at 8 years, n= 641 at 14 years) in schools in 14 different countries, who judged whether various native terms cognate to bullying, applied to them. Terms from 10 Indo–European languages and three Asian languages were sampled. Multidimensional scaling showed that 8–year–olds primarily discriminated nonaggressive and aggressive cartoon situations; however, 14–year–olds discriminated fighting from physical bullying, and also discriminated verbal bullying and social exclusion. Gender differences were less appreciable than age differences. Based on the 14–year–old data, profiles of 67 words were then constructed across the five major cartoon clusters. The main types of terms used fell into six groups: bullying (of all kinds), verbal plus physical bullying, solely verbal bullying, social exclusion, solely physical aggression, and mainly physical aggression. The findings are discussed in relation to developmental trends in how children understand bullying, the inferences that can be made from cross–national studies, and the design of such studies.


title: Comparison of two approaches to building a vertical search tool: a case study in the nanotechnology domain

abstract: As the Web has been growing exponentially, it has become increasingly difficult to search for desired information. In recent years, many domain-specific (vertical) search tools have been developed to serve the information needs of specific fields. This paper describes two approaches to building a domain-specific search tool. We report our experience in building two different tools in the nanotechnology domain -- (1) a server-side search engine, and (2) a client-side search agent. The designs of the two search systems are presented and discussed, and their strengths and weaknesses are compared. Some future research directions are also discussed.


title: Memory and the hippocampus: a synthesis from findings with rats, monkeys, and humans.

abstract: This article considers the role of the hippocampus in memory function. A central thesis is that work with rats, monkeys, and humans--which has sometimes seemed to proceed independently in 3 separate literatures--is now largely in agreement about the function of the hippocampus and related structures. A biological perspective is presented, which proposes multiple memory systems with different functions and distinct anatomical organizations. The hippocampus (together with anatomically related structures) is essential for a specific kind of memory, here termed declarative memory (similar terms include explicit and relational). Declarative memory is contrasted with a heterogeneous collection of nondeclarative (implicit) memory abilities that do not require the hippocampus (skills and habits, simple conditioning, and the phenomenon of priming). The hippocampus is needed temporarily to bind together distributed sites in neocortex that together represent a whole memory.


title: Sports VR Content Generation from Regular Camera Feeds

abstract: With the recent availability of commodity Virtual Reality (VR) products, immersive video content is receiving a significant interest. However, producing high-quality VR content often requires upgrading the entire production pipeline, which is costly and time-consuming. In this work, we propose using video feeds from regular broadcasting cameras to generate immersive content. We utilize the motion of the main camera to generate a wide-angle panorama. Using various techniques, we remove the parallax and align all video feeds. We then overlay parts from each video feed on the main panorama using Poisson blending. We examined our technique on various sports including basketball, ice hockey and volleyball. Subjective studies show that most participants rated their immersive experience when viewing our generated content between Good to Excellent. In addition, most participants rated their sense of presence to be similar to ground-truth content captured using a GoPro Omni 360 camera rig.


title: Digital Game-Based Learning Supports Student Motivation, Cognitive Success, and Performance Outcomes.

abstract: Traditional multimedia learning is primarily based on the cognitive load concept of information processing theory. Recent digital game-based learning (DGBL) studies have focused on exploring content support for learning motivation and related game characteristics. Motivation, volition, and performance (MVP) theory indicates that cognitive load and learning motivation simultaneously influence performance. To provide efficient management of learning effectiveness by understanding the latent relationship among cognitive load, motivation, and performance, this study investigated 63 university students using an online game entitled “Operating a Small Factory in Computer-Aided Manufacturing” for 8 weeks. We collected data based using an instructional materials motivation survey, a cognitive load scale, and performance (skill and cognitive) scales. The data analysis results showed that motivation and cognitive load exhibited a significant canonical correlation with performance. This preliminary finding suggests that when designing DGBL, designers should increase motivation and germane cognitive load to enhance learning effectiveness. In addition, the attention component must be compromised. However, relevance, satisfaction, and confidence do not conflict. This study proposes an application of game characteristics to the attention, relevance, satisfaction, and confidence model, and integrated multimedia effects in using DGBL design methods.


title: Order-independent constraint-based causal structure learning

abstract: We consider constraint-based methods for causal structure learning, such as the PC-, FCI-, RFCI- and CCD- algorithms (Spirtes et al., 1993, 2000; Richardson, 1996; Colombo et al., 2012; Claassen et al., 2013). The first step of all these algorithms consists of the adjacency search of the PC-algorithm. The PC-algorithm is known to be order-dependent, in the sense that the output can depend on the order in which the variables are given. This order-dependence is a minor issue in low-dimensional settings. We show, however, that it can be very pronounced in high-dimensional settings, where it can lead to highly variable results. We propose several modifications of the PC-algorithm (and hence also of the other algorithms) that remove part or all of this order-dependence. All proposed modifications are consistent in high-dimensional settings under the same conditions as their original counterparts. We compare the PC-, FCI-, and RFCI-algorithms and their modifications in simulation studies and on a yeast gene expression data set. We show that our modifications yield similar performance in low-dimensional settings and improved performance in high-dimensional settings. All software is implemented in the R-package pcalg.


title: Recommending twitter users to follow using content and collaborative filtering approaches

abstract: Recently the world of the web has become more social and more real-time. Facebook and Twitter are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research. In this paper we focus on one of the key features of the social web, namely the creation of relationships between users. Like recent research, we view this as an important recommendation problem -- for a given user, UT which other users might be recommended as followers/followees -- but unlike other researchers we attempt to harness the real-time web as the basis for profiling and recommendation. To this end we evaluate a range of different profiling and recommendation strategies, based on a large dataset of Twitter users and their tweets, to demonstrate the potential for effective and efficient followee recommendation.


Reviving the Past Conventions of realism in the virtual reconstruction of Rome Reborn


SPOT Poachers in Action: Augmenting Conservation Drones With Automatic Detection in Near Real Time.


title: An enhanced 16nm CMOS technology featuring 2nd generation FinFET transistors and advanced Cu/low-k interconnect for low power and high performance applications

abstract: Advancing the state-of-the-art 16nm technology reported last year, an enhanced 16nm CMOS technology featuring the second generation FinFET transistors and advanced Cu/low-k interconnect is presented. Core devices are re-optimized to provide additional 15% speed boost or 30% power reduction. Device overdrive capability is also extended by 70mV through reliability enhancement. Superior 128Mb High Density (HD) SRAM Vccmin capability of 450mV is achieved with variability reduction for the first time. Metal capacitance reduction by ∼9% is realized with advanced interconnect scheme to enable dynamic power saving.


title: Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation

abstract: Deep learning (DL) networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge datasets [3,11,16], especially for large pathologies. However, in the context of diseases such as Multiple Sclerosis (MS), monitoring all the focal lesions visible on MRI sequences, even very small ones, is essential for disease staging, prognosis, and evaluating treatment efficacy. Moreover, producing deterministic outputs hinders DL adoption into clinical routines. Uncertainty estimates for the predictions would permit subsequent revision by clinicians. We present the first exploration of multiple uncertainty estimates based on Monte Carlo (MC) dropout [4] in the context of deep networks for lesion detection and segmentation in medical images. Specifically, we develop a 3D MS lesion segmentation CNN, augmented to provide four different voxel-based uncertainty measures based on MC dropout. We train the network on a proprietary, large-scale, multi-site, multi-scanner, clinical MS dataset, and compute lesion-wise uncertainties by accumulating evidence from voxel-wise uncertainties within detected lesions. We analyze the performance of voxel-based segmentation and lesion-level detection by choosing operating points based on the uncertainty. Empirical evidence suggests that uncertainty measures consistently allow us to choose superior operating points compared only using the network's sigmoid output as a probability.


title: A Set-Aware Key-Value Store on Shingled Magnetic Recording Drives with Dynamic Band

abstract: Key-value (KV) stores play an increasingly critical role in supporting diverse large-scale applications in modern data centers hosting terabytes of KV items which even might reside on a single server due to virtualization purpose. The combination of ever growing volume of KV items and storage/application consolidation is driving a trend of high storage density for KV stores. Shingled Magnetic Recording (SMR) represents a promising technology for increasing disk capacity, but it comes at a cost of poor random write performance and severe I/O amplification. Applications/software working with SMR devices need to be designed and optimized in an SMR-friendly manner. In this work, we present SEALDB, a Log-Structured Merge tree (LSM-tree) based key-value store that is specifically optimized for and works well with SMR drives via adequately addressing the poor random writes and severe I/O amplification issues. First, for LSM-trees, SEALDB concatenates SSTables of each compaction, and groups them into sets. Taking sets as the basic unit for compactions, SEALDB improves compaction efficiency by mitigating random I/Os. Second, SEALDB creates varying size bands on HM-SMR drives, named dynamic bands. Dynamic bands not only accommodate the storage of sets, but also eliminate the auxiliary write amplification from SMR drives. We demonstrate the advantages of SEALDB via extensive experiments in various workloads. Overall, SEALDB delivers impressive performance improvement. Compared with LevelDB, SEALDB is 3.42 times faster on random load due to improved compaction efficiency and eliminated auxiliary write amplification on SMR drives.


title: A Study of Deep Feature Fusion based Methods for Classifying Multi-lead ECG

abstract: An automatic classification method has been studied to effectively detect and recognize Electrocardiogram (ECG). Based on the synchronizing and orthogonal relationships of multiple leads, we propose a Multi-branch Convolution and Residual Network (MBCRNet) with three kinds of feature fusion methods for automatic detection of normal and abnormal ECG signals. Experiments are conducted on the Chinese Cardiovascular Disease Database (CCDD). Through 10-fold cross-validation, we achieve an average accuracy of 87.04% and a sensitivity of 89.93%, which outperforms previous methods under the same database. It is also shown that the multi-lead feature fusion network can improve the classification accuracy over the network only with the single lead features.


title: Lower limb rehabilitation robot

abstract: This paper describes a new prototype of lower limb rehabilitation robot (for short: LLRR), including its detailed structure, operative principles, manipulative manual and control mode which give considerate protection to patients. It implements the programmable process during the course of the limbs rehabilitation, furthermore, renders variable and predetermined step posture and force sensing. LLRR could assist patient in-- simulating normal people's footsteps, exercising leg muscles, gradually recovering the neural control toward walking function and finally walking in normal way utterly. Such robot is comprised with steps posture controlling system and weight alleviation controlling mechanism.


title: Crisp Boundary Detection Using Pointwise Mutual Information

abstract: Detecting boundaries between semantically meaningful objects in visual scenes is an important component of many vision algorithms. In this paper, we propose a novel method for detecting such boundaries based on a simple underlying principle: pixels belonging to the same object exhibit higher statistical dependencies than pixels belonging to different objects. We show how to derive an affinity measure based on this principle using pointwise mutual information, and we show that this measure is indeed a good predictor of whether or not two pixels reside on the same object. Using this affinity with spectral clustering, we can find object boundaries in the image – achieving state-of-the-art results on the BSDS500 dataset. Our method produces pixel-level accurate boundaries while requiring minimal feature engineering.


title: Exome sequencing unravels unexpected differential diagnoses in individuals with the tentative diagnosis of Coffin–Siris and Nicolaides–Baraitser syndromes

abstract: Coffin-Siris syndrome (CSS) and Nicolaides-Baraitser syndrome (NCBRS) are rare intellectual disability/congenital malformation syndromes that represent distinct entities but show considerable clinical overlap. They are caused by mutations in genes encoding members of the BRG1- and BRM-associated factor (BAF) complex. However, there are a number of patients with the clinical diagnosis of CSS or NCBRS in whom the causative mutation has not been identified. In this study, we performed trio-based whole-exome sequencing (WES) in ten previously described but unsolved individuals with the tentative diagnosis of CSS or NCBRS and found causative mutations in nine out of ten individuals. Interestingly, our WES analysis disclosed overlapping differential diagnoses including Wiedemann-Steiner, Kabuki, and Adams-Oliver syndromes. In addition, most likely causative de novo mutations were identified in GRIN2A and SHANK3. Moreover, trio-based WES detected SMARCA2 and SMARCA4 deletions, which had not been annotated in a previous Haloplex target enrichment and next-generation sequencing of known CSS/NCBRS genes emphasizing the advantages of WES as a diagnostic tool. In summary, we discuss the phenotypic and diagnostic challenges in clinical genetics, establish important differential diagnoses, and emphasize the cardinal features and the broad clinical spectrum of BAF complex disorders and other disorders caused by mutations in epigenetic landscapers.


title: Distributed Time-Varying Graph Filtering

abstract: One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation. ::: The design philosophy, which allows us to design the ARMA coefficients independently from the underlying graph, renders the ARMA graph filters suitable in static and, particularly, time-varying settings. The latter occur when the graph signal and/or graph are changing over time. We show that in case of a time-varying graph signal our approach extends naturally to a two-dimensional filter, operating concurrently in the graph and regular time domains. We also derive sufficient conditions for filter stability when the graph and signal are time-varying. The analytical and numerical results presented in this paper illustrate that ARMA graph filters are practically appealing for static and time-varying settings, accompanied by strong theoretical guarantees.


title: Association rules-based Ontology Enrichment

abstract: Among the most powerful tools for knowledge representation, we cite the ontology which allows knowledge structuring and sharing. In order to achieve efficient domain knowledge bases content, the latter has to establish well linked and knowledge between its components. In parallel, data mining techniques are used to discover hidden structures within large databases. In particular, association rules are used to discover co-occurrence relationships from past experiences. In this context, we propose, to develop a method to enrich existing ontologies with the identification of novel semantic relations between concepts in order to have a better coverage of the domain knowledge. The enrichment process is realized through discovered association rules. Nevertheless, this technique generates a large number of rules, where some of them, may be evident or already declared in the knowledge base. To this end, the generated association rules are categorized into three main classes: known knowledge, novel knowledge and unexpected rules. We demonstrate the applicability of this method using an existing mammographic ontology and patient's records.


title: Smart metering and home automation solutions for the next decade

abstract: This paper emphasizes the use of the smart metering and home automation technologies for efficient utilization of energy, thus paving the way for a cleaner and greener environment for future generations. Next decade will revolutionize smart metering, Information and communications technology (ICT) and home automation by making it affordable, efficient and within the common man's reach. World over, tremendous work is going on in Advanced Metering Infrastructure (AMI), Smart Metering and Home Automation. Short distance Radio Frequency (RF) technologies like ZigBee#, Z-Wave#, Low Power Radio and Distribution Line Carrier (DLC) are ready to enter every household as the Home Area Network (HAN) of the future. Countries like USA, Australia and Italy etc have already started using AMI solutions while countries like China, South Korea, Austria, Spain, Sweden, Finland, Denmark, Netherlands, Norway, Ireland and UK are in process of implementing AMI [9]. In India under the Restructured Accelerated Power Development & Reform Program (R-APDRP), in the XI five year plan, smart metering and ICT solutions have been planned to increase efficiency, loss reduction, reduce energy theft and for efficient data collection for better energy accounting in the electrical sector. This is the beginning of the AMI in India [12].


title: SecGDB: Graph Encryption for Exact Shortest Distance Queries with Efficient Updates

abstract: In the era of big data, graph databases have become increasingly important for NoSQL technologies, and many systems can be modeled as graphs for semantic queries. Meanwhile, with the advent of cloud computing, data owners are highly motivated to outsource and store their massive potentially-sensitive graph data on remote untrusted servers in an encrypted form, expecting to retain the ability to query over the encrypted graphs.


title: Mitochondrial dynamics and mitochondrial quality control

abstract: Mitochondria are cellular energy powerhouses that play important roles in maintaining cell survival, cell death and cellular metabolic homeostasis. Timely removal of damaged mitochondria via autophagy (mitophagy) is thus critical for cellular homeostasis and function. Mitochondria are reticular organelles that have high plasticity for their dynamic structures and constantly undergo fission and fusion as well as movement through the cytoskeleton. In this review, we discuss the most recent progress on the molecular mechanisms and roles of mitochondrial fission/fusion and mitochondrial motility in mitophagy. We also discuss multiple pathways leading to the quality control of mitochondria in addition to the traditional mitophagy pathway under different conditions.


title: Understanding Deep Architectures using a Recursive Convolutional Network

abstract: A key challenge in designing convolutional network models is sizing them appropriately. Many factors are involved in these decisions, including number of layers, feature maps, kernel sizes, etc. Complicating this further is the fact that each of these influence not only the numbers and dimensions of the activation units, but also the total number of parameters. In this paper we focus on assessing the independent contributions of three of these linked variables: The numbers of layers, feature maps, and parameters. To accomplish this, we employ a recursive convolutional network whose weights are tied between layers; this allows us to vary each of the three factors in a controlled setting. We find that while increasing the numbers of layers and parameters each have clear benefit, the number of feature maps (and hence dimensionality of the representation) appears ancillary, and finds most of its benefit through the introduction of more weights. Our results (i) empirically confirm the notion that adding layers alone increases computational power, within the context of convolutional layers, and (ii) suggest that precise sizing of convolutional feature map dimensions is itself of little concern; more attention should be paid to the number of parameters in these layers instead.


title: Green Software Engineering: The Curse of Methodology

abstract: Computer Science often seems distant from its natural science cousins, especially software engineering which feels closer to sociology and psychology than to physics. Physical measurements are often rare in software engineering, except in a few niches. One such important niche is that of software energy consumption, green mining, green IT, and sustainable computing, which all fall under the umbrella of green software engineering. With the physical measurement of energy consumption comes all of the limitations of measurement and experimentation that exist in the natural sciences and engineering. Issues abound, from attribution of energy use, isolation of components, to replicable experiments. These get further complicated by cloud computing whereby systems are virtualized and attribution of resource usage is a serious issue. Thus in this work we discuss the current state of software energy consumption, and where will it go.


title: PVM: A Framework for Parallel Distributed Computing

abstract: The PVM system is a programming environment for the development and execution of large concurrent or parallel applications that consist of many interacting, but relatively independent, components. It is intended to operate on a collection of heterogeneous computing elements interconnected by one or more networks. The participating processors may be scalar machines, multiprocessors, or special-purpose computers, enabling application components to execute on the architecture most appropriate to the algorithm. PVM provides a straightforward and general inferface that permits the description of various types of algorithms (and their interactions), while the underlying infrastructure permits the execution of applications on a virtual computing environment that supports multiple parallel computation models. PVM contains facilities for concurrent, sequential or conditional execution of application components, is portable to a variety of architectures, and supports certain forms of error detection and recovery.


title: Can We Trust the Privacy Policies of Android Apps?

abstract: Recent years have witnessed the sharp increase of malicious apps that steal users' personal information. To address users' concerns about privacy risks, more and more apps are accompanied with privacy policies written in natural language because it is difficult for users to infer an app's behaviors according to the required permissions. However, little is known whether these privacy policies are trustworthy or not. It is worth noting that a questionable privacy policy may result from careless preparation by an app developer or intentional deception by an attacker. In this paper, we conduct the first systematic study on privacy policy by proposing a novel approach to automatically identify three kinds of problems in privacy policy. After tackling several challenging issues, we realize our approach in a system, named PPChecker, and evaluate it with real apps and privacy policies. The experimental results show that PPChecker can effectively identify questionable privacy policies with high precision. Moreover, applying PPChecker to 1,197 popular apps, we found that 282 apps (i.e., 23.6%) have at least one kind of problems. This study sheds light on the research of improving and regulating apps' privacy policies.


title: A hybrid hydrostatic transmission and human-safe haptic telepresence robot

abstract: We present a new type of hydrostatic transmission that uses a hybrid air-water configuration, analogous to N+1 cable-tendon transmissions, using N hydraulic lines and 1 pneumatic line for a system with N degrees of freedom (DOFs). The common air-filled line preloads all DOFs in the system, allowing bidirectional operation of every joint. This configuration achieves the high stiffness of a water-filled transmission with half the number of bulky hydraulic lines. We implemented this transmission using pairs of rolling-diaphragm cylinders to form rotary hydraulic actuators, with a new design achieving a 600-percent increase in specific work density per cycle. These actuators were used to build a humanoid robot with two 4-DOF arms, connected via the hydrostatic transmission to an identical master. Stereo cameras mounted on a 2-DOF servo-controlled neck stream live video to the operator's head-mounted display, which in turn sends the real-time attitude of the operator's head to the neck servos in the robot. The operator is visually immersed in the robot's physical workspace, and through the bilateral coupling of the low-impedance hydrostatic transmission, directly feels interaction forces between the robot and external environment. We qualitatively assessed the performance of this system for remote object manipulation and use as a platform to safely study physical human-robot interaction.


title: Transformation of ARIS Markup Language to EPML

abstract: This paper presents an approach to map EPC business process models available in EPML to Scalable Vector Graphics (SVG) and websites. This mapping has been implemented as an XSLT transformation program called EPML2SVG. Such a transformation may serve as a reference for visual tools that use EPML. Furthermore, business process models available in SVG can be used in web presentations and they can be viewed without buying a licence of a business process modelling tool. Moreover, EPML2SVG leverages EPML as an interchange format for EPCs. Websites generated by EPML2SVG contain SVG graphics for each EPC model and an HTML navigation structure based on links to each process models. We discuss design decisions of the program and illustrate the generated SVG by an example.


title: A Walk-Based Semantically Enriched Tree Kernel Over Distributed Word Representations

abstract: In this paper, we propose a walk-based graph kernel that generalizes the notion of treekernels to continuous spaces. Our proposed approach subsumes a general framework for word-similarity, and in particular, provides a flexible way to incorporate distributed representations. Using vector representations, such an approach captures both distributional semantic similarities among words as well as the structural relations between them (encoded as the structure of the parse tree). We show an efficient formulation to compute this kernel using simple matrix operations. We present our results on three diverse NLP tasks, showing state-of-the-art results.


title: Discriminative Phrase Embedding for Paraphrase Identification

abstract: This work, concerning paraphrase identification task, on one hand contributes to expanding deep learning embeddings to include continuous and discontinuous linguistic phrases. On the other hand, it comes up with a new scheme TF-KLD-KNN to learn the discriminative weights of words and phrases specific to paraphrase task, so that a weighted sum of embeddings can represent sentences more effectively. Based on these two innovations we get competitive state-of-the-art performance on paraphrase identification.


title: Mining semantic structures in movies

abstract: 'Video data mining' is a technique to discover useful patterns from videos. It plays an important role in efficient video management. Particularly, we concentrate on extracting useful editing patterns from movies. These editing patterns are useful for an amateur editor to produce a new, more attractive video. But, it is essential to extract editing patterns associated with their semantic contents, called 'semantic structures'. Otherwise the amateur editor can't determine how to use the extracted editing patterns during the process of editing a new video. In this paper, we propose two approaches to extract semantic structures from a movie, based on two different time series models of the movie. In one approach, the movie is represented as a multi-stream of metadata derived from visual and audio features in each shot. In another approach, the movie is represented as one-dimensional time series consisting of durations of target character's appearance and disappearance. To both time series models, we apply data mining techniques. As a result, we extract the semantic structures about shot transitions and about how the target character appears on the screen and disappears from the screen.


title: A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification

abstract: Forest and rural fires are one of the main causes of environmental degradation in Mediterranean countries. Existing fire detection systems only focus on detection, but not on the verification of the fire. However, almost all of them are just simulations, and very few implementations can be found. Besides, the systems in the literature lack scalability. In this paper we show all the steps followed to perform the design, research and development of a wireless multisensor network which mixes sensors with IP cameras in a wireless network in order to detect and verify fire in rural and forest areas of Spain. We have studied how many cameras, sensors and access points are needed to cover a rural or forest area, and the scalability of the system. We have developed a multisensor and when it detects a fire, it sends a sensor alarm through the wireless network to a central server. The central server selects the closest wireless cameras to the multisensor, based on a software application, which are rotated to the sensor that raised the alarm, and sends them a message in order to receive real-time images from the zone. The camera lets the fire fighters corroborate the existence of a fire and avoid false alarms. In this paper, we show the test performance given by a test bench formed by four wireless IP cameras in several situations and the energy consumed when they are transmitting. Moreover, we study the energy consumed by each device when the system is set up. The wireless sensor network could be connected to Internet through a gateway and the images of the cameras could be seen from any part of the world.


title: A Data Mining Framework for Optimal Product Selection in Retail Supermarket Data: The Generalized PROFSET Model

abstract: In recent years, data mining researchers have developed efficient association rule algorithms for retail market basket analysis. Still, retailers often complain about how to adopt association rules to optimize concrete retail marketing-mix decisions. It is in this context that, in a previous paper, the authors have introduced a product selection model called PROFSET. This model selects the most interesting products from a product assortment based on their cross-selling potential given some retailer defined constraints. However this model suffered from an important deficiency: it could not deal effectively with supermarket data, and no provisions were taken to include retail category management principles. Therefore, in this paper, the authors present an important generalization of the existing model in order to make it suitable for supermarket data as well, and to enable retailers to add category restrictions to the model. Experiments on real world data obtained from a Belgian supermarket chain produce very promising results and demonstrate the effectiveness of the generalized PROFSET model.


title: Medico-legal evaluation of deaths due to decapitation

abstract: Decapitation of bodies is a rare event in the civilian setting and is reported to account for approximately 0.1 % of medico-legal autopsies. Suicidal decapitation is a very rare method of self-destruction. In suicidal decapitation, the favored method is the one involving trains. Other encountered methods are decapitation in suicidal hanging, vehicle-assisted ligature suicide. Though being encountered rarely decapitation by guillotine was also reported. In this retrospective study, in order to depict characteristic features of deaths with decapitation, we presented the findings of fatalities with decapitation over a 10-year period in a medico-legal autopsy series in Istanbul, Turkey. A total of 36270 forensic autopsies were performed over the period of the study and in 19 cases, the bodies were found to be decapitated (0.05 %). The age range of decapitated bodies was 18 to 71 years (average 39,1 years), with a male to female ratio of 13/6. There was only one case of suicide and the way used for suicide was a mechanism like guillotine. In this case, a guillotine-like device designed by male victim had been used for deliberately decapitating the body. The age of the suicide case was 41 years. There was no female suicide case. There were 13 deaths of accidental origin. 6 vehicle crashes and 7 train-pedestrian accidents. The age range of individuals died in accidents was 26 to 68 years (average 36.8 years) with a male to female ratio of 11/2. During the study period the number of the homicide cases with decapitation was 5. The age range of homicide cases was 18 to 71 years (average, 45.2 years), with a male to female ratio of ¼. There were multiple traumas in other body parts of the bodies of homicide cases together with decapitation. In majority of homicide cases, multiple stab wounds were detected. Decapitation level of vertebrae was at the level of 3-4 cervical vertebrae in 9 cases and different levels at cervical region in other cases.


title: Journal of Experimental Psychology: Human Perception and Performance The Attentional Cost of Receiving a Cell Phone Notification

abstract: It is well documented that interacting with a mobile phone is associated with poorer performance on concurrently performed tasks because limited attentional resources must be shared between tasks. However, mobile phones generate auditory or tactile notifications to alert users of incoming calls and messages. Although these notifications are generally short in duration, they can prompt task-irrelevant thoughts, or mind wandering, which has been shown to damage task performance. We found that cellular phone notifications alone significantly disrupted performance on an attention-demanding task, even when participants did not directly interact with a mobile device during the task. The magnitude of observed distraction effects was comparable in magnitude to those seen when users actively used a mobile phone, either for voice calls or text messaging. (PsycINFO Database Record Keywords: Driver distraction; Language: en


title: Hybrid Recommendation Approaches: Collaborative Filtering via Valuable Content Information

abstract: Collaborative filtering (CF) method has been successfully used in recommender systems to support product recommendation, but it has several limitations. This work uses customer demands derived from the frequent purchased products in each industry as valuable content information. Accordingly, this work explores two hybrid approaches each of which combines CF and customer demands to improve quality of recommendation. Valuable content information is also included as a factor in making recommendations for re-ranking candidate products. The experimental results indicate that the quality of recommendation obtained by the combined methods is promising.


A genetic model for colorectal tumorigenesis


title: The Study on the Effect of Background Music on Customer Waiting Timein Restaurant

abstract: Many scholars study shows that the background music is restaurant controllable environmental factor that can affect the customer waiting time, which has some management implications for solving customer wait time problem in the service sector. This article used experimental management method, randomly selected a large restaurant, and then ob- served each customer just coming in, recorded background music types (including no music, slow-paced and fast-paced) and customer waiting time. And we made comparative analysis of the differences between weekdays and weekends. Fi- nally, the experimental results and analysis are given: (1) Background music can really affect the customer's waiting time; (2) Slow-paced background music can extend the customer waiting time, meanwhile fast-paced background music will shorten the waiting time of customers; (3) On weekends average waiting time is longer than working day.


title: Alternatives to splay trees with O(log n) worst-case access times

abstract: Splay trees are a self adjusting form of search tree that supports access operations in O(log n) amortized time. Splay trees also have several amazing distribution sensitive properties, the strongest two of which are the working set theorem and the dynamic finger theorem. However, these two theorems are shown to poorly bound the performance of splay trees on some simple access sequences. The unified conjecture is presented, which subsumes the working set theorem and dynamic finger theorem, and accurately bounds the performance of splay trees over some classes of sequences where the existing theorems' bounds are not tight. While the unified conjecture for splay trees is unproven, a new data structure, the unified structure, is presented where the unified conjecture does hold. This structure also has a worst case of O(log n) per operation, in contrast to the O(n) worst case runtime of splay trees. A second data structure, the working set structure, is introduced. The working set structure has the same performance attributed to splay trees through the working set theorem, except the runtime is worst case per operation rather than amortized.


title: How Cosmopolitan Are Emojis?: Exploring Emojis Usage and Meaning over Different Languages with Distributional Semantics

abstract: Choosing the right emoji to visually complement or condense the meaning of a message has become part of our daily life. Emojis are pictures, which are naturally combined with plain text, thus creating a new form of language. These pictures are the same independently of where we live, but they can be interpreted and used in different ways. In this paper we compare the meaning and the usage of emojis across different languages. Our results suggest that the overall semantics of the subset of the emojis we studied is preserved across all the languages we analysed. However, some emojis are interpreted in a different way from language to language, and this could be related to socio-geographical differences.


title: Performance analysis of image segmentation for breast ultrasound images

abstract: Breast cancer is one of the main causes of women mortality worldwide. Ultrasonography (USG) is other modalities than mammography that capable to support radiologists in diagnosing breast cancer. However, the diagnosis may come with different interpretation depending on the radiologists experience. Therefore, Computer-Aided Diagnosis (CAD) is developed as a tool for radiologist's second opinion. CAD is built based on digital image processing of ultrasound (US) images which consists of several stages. Lesion segmentation is an important step in CAD system because it contains many important features for classification process related to lesion characteristics. This study provides a performance analysis and comparison of image segmentation for breast USG images. In this paper, several methods are presented such as a comprehensive comparison of adaptive thresholding, fuzzy C-Means (FCM), Fast Global Minimization for Active Contour (FGMAC) and Active Contours Without Edges (ACWE). The performance of these methods are evaluated with evaluation metrics Dice coefficient, Jaccard coefficient, FPR, FNR, Hausdorff distance, PSNR and MSSD parameters. Morphological operation is able to increase the performance of each segmentation methods. Overall, ACWE with morphological operation gives the best performance compare to the other methods with the similarity level of more than 90%.


title: Large-scale Ensemble Model for Customer Churn Prediction in Search Ads

abstract: Customer churn prediction is one of the most important issues in search ads business management, which is a multi-billion market. The aim of churn prediction is to detect customers with a high propensity to leave the ads platform, then to do analysis and increase efforts for retaining them ahead of time. Ensemble model combines multiple weak models to obtain better predictive performance, which is inspired by human cognitive system and is widely used in various applications of machine learning. In this paper, we investigate how the ensemble model of gradient boosting decision tree (GBDT) to predict whether a customer will be a churner in the foreseeable future based on its activities in the search ads. We extract two types of features for the GBDT: dynamic features and static features. For dynamic features, we consider a sequence of customers’ activities (e.g., impressions, clicks) during a long period. For static features, we consider the information of customers setting (e.g., creation time, customer type). We evaluated the prediction performance in a large-scale customer data set from Bing Ads platform, and the results show that the static and dynamic features are complementary, and get the AUC (area under the curve of ROC) value 0.8410 on the test set by combining all features. The proposed model is useful to predict those customers who will be churner in the near future on the ads platform, and it has been successfully daily run on the Bing Ads platform.


title: Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset

abstract: Abstract The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this challenge, realistic protection and investigation countermeasures, such as network intrusion detection and network forensic systems, need to be effectively developed. For this purpose, a well-structured and representative dataset is paramount for training and validating the credibility of the systems. Although there are several network datasets, in most cases, not much information is given about the Botnet scenarios that were used. This paper proposes a new dataset, so-called Bot-IoT, which incorporates legitimate and simulated IoT network traffic, along with various types of attacks. We also present a realistic testbed environment for addressing the existing dataset drawbacks of capturing complete network information, accurate labeling, as well as recent and complex attack diversity. Finally, we evaluate the reliability of the BoT-IoT dataset using different statistical and machine learning methods for forensics purposes compared with the benchmark datasets. This work provides the baseline for allowing botnet identification across IoT-specific networks. The Bot-IoT dataset can be accessed at Bot-iot (2018) [1] .


title: Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems

abstract: Solving math word problems is a challenging task that requires accurate natural language understanding to bridge natural language texts and math expressions. Motivated by the intuition about how human generates the equations given the problem texts, this paper presents a neural approach to automatically solve math word problems by operating symbols according to their semantic meanings in texts. This paper views the process of generating equation as a bridge between the semantic world and the symbolic world, where the proposed neural math solver is based on an encoder-decoder framework. In the proposed model, the encoder is designed to understand the semantics of problems, and the decoder focuses on tracking semantic meanings of the generated symbols and then deciding which symbol to generate next. The preliminary experiments are conducted in a dataset Math23K, and our model significantly outperforms both the state-of-the-art single model and the best non-retrieval-based model over about 10% accuracy, demonstrating the effectiveness of bridging the symbolic and semantic worlds from math word problems.


title: Spherical particle imaging and glare-ring spectroscopy with backscattered light

abstract: The imaging of a dielectric microsphere with linearly polarized backscattered light is investigated theoretically. Analytical expressions for the electric field and the imaged light intensity distribution in two dimensions are obtained for an arbitrary backscatter geometry, including both telescopic and microscopic arrangements. These distributions are shown to be in many cases more sensitive to small variations in the refractive index of the imaged particle than those obtained by imaging in the off-axis scattering. In microscopic geometry, the sensitivity may be further enhanced by the secondary imaging of the glare rings formed by the backscattered peripheral rays. The possibility of using the backscatter imaging for the absorption spectroscopy of small impurities is discussed.


title: Novel linearly and circularly polarized 60 GHz MEMS antennas on low- and high-resistivity silicon

abstract: Two novel MEMS antennas for 60 GHz applications are introduced in this paper. The first antenna is radiating linearly polarized wave, while circularly polarized wave can be radiated from the second antenna. Both antennas possess good isolation from the driving circuit via the presence of ground metal plane. The fabrication process requires only one silicon wafer with no need for wafer bonding or hybrid integration. The two antennas are radiating mainly from one side of the substrate, as indicated by high front-to-back ratio. They enjoy good polarization purity characterized by low cross-polarization level. Both high- and low-resistivity silicon are considered as substrates for each antenna. The first solution is associated with very high radiation efficiency and gain, and hence suitable for applications requires considerable distance between transmitter and receiver. On the other hand, the much cheaper and more electronics-compatible low-resistivity silicon solution offers relatively low radiation efficiency and gain, which makes it more suitable for in-door communication systems.