title: Learning geometric graph grammars

abstract: We introduce geometric graph grammars, demonstrate how they can generate geometric structures, and introduce an algorithm for their automatic learning (inverse procedural modeling). Our approach extends the concept of graph grammars to allow for coding not only topological data, but also geometry. Forward modeling generates geometric graphs and considers various strategies for node connectivity. Inverse procedural modeling performs learning of geometric graphs, by discovering repeated structures and their connectivity. These structures are encoded into geometric graph grammar rewriting rules. We demonstrate usability of our approach on an example using urban networks. Graph learning is reasonably fast; in our implementation, learning of a road network with 72k vertices and 100k edges is performed in less than one minute.


title: An ant system based on moderate search for TSP

abstract: Ant Colony Optimization (ACO) algorithms often suffer from criticism for the ::: local optimum and premature convergence. In order to overcome these inherent ::: shortcomings shared by most ACO algorithms, we divide the ordinary ants into ::: two types: the utilization-oriented ants and the exploration-oriented ants. ::: The utilization-oriented ants focus on constructing solutions based on the ::: learned experience like ants in many other ACO algorithms. On the other hand, ::: inspired by the adaptive behaviors of some real-world Monomorium ant species ::: who tend to select paths with moderate pheromone concentration, a novel ::: search strategy, that is, a completely new transition rule is designed for ::: the exploration-oriented ants to explore more unknown solutions. In addition, ::: a new corresponding update strategy is also employed. Moreover, applying the ::: new search strategy and update strategy, we propose an improved version of ::: ACO algorithm-Moderate Ant System. This improved algorithm is experimentally ::: turned out to be effective and competitive.


title: OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World

abstract: While deep reinforcement learning techniques have recently produced considerable achievements on many decision-making problems, their use in robotics has largely been limited to simulated worlds or restricted motions, since unconstrained trial-and-error interactions in the real world can have undesirable consequences for the robot or its environment. To overcome such limitations, we propose a novel reinforcement learning architecture, OptLayer, that takes as inputs possibly unsafe actions predicted by a neural network and outputs the closest actions that satisfy chosen constraints. While learning control policies often requires carefully crafted rewards and penalties while exploring the range of possible actions, OptLayer ensures that only safe actions are actually executed and unsafe predictions are penalized during training. We demonstrate the effectiveness of our approach on robot reaching tasks, both simulated and in the real world.


title: Highly reliable operation of InGaAlAs mesa-waveguide photodiodes in a humid ambient

abstract: Reliable mesa-type waveguide photodiode operation in a humid ambient is demonstrated. Median lifetime at 45°C/50%RH estimated from a pressure cooker test was 20 years, which is sufficient to apply this device in low-cost nonhermetic modules.


title: ShuffleSeg: Real-time Semantic Segmentation Network

abstract: Real-time semantic segmentation is of significant importance for mobile and robotics related applications. We propose a computationally efficient segmentation network which we term as ShuffleSeg. The proposed architecture is based on grouped convolution and channel shuffling in its encoder for improving the performance. An ablation study of different decoding methods is compared including Skip architecture, UNet, and Dilation Frontend. Interesting insights on the speed and accuracy tradeoff is discussed. It is shown that skip architecture in the decoding method provides the best compromise for the goal of real-time performance, while it provides adequate accuracy by utilizing higher resolution feature maps for a more accurate segmentation. ShuffleSeg is evaluated on CityScapes and compared against the state of the art real-time segmentation networks. It achieves 2x GFLOPs reduction, while it provides on par mean intersection over union of 58.3% on CityScapes test set. ShuffleSeg runs at 15.7 frames per second on NVIDIA Jetson TX2, which makes it of great potential for real-time applications.


title: Summary of the Third Workshop on Metallization for Crystalline Silicon Solar Cells

abstract: Abstract The Third Workshop on Metallization for Crystalline Silicon Solar Cells was held in Charleroi, Belgium on 25 and 26 October 2011. This paper reports on the different contributions and discussions that took place. Great progress has recently been achieved in paste development, which yet again has stretched the lifetime of screen-printed metallization technology. Nevertheless, a transition towards Cu plating-based metallization is anticipated, and solutions to the different challenges were presented and discussed.


title: Histopathology of farmed freshwater fish infested with different helminthes

abstract: n this study, histopathology of tissues of different cultured fishes (Cyprinus carpio, Clarias gariepinus, Oreochromis aureus, Sarotherodon galilaeus and Tilapia zillii) due to different helminthes had been investigated. Obviously, the direct effect of helminth parasites mainly depend on their attachment organs. Herein, helminthes belong to monogenean, digenean, cestode, nematode and acanthocephalan parasites, were identified as Cichlidogyrus halli typicus, Dactylogyrus afrobarbae, Orientocreadium batrachoides, Polyonchobothrium clarias, Paracamallanus cyathopharynx, Procamallanus laevichonchus and Acanthosentis tilapiae respectively. Severe damage occurred to the gill tissue due to the monogeneans opithohaptor. Limited lesions to the infected tissue of the fish host due to the nematodes were induced at the attachment site by the parasite buccal capsule. Deep penetration of intestinal tissue of the fish infested with the cestode Polyonchobothrium clarias was occurred due to the parasitic scolex. The digenean Orientocreadium batrachoides induced damage by its sucker at the attachment site to the infected epithelia of the host tissue as well. The acanthocephalan parasite Acanthosentis tilapiae caused severe rupture of the infected tissue by its proboscis hooks at the attachment site. Moreover, it may lead to complete decompose to intestinal tissue of the host. It is worthy to conclude that, in cases of the intense infestation negative impact of the parasite become more dangerous.


title: A Multi-objective Framework for Location Recommendation Based on User Preference

abstract: Location recommendation has attracted increasing attention in recent years. This paper proposes a novel multi-objective framework for location recommendation based on user preference. Under this framework, user preference can be separated into common preference and individual preference. Then two contradictory objective functions are designed to describe these two kinds of preferences. It is difficult to optimize these two objective functions simultaneously. In this paper, a novel multi-objective evolutionary algorithm is proposed to optimize these two objective functions. The proposed algorithm can make a good balance between these two objective functions. Experiments on two real application recommendation scenarios: Foursquare dataset and Gowalla dataset show that the proposed algorithm is effective to recommend locations.


title: Beclin 2 Functions in Autophagy, Degradation of G Protein-Coupled Receptors, and Metabolism

abstract: The molecular mechanism of autophagy and its relationship to other lysosomal degradation pathways remain incompletely understood. Here, we identified a previously uncharacterized mammalian-specific protein, Beclin 2, which, like Beclin 1, functions in autophagy and interacts with class III PI3K complex components and Bcl-2. However, Beclin 2, but not Beclin 1, functions in an additional lysosomal degradation pathway. Beclin 2 is required for ligand-induced endolysosomal degradation of several G protein-coupled receptors (GPCRs) through its interaction with GASP1. Beclin 2 homozygous knockout mice have decreased embryonic viability, and heterozygous knockout mice have defective autophagy, increased levels of brain cannabinoid 1 receptor, elevated food intake, and obesity and insulin resistance. Our findings identify Beclin 2 as a converging regulator of autophagy and GPCR turnover and highlight the functional and mechanistic diversity of Beclin family members in autophagy, endolysosomal trafficking, and metabolism.


title: Mechanical characteristics of graphene nanoribbons encapsulated in single-walled carbon nanotubes using molecular dynamics simulations

abstract: Abstract We perform molecular dynamics (MD) simulations of nanoindentation to study the mechanical properties of a graphene nanoribbon encapsulated in a single-walled carbon nanotube (GNR@SWCNT). The effects of different temperatures, nanotube diameters, and slenderness ratios on the properties of the GNR@SWCNT are examined. Results show that the maximum load and contact stiffness increase with an increase of temperature. But the adhesion force and ratio of springback are low at a high temperature of 1200 K. The maximum load and contact stiffness are high for the nanoindentation of the GNR@SWCNT with a low slenderness ratio. In addition, the GNR@SWCNT has a more than 15% rate of springback exhibiting a superelastic nanocomposite behavior. The results are useful for engineering applications of nanocomposites composed of carbon nanotubes and graphene materials.


title: Autoimmune comorbidities in Hashimoto’s thyroiditis: different patterns of association in adulthood and childhood/adolescence

abstract: OBJECTIVE ::: Hashimoto's thyroiditis (HT), the most common autoimmune thyroid disease at any age, is often associated with other autoimmune diseases. The present study was aimed to describe the type and frequency of non-thyroidal autoimmune diseases (NTADs) in HT patients and to delineate the clinical pattern of diseases clustering in pediatric/adolescent and adult age. ::: ::: ::: DESIGN ::: Cross-sectional study. ::: ::: ::: METHODS ::: 1053 newly diagnosed HT patients (500 adults (467 F, mean age 40.2 ± 13.7 years) and 553 children/adolescents (449 F, mean age 11.1 ± 3.0 years)) were evaluated for common NTADs by means of careful recording of medical history, physical examination and assessment of selected autoantibody profiles. ::: ::: ::: RESULTS ::: The prevalence of associated NTADs was significantly higher in adults than that in pediatric/adolescent HT patients (P < .0001). In addition, the number of adult patients suffering from two or more associated NTADs was significantly higher than that of children/adolescent (P < 0.0001). A female prevalence was evident in both cohorts, but was significant in the adults (P < 0.0001). The epidemiological distribution of NTADs was strongly different in the two cohorts, the most frequent associated diseases being arthropathies and connective tissue diseases in adults and type 1 diabetes and coeliac disease in children/adolescents. Skin diseases were represented with similar prevalence in both cohorts, vitiligo being the most common. ::: ::: ::: CONCLUSIONS ::: Age at HT presentation may influence autoimmune diseases clustering, favoring the association of specific NTADs in different ages of life. Moreover, the association between HT and NTADs increases with age and occurs most frequently in adults.


title: Agents and the Semantic Web

abstract: Many challenges of bringing communicating multi-agent systems to the World Wide Web require ontologies. The integration of agent technology and ontologies could significantly affect the use of Web services and the ability to extend programs to perform tasks for users more efficiently and with less human intervention.


title: A proportional share resource allocation algorithm for real-time, time-shared systems

abstract: We propose and analyze a proportional share resource allocation algorithm for realizing real-time performance in time-shared operating systems. Processes are assigned a weight which determines a share (percentage) of the resource they are to receive. The resource is then allocated in discrete-sized time quanta in such a manner that each process makes progress at a precise, uniform rate. Proportional share allocation algorithms are of interest because: they provide a natural means of seamlessly integrating real and non-real-time processing; they are easy to implement; they provide a simple and effective means of precisely controlling the real-time performance of a process; and they provide a natural means of policing so that processes that use more of a resource than they request have no ill-effect on well-behaved processes. We analyze our algorithm in the context of an idealized system in which a resource is assumed to be granted in arbitrarily small intervals of time and show that our algorithm guarantees that the difference between the service time that a process should receive and the service time it actually receives is optimally bounded by the size of a time quantum. In addition, the algorithm provides support for dynamic operations, such as processes joining or leaving the competition, and for both fractional and non-uniform time quanta. As a proof of concept we have implemented a prototype of a CPU scheduler under FreeBSD. The experimental results shows that our implementation performs within the theoretical bounds and hence supports real-time execution in a general purpose operating system.


title: Petri Net Controlled Finite Automata

abstract: We present a generalization of finite automata using Petri nets as control, called Concurrent Finite Automata for short. Several modes of acceptance, defined by final markings of the Petri net, are introduced, and their equivalence is shown. The class of languages obtained by *-free concurrent finite automata contains both the class of regular sets and the class of Petri net languages defined by final marking, and is contained in the class of context-sensitive languages.


Sentic Computing


title: Brain systems for assessing facial attractiveness

abstract: Attractiveness is a facial attribute that shapes human affiliative behaviours. In a previous study we reported a linear response to facial attractiveness in orbitofrontal cortex (OFC), a region involved in reward processing. There are strong theoretical grounds for the hypothesis that coding stimulus reward value also involves the amygdala. The aim of the present investigation is to address whether the amygdala is also sensitive to reward value in faces, indexed as facial attractiveness. We hypothesized that contrary to the linear effects reported previously in OFC, the amygdala would show a non-linear effect of attractiveness by responding to both high and low attractive faces relative to middle attractive faces. Such a non-linear response would explain previous failures to report an amygdala response to attractiveness. Human subjects underwent fMRI while they were presented with faces that varied in facial attractiveness where the task was either to rate faces for facial attractiveness or for age. Consistent with our hypothesis, right amygdala showed a predicted non-linear response profile with greater responses to highly attractive and unattractive faces compared to middle-ranked faces, independent of task. Distinct patterns of activity were seen across different regions of OFC, with some sectors showing linear effects of attractiveness, others exhibiting a non-linear response profile and still others demonstrating activation only during age judgments. Significant effects were also seen in medial prefrontal and paracingulate cortices, posterior OFC, insula, and superior temporal sulcus during explicit attractiveness judgments. The non-linear response profile of the amygdala is consistent with a role in sensing the value of social stimuli, a function that may also involve specific sectors of the OFC.


title: Weisfeiler-Lehman Neural Machine for Link Prediction

abstract: In this paper, we propose a next-generation link prediction method, Weisfeiler-Lehman Neural Machine (WLNM), which learns topological features in the form of graph patterns that promote the formation of links. WLNM has unmatched advantages including higher performance than state-of-the-art methods and universal applicability over various kinds of networks. WLNM extracts an enclosing subgraph of each target link and encodes the subgraph as an adjacency matrix. The key novelty of the encoding comes from a fast hashing-based Weisfeiler-Lehman (WL) algorithm that labels the vertices according to their structural roles in the subgraph while preserving the subgraph's intrinsic directionality. After that, a neural network is trained on these adjacency matrices to learn a predictive model. Compared with traditional link prediction methods, WLNM does not assume a particular link formation mechanism (such as common neighbors), but learns this mechanism from the graph itself. We conduct comprehensive experiments to show that WLNM not only outperforms a great number of state-of-the-art link prediction methods, but also consistently performs well across networks with different characteristics.


title: Optimization and Simulation of Resource Constrained Scheduling Problem Using Genetic Algorithm

abstract: Due to the development of management idea and the scarcity of some resources, the lean management has become the necessary request to implement effective control of resource constrained project. Resource constrained project scheduling is the significant guarantee to attain the lean management. The resource constrained project scheduling problem (RCPSP), with the objective of minimizing project duration and with the precedence relations described by an activity-on-arrow (AOA) network, is formulated as a combination optimization problem and solved using the priority-based genetic algorithm (GA). The activity priorities are represented by chromosome and serial scheduling scheme (SSS) and parallel scheduling scheme (PSS) are developed and utilized to transform chromosome-represented priorities to an active schedule subject to the logic and resource constraints so that project duration corresponding to each chromosome can be evaluated. The overall framework of the GA for the RCPSP is developed and the basic components of the algorithm are designed. Simulation is provided so as to investigate the performance of the priority-based GA with SSS and PSS as decoding method, respectively. The optimal solution to a small-sized resource constrained benchmark instance is scheduled to find the shortest project duration. Comparative simulation results demonstrate not only the effectiveness and efficiency of GA with SSS or PSS as decoding methods in solution to RCPSP with precedence relation of activities diagramed as an AOA network but also the effect of different evolution parameter settings on solution quality of the problem.


title: Underactuated five-finger prosthetic hand inspired by grasping force distribution of humans

abstract: Simple grippers with one or two degrees of freedom are commercially available prosthetic hands; these pinch type devices cannot grasp small cylinders and spheres because of their small degree of freedom. This paper presents the design and prototyping of underactuated five-finger prosthetic hand for grasping various objects in daily life. Underactuated mechanism enables the prosthetic hand to move fifteen compliant joints only by one ultrasonic motor. The innovative design of this prosthetic hand is the underactuated mechanism optimized to distribute grasping force like those of humans who can grasp various objects robustly. Thanks to human like force distribution, the prototype of prosthetic hand could grasp various objects in daily life and heavy objects with the maximum ejection force of 50 N that is greater than other underactuated prosthetic hands.


title: Effect of television advertisements for foods on food consumption in children

abstract: The impact of television (TV) advertisements (commercials) on children's eating behaviour and health is of critical interest. In a preliminary study we examined lean, over weight and obese children's ability to recognise eight food and eight non-food related adverts in a repeated measures design. Their consumption of sweet and savoury, high and low fat snack foods were measured after both sessions. Whilst there was no significant difference in the number of non-food adverts recognised between the lean and obese children, the obese children did recognise significantly more of the food adverts. The ability to recognise the food adverts significantly correlated with the amount of food eaten after exposure to them. The overall snack food intake of the obese and overweight children was significantly higher than the lean children in the control (non-food advert) condition. The consumption of all the food offered increased post food advert with the exception of the low-fat savoury snack. These data demonstrate obese children's heightened alertness to food related cues. Moreover, exposure to such cues induce increased food intake in all children. As suggested the relationship between TV viewing and childhood obesity appears not merely a matter of excessive sedentary activity. Exposure to food adverts promotes consumption.


title: Reputation systems: A survey and taxonomy

abstract: In our increasingly interconnected world, the need for reputation is becoming more important as larger numbers of people and services interact online. Reputation is a tool to facilitate trust between entities, as it increases the efficiency and effectiveness of online services and communities. As most entities will not have any direct experience of other entities, they must increasingly come to rely on reputation systems. Such systems allow the prediction who is likely to be trustworthy based on feedback from past transactions. In this paper we introduce a new taxonomy for reputation systems, along with: a reference model for reputation context, a model of reputation systems, a substantial survey, and a comparison of existing reputation research and deployed reputation systems. The concepts for reputation systems are discussed.A survey of existing reputation systems is presented.We construct a new taxonomy for reputation systems.We identify under-represented areas for research.


title: Production of rare sugars from common sugars in subcritical aqueous ethanol

abstract: Abstract A new isomerization reaction was developed to synthesize rare ketoses. d -Tagatose, d -xylulose, and d -ribulose were obtained in the maximum yields of 24%, 38%, and 40%, respectively, from the corresponding aldoses, d -galactose, d -xylose, and d -ribose, by treating the aldoses with 80% (v/v) subcritical aqueous ethanol at 180 °C. The maximum productivity of d -tagatose was ca . 80 g/(L h). Increasing the concentration of ethanol significantly increased the isomerization of d -galactose. Variation in the reaction temperature did not significantly affect the production of d -tagatose from d -galactose. Subcritical aqueous ethanol converted both 2,3-threo and 2,3-erythro aldoses to the corresponding C-2 ketoses in high yields. Thus, the treatment of common aldoses in subcritical aqueous ethanol can be regarded as a new method to synthesize the corresponding rare sugars.


title: A high performance parallel algorithm for 1-D FFT

abstract: Proposes a parallel high-performance fast Fourier transform (FFT) algorithm based on a multi-dimensional formulation. We use this to solve a commonly encountered FFT based kernel on a distributed memory parallel machine, the IBM scalable parallel system, SP1. The kernel requires a forward FFT computation of an input sequence, multiplication of the transformed data by a coefficient array, and finally an inverse FFT computation of the resultant data. We show that the multi-dimensional formulation helps in reducing the communication costs and also improves the single node performance by effectively utilizing the memory system of the node. We implemented this kernel on the IBM SP1 and observed a performance of 1.25 GFLOPS on a 64-node machine. >


AppDialogue: Multi-App Dialogues for Intelligent Assistants.


An Evaluation of Parser Robustness for Ungrammatical Sentences


title: InaNLP: Indonesia natural language processing toolkit, case study: Complaint tweet classification

abstract: This research discusses how natural language processing (NLP) toolkit for Indonesia formal text and social media text, named as InaNLP, has been developed. Several NLP modules were integrated into InaNLP to make people easier in building an NLP system for Indonesia language. The toolkit contains several NLP modules such as sentence splitter, tokenization, Part of Speech (POS) tagger, phrase chunker, named entity (NE) tagger, syntactic parser, semantic analyzer, and word normalization. Several NLP modules were built using rule based approach, whereas several others implemented statistical based approach. Here, the accuracy of several modules such as the POS tagger, NE tagger, syntactic parser and semantic analyzer are shown. In the NE tagger, five (5) word windows with features of POS, orthography, and word list are used. In the NE tagger experiment for evaluating the features, using SMO algorithm and 1500 sentences, for 15 NE classes, token classification accuracy of 93.419%, which outperform related work, could be achieved. For the POS tagger, using 12,000 token as the training data and 3,000 token as the testing data, the accuracy of 96.50% was achieved. For the syntactic parser, using CYK algorithm and 100 sentences as the training data and 36 sentences as the testing data, the experiment achieved the accuracy of 47.22%. For the semantic analyzer, using 200 sentences as the training data, the experiment achieved the accuracy of 62.50%. This research also shows an example in building an Indonesia NLP system using InaNLP for complaint tweet classification. In the experiment for the complaint classification, using 7440 data, the experiment achieved 0.892 of average F-measure score.


title: Strategies and algorithms for clustering large datasets: a review

abstract: The exploratory nature of data analysis and data mining makes clustering one of the most usual tasks in these kind of projects. More frequently these projects come from many different application areas like biology, text analysis, signal analysis, etc that involve larger and larger datasets in the number of examples and the number of attributes. Classical methods for clustering data like K-means or hierarchical clustering are beginning to reach its maximum capability to cope with this increase of dataset size. The limitation for these algorithms come either from the need of storing all the data in memory or because of their computational time complexity. These problems have opened an area for the search of algorithms able to reduce this data overload. Some solutions come from the side of data preprocessing by transforming the data to a lower dimensionality manifold that represents the structure of the data or by summarizing the dataset by obtaining a smaller subset of examples that represent an equivalent information. A different perspective is to modify the classical clustering algorithms or to derive other ones able to cluster larger datasets. This perspective relies on many different strategies. Techniques such as sampling, on-line processing, summarization, data distribution and efficient datastructures have being applied to the problem of scaling clustering algorithms. This paper presents a review of different strategies and clustering algorithms that apply these techniques. The aim is to cover the different range of methodologies applied for clustering data and how they can be scaled.


title: Relaxation response-based yoga improves functioning in young children with autism: a pilot study.

abstract: OBJECTIVES ::: The study objectives were to develop and objectively assess the therapeutic effect of a novel movement-based complementary and alternative medicine approach for children with an autism-spectrum disorder (ASD). ::: ::: ::: DESIGN ::: A within-subject analysis comparing pre- to post-treatment scores on two standard measures of childhood behavioral problems was used. SETTINGS AND LOCATION: The intervention and data analysis occurred at a tertiary care, medical school teaching hospital. ::: ::: ::: SUBJECTS ::: Twenty-four (24) children aged 3-16 years with a diagnosis of an ASD comprised the study group. ::: ::: ::: INTERVENTION ::: The efficacy of an 8-week multimodal yoga, dance, and music therapy program based on the relaxation response (RR) was developed and examined. ::: ::: ::: OUTCOME MEASURES ::: The study outcome was measured using The Behavioral Assessment System for Children, Second Edition (BASC-2) and the Aberrant Behavioral Checklist (ABC). ::: ::: ::: RESULTS ::: Robust changes were found on the BASC-2, primarily for 5-12-year-old children. Unexpectedly, the post-treatment scores on the Atypicality scale of the BASC-2, which measures some of the core features of autism, changed significantly (p=0.003). ::: ::: ::: CONCLUSIONS ::: A movement-based, modified RR program, involving yoga and dance, showed efficacy in treating behavioral and some core features of autism, particularly for latency-age children.


title: SPAD Smart Pixel for Time-of-Flight and Time-Correlated Single-Photon Counting Measurements

abstract: We present a smart pixel based on a single-photon avalanche diode (SPAD) for advanced time-of-flight (TOF) and time-correlated single photon counting (TCSPC) applications, fabricated in a cost-effective 0.35- m CMOS technology. The large CMOS detector (30- m active area diameter) shows very low noise (12 counts per second at room temperature at 5-V excess bias) and high efficiency in a wide wavelength range (about 50% at 410 nm and still 5% at 800 nm). The analog front-end electronics promptly senses and quenches the avalanche, thus leading to an almost negligible afterpulsing effect. The in-pixel 10-bit time-to-digital converter (TDC) provides 312-ps resolution and 320-ns full-scale range (FSR), i.e., 10-cm single-shot spatial resolution within 50-m depth range in a TOF system. The in-pixel 10-bit memory and output buffers make this smart pixel the viable building block for advanced single-photon imager arrays for 3-D depth ranging in safety and security applications and for 2-D fluorescence lifetime decays in biomedical imaging.


A primer on critical success factors


An Efficient Delivery Middleware - (DMIFWare) For MPEG-4 Applications: Design, Implementation and Experience


title: Mobile phishing attack for Android platform

abstract: In this paper, we address the problem of mobile phishing via the implementation of a Trojan that commits phishing through the mobile's pre-installed applications, which are naturally trusted. It utilizes task interception along with lack of identity indicators, and it overrides the default behavior of some functions to succeed with the attack. We also study the impact of this Trojan on the device's performance. Finally, we propose some security enhancements that do not rely on the human factor, which are categorized as Operating System (OS) Level solutions, and Secure Socket Layer (SSL) solutions.


title: On the Impact of Micro-Packages: An Empirical Study of the npm JavaScript Ecosystem

abstract: The rise of user-contributed Open Source Software (OSS) ecosystems demonstrate their prevalence in the software engineering discipline. Libraries work together by depending on each other across the ecosystem. From these ecosystems emerges a minimized library called a micro-package. Micro- packages become problematic when breaks in a critical ecosystem dependency ripples its effects to unsuspecting users. In this paper, we investigate the impact of micro-packages in the npm JavaScript ecosystem. Specifically, we conducted an empirical in- vestigation with 169,964 JavaScript npm packages to understand (i) the widespread phenomena of micro-packages, (ii) the size dependencies inherited by a micro-package and (iii) the developer usage cost (ie., fetch, install, load times) of using a micro-package. Results of the study find that micro-packages form a significant portion of the npm ecosystem. Apart from the ease of readability and comprehension, we show that some micro-packages have long dependency chains and incur just as much usage costs as other npm packages. We envision that this work motivates the need for developers to be aware of how sensitive their third-party dependencies are to critical changes in the software ecosystem.


title: MobiLearn go: mobile microlearning as an active, location-aware game

abstract: Mobile technologies hold great potential to make studying both more effective and more enjoyable. In this work we present a mobile, microlearning application. Our system is designed with two goals: be flexible enough to support learning in any subject and encourage frequent short study sessions in a variety of contexts. We discuss the use of our application to assess the feasibility of microlearning for non-language learning and the relationship between the physical location of study sessions and information retention.


title: Multiple-wavelength integrated photonic networks based on microring resonator devices

abstract: Feature Issue on Nanoscale Integrated Photonics for Optical Networks Microring resonator devices implemented on silicon and silicon-on-insulator substrates have a unique potential to be used in high-bandwidth multiple-wavelength integrated photonic networks. A scheme for the wavelength allocation is proposed, and its feasibility is verified experimentally. The important system-level trade-offs that result from the proposed scheme, including those among bandwidth, device footprint, and electrical power consumption, are discussed as well.


title: HAIR: HAptic feedback with a mobile AIR jet

abstract: Haptic devices are dedicated to render virtual tactile stimulation. A limitation of these devices is the intrusiveness of their mechanical structures, i.e. the user need to hold or wear the device to interact with the environment. Here, we propose a concept of new tactile device named HAIR. The device is composed of a computer vision system, a mechatronic device and air jets that stimulate the skin. We designed a first prototype and conducted a preliminary experiment to validate our concept. The interface enables a tactile interaction without using physical contact with material devices, providing better freedom of movement and enhancing the interaction transparency.


title: Fuzzy Darwinian Detection of Credit Card Fraud

abstract: Credit evaluation is one of the most important and difficult tasks for credit card companies, mortgage companies,banks and other financial institutes. Incorrect credit judgement causes huge financial losses. This work describes theuse of an evolutionary-fuzzy system capable of classifying suspicious and non-suspicious credit card transactions.The paper starts with the details of the system used in this work. A series of experiments are described, showing thatthe complete system is capable of attaining good accuracy and intelligibility levels for real data.


title: Online flow experiences, problematic Internet use and Internet procrastination

abstract: This study explores the theoretical and practical overlap between online procrastination, problematic Internet use, and flow on the Internet. At the theoretical level there is a great deal of interrelatedness between these three concepts (for example, all three concepts deal with the issue of a lack of control over time spent online and acknowledge the distracting and entertaining properties of the Internet); yet, one can also argue that the concepts are theoretically distinct (for example, flow is a total absorption in the work at hand, whereas procrastination is the avoidance of the work at hand). All three concepts have been used to describe either desirable (flow) or undesirable (procrastination and problematic Internet use) states when online. In this study a sample of 1399 Internet users was obtained from a survey placed on a South African online information technology magazine. Using the problematic Internet use questionnaire (PIUQ), the distraction subscale of the online cognition scale (OCS), and a modified version of the Flow scale it was found that there were strong positive relationships between all three variables (the strongest relationship being between problematic Internet use and online procrastination). The results also suggested that procrastination may be a connector between PIU and flow; also that PIU is a connector between procrastination and flow, but that flow is independent of the relationship between PIU and procrastination. These results are discussed in relation to previous studies on problematic Internet use and in particular, whether these relationships are unique to respondents involved in the information technology sector.


title: The four blastomeres of a 4-cell stage human embryo are able to develop individually into blastocysts with inner cell mass and trophectoderm

abstract: BACKGROUND: Early mammalian blastomeres are thought to be flexible and totipotent allowing the embryo to overcome perturbations in its organization during preimplantation development. In the past, experiments using single blastomeres from 2-, 4- and 8-cell stage mammalian embryos have provided evidence that at least some of the isolated cells can develop into healthy fertile animals and therefore are totipotent. We investigated whether isolated blastomeres of human 4-cell stage embryos could develop in vitro into blastocysts with trophectoderm (TE) and inner cell mass (ICM). METHODS: Six 4-cell stage human embryos were split and the four blastomeres were cultured individually. The expression of NANOG, a marker for ICM cells, was analysed by immunocytochemistry. RESULTS: The majority of the blastomere-derived embryos followed the normal pattern of development with compaction on Day 4 and cavitation on Day 5 and developed into small blastocysts with TE and ICM on Day 6 (n = 12). The four cells of one embryo were individually capable of developing into blastocysts with TE and ICM, and NANOG was expressed in the ICM. CONCLUSIONS: Although based on a small number of embryos, we conclude that the blastomeres of a 4-cell stage human embryo are flexible and able to develop into blastocysts with ICM and TE.


title: Methodological Review: Coreference resolution: A review of general methodologies and applications in the clinical domain

abstract: Coreference resolution is the task of determining linguistic expressions that refer to the same real-world entity in natural language. Research on coreference resolution in the general English domain dates back to 1960s and 1970s. However, research on coreference resolution in the clinical free text has not seen major development. The recent US government initiatives that promote the use of electronic health records (EHRs) provide opportunities to mine patient notes as more and more health care institutions adopt EHR. Our goal was to review recent advances in general purpose coreference resolution to lay the foundation for methodologies in the clinical domain, facilitated by the availability of a shared lexical resource of gold standard coreference annotations, the Ontology Development and Information Extraction (ODIE) corpus.


title: An evaluation of computational intelligence in credit card fraud detection

abstract: Over the past few years, credit card transactions have been experiencing significantly fast development with the growth of e-commerce and shows tremendous promise of advancement in the future. Hence, due to explosion of credit card transaction, it is inevitable to secure transactions. To detect credit card fraud, computational intelligence has been widely used and plays a vital role. The selection of the preeminent classifier algorithm for a fraud detection system is a very extensive problem. This paper analyzes and compares various popular classifier algorithms that have been most commonly using in detecting credit card fraud. Moreover, it focuses on the measure used to assess the classification performance and rank those algorithms. Performance evaluation experiments were carried out on UCSD-FICO Data Mining Contest 2009 dataset. The experimental results suggest that (1) though achieved classification accuracy rate is 98.25% but fraud detection success rate is below 50%, (2) meta and tree classifiers perform better than other group of classifiers. Eventually, overall success rate (fraud detection rate) should be taken into consideration to evaluate the performance of the classifiers in a fraud detection system.


title: NLAST: A natural language assistant for students

abstract: This paper presents a system that works as an assistant for students in their learning process. The assistant system has two main parts: an Android application and a server platform. The Android application is a chatterbot (i.e., an agent intended to conduct a conversation in natural language with a human being) based on AIML, one of the more successful languages for developing conversational agents. The chatterbot acts as an intermediation agent between a student and the server platform. The server platform contains four repositories and a recommender (which are part of a bigger professor assessment system). The final objective of the assistant system is to make a student able to carry out several actions related to his/her learning and assessment processes, such as: to consult exam questions from a repository, to receive recommendations about learning material, to ask questions about a course, and to check his/her own assessed exams. These actions are carried out through and Android application using a natural language interface (by voice or typing). The purpose of this development is to facilitate the access to this information through a friendly interface.


title: Mobile robotics in arable lands: Current state and future trends

abstract: This paper presents a summary of the current state of mobile robotics oriented to perform precision agricultural tasks on arable lands. Two approaches of robot configurations are identified and some relevant examples are mentioned in addition to identifying the trend of robotics in agriculture, the current limitations, and the following steps as understood by the authors for reducing the gap for increased inclusion of robotics in everyday agricultural tasks.


title: QuickCheck: a lightweight tool for random testing of Haskell programs

abstract: Quick Check is a tool which aids the Haskell programmer in formulating and testing properties of programs. Properties are described as Haskell functions, and can be automatically tested on random input, but it is also possible to define custom test data generators. We present a number of case studies, in which the tool was successfully used, and also point out some pitfalls to avoid. Random testing is especially suitable for functional programs because properties can be stated at a fine grain. When a function is built from separately tested components, then random testing suffices to obtain good coverage of the definition under test.


title: Improving the Recruitment Process through Ontology-based Querying

abstract: While using semantic data can enable improved retrieval of suitable jobs or applicants in a recruitment process, cases of inconsistent or overly specific queries which would return no results still have to be dealt with. In this paper the extension of a semantic job portal with a novel query relaxation technique is presented which is able to return results even in such cases. Subsymbolic methods estimate the (quantitative) similarity between job or applicant descriptions. Symbolic approaches allow a more intuitive way to formulate and handle preferences and domain knowledge. But due to their partial preference order they can not rank all results in practice like subsymbolic approaches. In this paper we propose a query relaxation method which combines both methods. This method demonstrates that by having data based on formal ontologies, one can improve the retrieval also in a user-friendly way.


title: Taming Performance Hotspots in Cloud Storage with Dynamic Load Redistribution

abstract: Cloud storage services are associated with high latency variance, and degraded throughput which is problematic when users are fetching and storing content for interactive applications. This can be attributed to performance hotspots created by slow nodes in a storage cluster, and performance interference caused by multi-tenancy, and background tasks such as data scrubbing, backfilling, recovery, etc. In this paper, we present DLR, a system that improves the performance of cloud storage services in the presence of hardware heterogeneity, and performance interference through a dynamic load redistribution technique. We designed DLR to dynamically adjust the load serving ratio of storage servers based on the system-level performance measurements from the storage cluster. We implemented DLR using Ceph, a popular distributed object storage system, and evaluated its performance on NSFCloud's Chameleon testbed using Ceph's Rados benchmark. Experimental results show that DLR improves the average throughput and latency of Ceph storage by up to 65%, and 41% respectively compared to the default case. Compared to Ceph's in-built load balancing technique, DLR improves the throughput by up to 98%, and latency by 96%.


title: Toward enhanced P300 speller performance

abstract: This study examines the effects of expanding the classical P300 feature space on the classification performance of data collected from a P300 speller paradigm [Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroenceph Clin Neurophysiol 1988;70:510-23]. Using stepwise linear discriminant analysis (SWLDA) to construct a classifier, the effects of spatial channel selection, channel referencing, data decimation, and maximum number of model features are compared with the intent of establishing a baseline not only for the SWLDA classifier, but for related P300 speller classification methods in general. By supplementing the classical P300 recording locations with posterior locations, online classification performance of P300 speller responses can be significantly improved using SWLDA and the favorable parameters derived from the offline comparative analysis.


title: Changes in resident attitudes towards tourism development and conservation in the Okavango Delta, Botswana.

abstract: Negative attitudes of resident communities towards conservation are associated with resource decline in developing countries. In Botswana, Community-Based Natural Resource Management (CBNRM) was adopted to address this challenge. CBNRM links rural development and conservation. However, the impact of CBNRM on changes of resident attitudes towards conservation and tourism is not adequately researched. This paper, therefore, assesses the impacts of CBNRM on resident attitudes towards tourism development and conservation in the Okavango Delta, Botswana. The study purposively sampled villages of Khwai, Mababe and Sankoyo. Household data using variables like: economic benefits from CBNRM; level of satisfaction with CBNRM; co-management of natural resources between resident communities and government agencies; and collective action was collected. This data was supplemented by secondary and ethnographic data. Using qualitative and quantitative analysis, results indicate changes in resident attitudes from being negative to positive towards tourism and conservation. These changes are triggered by economic benefits residents derived from CBNRM, co-management in resource management; and, collective action of communities in CBNRM development. Positive attitudes towards conservation and tourism are the first building blocks towards achieving conservation in nature-based tourism destinations. As a result, decision-makers should give priority to CBNRM and use it as a tool to achieve conservation and improved livelihoods in nature-based tourism destinations of developing countries.


title: Tri-Modal Half-Bridge Converter Topology for Three-Port Interface

abstract: This letter proposes a novel converter topology that interfaces three power ports: a source, a bidirectional storage port, and an isolated load port. The proposed converter is based on a modified version of the isolated half-bridge converter topology that utilizes three basic modes of operation within a constant-frequency switching cycle to provide two independent control variables. This allows tight control over two of the converter ports, while the third port provides the power balance in the system. The switching sequence ensures a clamping path for the energy of the leakage inductance of the transformer at all times. This energy is further utilized to achieve zero-voltage switching for all primary switches for a wide range of source and load conditions. Basic steady-state analysis of the proposed converter is included, together with a suggested structure for feedback control. Key experimental results are presented that validate the converter operation and confirm its ability to achieve tight independent control over two power processing paths. This topology promises significant savings in component count and losses for power-harvesting systems. The proposed topology and control is particularly relevant to battery-backed power systems sourced by solar or fuel cells


title: Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded

abstract: Many vision and language models suffer from poor visual grounding -- often falling back on easy-to-learn language priors rather than basing their decisions on visual concepts in the image. In this work, we propose a generic approach called Human Importance-aware Network Tuning (HINT) that effectively leverages human demonstrations to improve visual grounding. HINT encourages deep networks to be sensitive to the same input regions as humans. Our approach optimizes the alignment between human attention maps and gradient-based network importances -- ensuring that models learn not just to look at but rather rely on visual concepts that humans found relevant for a task when making predictions. We apply HINT to Visual Question Answering and Image Captioning tasks, outperforming top approaches on splits that penalize over-reliance on language priors (VQA-CP and robust captioning) using human attention demonstrations for just 6% of the training data.


title: X-RHex: A Highly Mobile Hexapedal Robot for Sensorimotor Tasks

abstract: We report on the design and development of X-RHex, a hexapedal robot with a single actuator per leg, intended for real-world mobile applications. X-RHex is an updated version of the RHex platform, designed to offer substantial improvements in power, run-time, payload size, durability, and terrain negotiation, with a smaller physical volume and a comparable footprint and weight. Furthermore, X-RHex is designed to be easier to build and maintain by using a variety of commercial off-the-shelf (COTS) components for a majority of its internals. This document describes the X-RHex architecture and design, with a particular focus on the new ability of this robot to carry modular payloads as a laboratory on legs. X-RHex supports a variety of sensor suites on a small, mobile robotic platform intended for broad, general use in research, defense, and search and rescue applications. Comparisons with previous RHex platforms are presented throughout, with preliminary tests indicating that the locomotive capabilities of X-RHex can meet or exceed the previous platforms. With the additional payload capabilities of X-RHex, we claim it to be the first robot of its size to carry a fully programmable GPU for fast, parallel sensor processing.


title: chrF: character n-gram F-score for automatic MT evaluation

abstract: We propose the use of character n-gram F-score for automatic evaluation of machine translation output. Character ngrams have already been used as a part of more complex metrics, but their individual potential has not been investigated yet. We report system-level correlations with human rankings for 6-gram F1-score (CHRF) on the WMT12, WMT13 and WMT14 data as well as segment-level correlation for 6gram F1 (CHRF) and F3-scores (CHRF3) on WMT14 data for all available target languages. The results are very promising, especially for the CHRF3 score – for translation from English, this variant showed the highest segment-level correlations outperforming even the best metrics on the WMT14 shared evaluation task.


title: Multi-Mode Inference Engine for Convolutional Neural Networks

abstract: During the past few years, interest in convolutional neural networks (CNNs) has risen constantly, thanks to their excellent performance on a wide range of recognition and classification tasks. However, they suffer from the high level of complexity imposed by the high-dimensional convolutions in convolutional layers. Within scenarios with limited hardware resources and tight power and latency constraints, the high computational complexity of CNNs makes them difficult to be exploited. Hardware solutions have striven to reduce the power consumption using low-power techniques, and to limit the processing time by increasing the number of processing elements (PEs). While most of ASIC designs claim a peak performance of a few hundred giga operations per seconds, their average performance is substantially lower when applied to state-of-the-art CNNs such as AlexNet, VGGNet and ResNet, leading to low resource utilization. Their performance efficiency is limited to less than 55% on average, which leads to unnecessarily high processing latency and silicon area. In this paper, we propose a dataflow which enables to perform both the fully-connected and convolutional computations for any filter/layer size using the same PEs. We then introduce a multi-mode inference engine (MMIE) based on the proposed dataflow. Finally, we show that the proposed MMIE achieves a performance efficiency of more than 84% when performing the computations of the three renown CNNs (i.e., AlexNet, VGGNet and ResNet), outperforming the best architecture in the state-of-the-art in terms of energy consumption, processing latency and silicon area.


Automatic Feature Extraction and Indexing for Content-Based Visual Query


title: Short text understanding through lexical-semantic analysis

abstract: Understanding short texts is crucial to many applications, but challenges abound. First, short texts do not always observe the syntax of a written language. As a result, traditional natural language processing methods cannot be easily applied. Second, short texts usually do not contain sufficient statistical signals to support many state-of-the-art approaches for text processing such as topic modeling. Third, short texts are usually more ambiguous. We argue that knowledge is needed in order to better understand short texts. In this work, we use lexical-semantic knowledge provided by a well-known semantic network for short text understanding. Our knowledge-intensive approach disrupts traditional methods for tasks such as text segmentation, part-of-speech tagging, and concept labeling, in the sense that we focus on semantics in all these tasks. We conduct a comprehensive performance evaluation on real-life data. The results show that knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are effective in harvesting semantics of short texts.


title: A receptor heteromer mediates the male perception of female attractants in plants

abstract: Sexual reproduction requires recognition between the male and female gametes. In flowering plants, the immobile sperms are delivered to the ovule-enclosed female gametophyte by guided pollen tube growth. Although the female gametophyte-secreted peptides have been identified to be the chemotactic attractant to the pollen tube, the male receptor(s) is still unknown. Here we identify a cell-surface receptor heteromer, MDIS1-MIK, on the pollen tube that perceives female attractant LURE1 in Arabidopsis thaliana. MDIS1, MIK1 and MIK2 are plasma-membrane-localized receptor-like kinases with extracellular leucine-rich repeats and an intracellular kinase domain. LURE1 specifically binds the extracellular domains of MDIS1, MIK1 and MIK2, whereas mdis1 and mik1 mik2 mutant pollen tubes respond less sensitively to LURE1. Furthermore, LURE1 triggers dimerization of the receptors and activates the kinase activity of MIK1. Importantly, transformation of AtMDIS1 to the sister species Capsella rubella can partially break down the reproductive isolation barrier. Our findings reveal a new mechanism of the male perception of the female attracting signals.


title: Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection

abstract: Fully convolutional deep neural networks have been asserted to be fast and precise frameworks with great potential in image segmentation. One of the major challenges in training such networks raises when data is unbalanced, which is common in many medical imaging applications such as lesion segmentation where lesion class voxels are often much lower in numbers than non-lesion voxels. A trained network with unbalanced data may make predictions with high precision and low recall, being severely biased towards the non-lesion class which is particularly undesired in most medical applications where FNs are more important than FPs. Various methods have been proposed to address this problem, more recently similarity loss functions and focal loss. In this work we trained fully convolutional deep neural networks using an asymmetric similarity loss function to mitigate the issue of data imbalance and achieve much better tradeoff between precision and recall. To this end, we developed a 3D FC-DenseNet with large overlapping image patches as input and an asymmetric similarity loss layer based on Tversky index (using Fbeta scores). We used large overlapping image patches as inputs for intrinsic and extrinsic data augmentation, a patch selection algorithm, and a patch prediction fusion strategy using B-spline weighted soft voting to account for the uncertainty of prediction in patch borders. We applied this method to MS lesion segmentation based on two different datasets of MSSEG and ISBI longitudinal MS lesion segmentation challenge, where we achieved top performance in both challenges. Our network trained with focal loss ranked first according to the ISBI challenge overall score and resulted in the lowest reported lesion false positive rate among all submitted methods. Our network trained with the asymmetric similarity loss led to the lowest surface distance and the best lesion true positive rate.


title: Enhanced gamma (30–150Hz) frequency in the human medial temporal lobe

abstract: We performed fast Fourier transformation power spectral analysis of the electrocorticogram in human medial temporal lobe during wakeful rest in six epileptic subjects. Compared with the electrocorticogram wave in the basal temporal lobe, which showed monotonic decline of spectral power across the frequency axis, the electrocorticogram wave in the parahippocampal gyrus was enhanced (or did not decline) in the gamma frequency range (30-150 Hz) in all subjects. Although it has been suggested that electrical oscillations of the hippocampus have functional roles in higher brain functions, namely learning and memory, the knowledge of hippocampal oscillations is largely limited to animal studies. The present results demonstrate that fast frequency oscillation is also present in the human medial temporal lobe, which has been reported in animal hippocampi. They also demonstrate the importance of recording very fast field potentials in human electrocorticograms. This fast oscillation is likely to play important functional roles related to learning and memory, possibly to induce long-term potentiation in the human medial temporal lobe.


title: Child Vulnerability and Well-being among Farmer Families

abstract: This research aimed to identify vulnerability and well-being of children in a family of farmers, identify the typology of vulnerability and well-being of children in the family of farmers, and analyze the relationship between family characteristics, characteristics of children with the vulnerability and well-being of children. The population in this research is family farmers who have children of 4 and 5th grade who reside in the village Sindangjaya, District Cipanas, Cianjur with a sample of 35 children. Sampling was conducted using probability sampling method with random sampling techniques. The results showed that the internal susceptibility to family farmers is low, relatively low external vulnerability, and low well-being. Typology vulnerability of children and the well-being of children in a family of farmers included in Type 1 and Type 4. There was a significant negative correlation between family size and order of the child with the child's welfare, but there was no significant relationship between the characteristics of families and children with children's vulnerability.


title: My phone is my keypad: privacy-enhanced PIN-entry on public terminals

abstract: More and more services are available on public terminals. Due to their public location and permanent availability, they can easily fall victim to manipulation. These manipulations mostly aim at stealing the customers' authentication information (e.g. bank card PIN) to gain access to the victims' possessions. By relocating the input from the terminal to the users' mobile device, the system presented in this paper makes the authentication process resistant against such manipulations. In principle, this relocation makes PIN entry more complex, with a tendency to worse usability. In this paper, we present the concept as well as an evaluation that has been conducted to study the trade off between usability and security. The results show that users apparently are willing to accept a certain increase of interaction time in exchange for improved security.


title: IMPACTS DUE TO INTERNET ADDICTION AMONG MALAYSIAN UNIVERSITY STUDENTS

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


title: KESeDa: Knowledge Extraction from Heterogeneous Semi-Structured Data Sources

abstract: A large part of the free knowledge existing on the Web is available as heterogeneous, semi-structured data, which is only weakly interlinked and in general does not include any semantic classification. Due to the enormous amount of information the necessary preparation of this data for integrating it in the Web of Data requires automated processes. The extraction of knowledge from structured as well as unstructured data has already been the topic of research. But especially for the semi-structured data format JSON, which is widely used as a data exchange format e.g., in social networks, extraction solutions are missing. Based on the findings we made by analyzing existing extraction methods, we present our KESeDa approach for extracting knowledge from heterogeneous, semi-structured data sources. We show how knowledge can be extracted by describing different analysis and processing steps. With the resulting semantically enriched data the potential of Linked Data can be utilized.


title: A Circularly-Polarized Isoflux Antenna Based on Anisotropic Metasurface

abstract: Theory, design, realization and measurements of an X-band isoflux circularly polarized antenna for LEO satellite platforms are presented. The antenna is based on a metasurface composed by a dense texture of sub-wavelength metal patches on a grounded dielectric slab, excited by a surface wave generated by a coplanar feeder. The antenna is extremely flat (1.57 mm) and light (less than 1 Kg) and represents a competitive solution for space-to-ground data link applications.


title: A Gentle Introduction to Epistemic Planning: The DEL Approach

abstract: Epistemic planning can be used for decision making in multi-agent situations with distributed knowledge and capabilities. Dynamic Epistemic Logic (DEL) has been shown to provide a very natural and expressive framework for epistemic planning. In this paper, we aim to give an accessible introduction to DEL-based epistemic planning. The paper starts with the most classical framework for planning, STRIPS, and then moves towards epistemic planning in a number of smaller steps, where each step is motivated by the need to be able to model more complex planning scenarios.


title: An integrator circuit using differential difference current conveyor transconductance amplifier

abstract: An integrator circuit is presented in this paper that has used Differntial Difference Current Conveyor Transconductance Amplifier (DDCCTA). It has one DDCCTA and one passive component. It has been realized only with first order low pass response. The operation of the circuit has been observed and enforced at a supply voltage of ± 1.8V (bias current 50pA) using cadence and the model parameters of gpdk 180nm CMOS technology. The worthy of the proposed circuit has been test checked using DDCCTA and further tested for its efficiency on a laboratory breadboard. In this commercially available AD844AN and LM13600 ICs are used. Further, the circuit presented in this paper is impermeable to noise, possessing low voltage and insensitive to temperature.


title: Interactive Near-Field Illumination for Photorealistic Augmented Reality with Varying Materials on Mobile Devices

abstract: At present, photorealistic augmentation is not yet possible since the computational power of mobile devices is insufficient. Even streaming solutions from stationary PCs cause a latency that affects user interactions considerably. Therefore, we introduce a differential rendering method that allows for a consistent illumination of the inserted virtual objects on mobile devices, avoiding delays. The computation effort is shared between a stationary PC and the mobile devices to make use of the capacities available on both sides. The method is designed such that only a minimum amount of data has to be transferred asynchronously between the participants. This allows for an interactive illumination of virtual objects with a consistent appearance under both temporally and spatially varying real illumination conditions. To describe the complex near-field illumination in an indoor scenario, HDR video cameras are used to capture the illumination from multiple directions. In this way, sources of illumination can be considered that are not directly visible to the mobile device because of occlusions and the limited field of view. While our method focuses on Lambertian materials, we also provide some initial approaches to approximate non-diffuse virtual objects and thereby allow for a wider field of application at nearly the same cost.


title: Compact and Tunable Slot-Loop Antenna

abstract: A compact and tunable slot-loop antenna is studied in this communication. By periodically loading the slot line with varactor diodes, the slow-wave loop is capable of operating at frequencies from 2.34 GHz to 4 GHz with the measured input reflection coefficient better than -7.5 dB while occupying a small footprint of 0.072 λg × 0.072 λg (8.1 mm × 8.1 mm), where λg is the guided wavelength of a slot line at 2.34 GHz. The slot-loop in the ground plane is excited by a simple microstrip line, which enables the impedance matching from 3.2 GHz to 4 GHz for VSWR ≤ 2. A LC low-pass circuit is configured into the bias network to isolate the RF signal from the DC power path and consistent radiation patterns are experimentally obtained across the entire tunable frequencies of interest. The slot loop combined with the feed line and the bias circuitry is only 40 mm × 38.97 mm. Experimental results validate the feasibility of the presented antenna and agree well with simulation data.


title: Simple, robust, scalable semi-supervised learning via expectation regularization

abstract: Although semi-supervised learning has been an active area of research, its use in deployed applications is still relatively rare because the methods are often difficult to implement, fragile in tuning, or lacking in scalability. This paper presents expectation regularization, a semi-supervised learning method for exponential family parametric models that augments the traditional conditional label-likelihood objective function with an additional term that encourages model predictions on unlabeled data to match certain expectations---such as label priors. The method is extremely easy to implement, scales as well as logistic regression, and can handle non-independent features. We present experiments on five different data sets, showing accuracy improvements over other semi-supervised methods.


title: Forecasting economic time series using targeted predictors

abstract: This paper studies two refinements to the method of factor forecasting. First, we consider the method of quadratic principal components that allows the link function between the predictors and the factors to be non-linear. Second, the factors used in the forecasting equation are estimated in a way to take into account that the goal is to forecast a specific series. This is accomplished by applying the method of principal components to 'targeted predictors' selected using hard and soft thresholding rules. Our three main findings can be summarized as follows. First, we find improvements at all forecast horizons over the current diffusion index forecasts by estimating the factors using fewer but informative predictors. Allowing for non-linearity often leads to additional gains. Second, forecasting the volatile one month ahead inflation warrants a high degree of targeting to screen out the noisy predictors. A handful of variables, notably relating to housing starts and interest rates, are found to have systematic predictive power for inflation at all horizons. Third, the targeted predictors selected by both soft and hard thresholding changes with the forecast horizon and the sample period. Holding the set of predictors fixed as is the current practice of factor forecasting is unnecessarily restrictive.


title: The OntoWordNet Project: Extension and Axiomatization of Conceptual Relations in WordNet

abstract: In this paper we present a progress report of the OntoWordNet project, a research program aimed at achieving a formal specification of WordNet. Within this program, we developed a hybrid bottom-up top-down methodology to automatically extract association relations from WordNet, and to interpret those associations in terms of a set of conceptual relations, formally defined in the DOLCE foundational ontology. Preliminary results provide us with the conviction that a research program aiming to obtain a consistent, modularized, and axiomatized ontology from WordNet can be completed in acceptable time with the support of semi-automatic techniques.


title: Adaptive Influence Maximization in Dynamic Social Networks

abstract: For the purpose of propagating information and ideas through a social network, a seeding strategy aims to find a small set of seed users that are able to maximize the spread of the influence, which is termed influence maximization problem. Despite a large number of works have studied this problem, the existing seeding strategies are limited to the models that cannot fully capture the characteristics of real-world social networks. In fact, due to high-speed data transmission and large population of participants, the diffusion processes in real-world social networks have many aspects of uncertainness. As shown in the experiments, when taking such uncertainness into account, the state-of-the-art seeding strategies are pessimistic as they fail to trace the influence diffusion. In this paper, we study the strategies that select seed users in an adaptive manner. We first formally model the dynamic independent Cascade model and introduce the concept of adaptive seeding strategy. Then, based on the proposed model, we show that a simple greedy adaptive seeding strategy finds an effective solution with a provable performance guarantee. Besides the greedy algorithm, an efficient heuristic algorithm is provided for better scalability. Extensive experiments have been performed on both the real-world networks and synthetic power-law networks. The results herein demonstrate the superiority of the adaptive seeding strategies to other baseline methods.


title: Fast parameterless density-based clustering via random projections

abstract: Clustering offers significant insights in data analysis. Density based algorithms have emerged as flexible and efficient techniques, able to discover high-quality and potentially irregularly shaped- clusters. We present two fast density-based clustering algorithms based on random projections. Both algorithms demonstrate one to two orders of magnitude speedup compared to equivalent state-of-art density based techniques, even for modest-size datasets. We give a comprehensive analysis of both our algorithms and show runtime of O(dNlog2 N), for a d-dimensional dataset. Our first algorithm can be viewed as a fast variant of the OPTICS density-based algorithm, but using a softer definition of density combined with sampling. The second algorithm is parameter-less, and identifies areas separating clusters.


title: The distribution of path loss exponent in 3D indoor environment

abstract: Wireless communications in indoor environment such as wireless sensor network and WiFi is slowing becoming the norm of everyday business, especially in industrial indoor settings. Tracking flying objects in an indoor environment controlled via wireless links require proper understanding of the path loss distribution in a 3D environment. Such a setting is rarely discussed - if any - in the open literature where the path loss exponent is generalized over the whole 3D indoor space. This study, however, attempts to address minute changes in path loss exponent distribution in such environment. An extensive measurement campaign was conducted in our university laboratory mimicking an industrial indoor setting where drones are supposed to operate. Our initial study involved a placement of 2.4 GHz transmitter at different heights with 0.00 m, 0.61 m, 1.36m and 1.88 m while varying the receiver antenna location over a 3D grid of 1 meter resolution in x, y, and z-axes directions. The received signal strength indicator (RSSI) readings were recorded to derive the path loss exponent at every grid point. Interestingly, results indicate that path loss exponent is definitely affecting along the z-axis (vertically) but still can be averaged over x-y- axes (horizontally). Increasing the transmitter or receiver height decreases the path loss exponent averaged over the s-x axes grid lines. Furthermore, due to furniture clutter on the floor and the availability of clear LOS components at higher links, the path loss becomes a function of the height. The results confirm further the furniture clutter and the floor proximity do add up to the path loss exponent due to lack of LOS links while higher links experience smaller path loss exponent. This is quite important as it has never been reported in the open literature before.


title: Direct Torque Control of Brushless DC Motor Drives With Improved Reliability

abstract: Currently, road vehicles are more equipped with permanent magnet synchronous motors. Particularly interesting are brushless dc (BLDC) motors, which, due to their inherently sensorless operation, are considered as viable candidates in large-scale production industries such as the automotive one. Within this trend, this paper deals with the synthesis and implementation of a novel direct torque control (DTC) strategy dedicated to the control of BLDC motor drives. Compared with the most recent and highly performed DTC strategy, the proposed one offers improved reliability due to the achievement of balanced switching frequencies of the inverter upper and lower insulated-gate bipolar transistors, on one hand, and the reduction in the average value of the motor common mode voltage, on the other hand. Furthermore, the torque ripple is significantly damped during sector-to-sector commutations using a three-level hysteresis torque controller. An experientially based comparative study of the most recent and highly performed DTC strategy from one side and the introduced one from the other side clearly highlights the potentialities exhibited by the latest one.


title: Automated Attack Planning

abstract: Penetration Testing is a methodology for assessing network security, by generating and executing possible attacks. Doing so automatically allows for regular and systematic testing. A key question then is how to automatically generate the attacks. A natural way to address this issue is as an attack planning problem. In this thesis, we are concerned with the specific context of regular automated pentesting, and use the term"attack planning"in that sense. The following three research directions are investigated. First, we introduce a conceptual model of computer network attacks, based on an analysis of the penetration testing practices. We study how this attack model can be represented in the PDDL language. Then we describe an implementation that integrates a classical planner with a penetration testing tool. This allows us to automatically generate attack paths for real world pentesting scenarios, and to validate these attacks by executing them. Secondly, we present efficient probabilistic planning algorithms, specifically designed for this problem, that achieve industrial-scale runtime performance (able to solve scenarios with several hundred hosts and exploits). These algorithms take into account the probability of success of the actions and their expected cost (for example in terms of execution time, or network traffic generated). Finally, we take a different direction: instead of trying to improve the efficiency of the solutions developed, we focus on improving the model of the attacker. We model the attack planning problem in terms of partially observable Markov decision processes (POMDP). This grounds penetration testing in a well-researched formalism. POMDPs allow the modelling of information gathering as an integral part of the problem, thus providing for the first time a means to intelligently mix scanning actions with actual exploits.


title: A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming

abstract: We present an intelligent technique for image denoising problem of gray level images degraded with Gaussian white noise in spatial domain. The proposed technique consists of using fuzzy logic as a mapping function to decide whether a pixel needs to be krigged or not. Genetic programming is then used to evolve an optimal pixel intensity-estimation function for restoring degraded images. The proposed system has shown considerable improvement when compared both qualitatively and quantitatively with the adaptive Wiener filter, methods based on fuzzy kriging, and a fuzzy-based averaging technique. Experimental results conducted using an image database confirms that the proposed technique offers superior performance in terms of image quality measures. This also validates the use of hybrid techniques for image restoration. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 224–231, 2007


title: 3–5 GHz UWB Impulse Radio Transmitter and Receiver MMIC Optimized for Long Range Precision Wireless Sensor Networks

abstract: Abstract-In this paper, an ultra wideband impulse radio (UWB-IR) transmitter and receiver monolithic microwave integrate circuit (MMIC) for precision localization sensor network is presented. Longer range is achieved with low power consumption at the transmitter by fully exploiting FCC's peak power constraint efficiently. The transmitter and receiver MMIC are fabricated on a commercial low cost 2 μm GaAs HBT process. The transmitter MMIC peak power output is 20 dBm with power consumption of 0.8 mW at 1 MHz pulse repetitive rate. The receiver MMIC uses non-coherent detection and has a 44 dB front-end conversion gain. The receiver MMIC is cascaded with a 500 MHz baseband amplifier to achieve input tangential signal sensitivity (TSS) of - 71 dBm. Equivalent time sampling using analogue to digital converters running at around a few MHz is utilized for precise time of arrival ranging with low cost components. Two dimensional wireless localization network utilizing time difference of arrival (TDOA) is constructed and test results covering an 80 X 90 m2 area show position error variance of less than 10 cm. Ranging at 200 m is demonstrated with range error variance of better than 15 cm.


title: Toward a Rational Choice Process Theory of Internet Scamming: The Offender's Perspective Research-in-Progress

abstract: Internet fraud scam is a crime enabled by the Internet to swindle Internet users. The global costs of these scams are in the billions of US dollars. Existing research suggests that scammers maximize their economic gain. Although this is a plausible explanation, since the idea of the scam is to fool people to send money, this explanation alone, cannot explain why individuals become Internet scammers. An equally important, albeit unexplored riddle, is the question of what strategies Internet scammers adopt to perform the act. As a first step to address these gaps, we interviewed five Internet scammers in order to develop a rational choice process theory of Internet scammers’ behavior. The initial results suggest that an interplay of socioeconomic and dynamic thinking processes explains why individuals drift into Internet scamming. Once an individual drifts into Internet scamming, a successful scam involves two processes: persuasive strategy and advance fee strategy.


Integrating GIS and MCDM to Deal with Landfill Site Selection


title: Videogames: Dispelling myths and tabloid headlines that videogames are bad

abstract: Videogamers are often portrayed as adolescent overweight males eating fast food in their bedroom, and videogames often blamed in the media for violent crime, obesity, social isolation and depression. However videogaming is a mainstream activity. In Australia 65% of the population play ::: videogames (Digital Australia 2014), and humanity as a species play about 3 billion hours of videogames a week. This paper dispels the myths and sensationalised negative tabloid headlines that videogames are bad by presenting the latest research showing that videogames can help fight ::: depression, improve brain function and stimulate creativity; that gamers have higher levels of family closeness and better attachment to school; and that videogames help boys and young men to relax, cope and socialise. Children and adolescents deliberately choose to play videogames in the knowledge that they will feel better as a result, and videogame play allow players to express ::: themselves in ways they may not feel comfortable doing in real life because of their appearance, gender, sexuality, and/or age. The potential benefits of videogames to the individual and to society are yet to be fully realised. However already videogames are helping many gamers to flourish in life.


title: Glasswing: accelerating mapreduce on multi-core and many-core clusters

abstract: The impact and significance of parallel computing techniques is continuously increasing given the current trend of incorporating more cores in new processor designs. However, many Big Data systems fail to exploit the abundant computational power of multi-core CPUs and GPUs to their full potential. We present Glasswing, a scalable MapReduce framework that employs a configurable mixture of coarse- and fine-grained parallelism to achieve high performance on multi-core CPUs and GPUs. We experimentally evaluated the performance of five MapReduce applications and show that Glasswing outperforms Hadoop on a 64-node multi-core CPU cluster by a factor between 1.8 and 4, and by a factor from 20 to 30 on a 16-node GPU cluster.


title: A Square Root Inverse Filter for Efficient Vision-aided Inertial Navigation on Mobile Devices

abstract: In this paper, we present a square-root inverse sliding window filter (SR-ISWF) for vision-aided inertial navigation systems (VINS). While regular inverse filters suffer from numerical issues, employing their square-root equivalent enables the usage of single-precision number representations, thus achieving considerable speed ups as compared to doubleprecision alternatives on resource-constrained mobile platforms. Besides a detailed description of the SR-ISWF for VINS, which focuses on the numerical procedures that enable exploiting the problem’s structure for gaining in efficiency, this paper presents a thorough validation of the algorithm’s processing requirements and achieved accuracy. In particular, experiments are conducted using a commercial-grade cell phone, where the proposed algorithm is shown to achieve the same level of estimation accuracy, when compared to state-of-the-art VINS algorithms, with significantly higher speed.


title: A Multilingual Multimedia Indian Sign Language Dictionary Tool

abstract: A method of determining time-to-breakdown of a gate dielectric in an NFET or a PFET transistor. For an NFET transistor, the method includes providing an N+ injector ring in the p-substrate and forward biasing the N+ injector ring with respect to the p-substrate. A first positive reference voltage level is applied to the source and the drain regions. A second positive reference voltage level is applied to the gate dielectric. The first and second positive reference voltage levels are maintained on the transistor until breakdown of the gate dielectric occurs. Another embodiment of the method may be used in a PFET transistor.


title: Using Mininet for emulation and prototyping Software-Defined Networks

abstract: Software-Defined Networks (SDNs) represents an innovative approach in the area of computer networks, since they propose a new model to control forwarding and routing data packets that navigate the World Wide Web. Since research on this topic is still in progress, there are not many devices such as routers and switches that implement SDN functionalities; moreover, the existing ones are very expensive. Thus, in order to make researchers able to do experiments and to test novel features of this new paradigm in practice at a low financial cost, one solution is to use virtual network emulators. As a result, this paper focuses on study and evaluation of SDN emulation tool called Mininet. Initial tests suggested that the capacity of rapid and simplified prototyping, the ensuring applicability, the possibility of sharing results and tools at zero cost are positive factors that help scientists boost their researches despite the limitations of the tool in relation to the performance fidelity between the simulated and the real environment. After presenting some concepts of this paradigm, the purpose of its appearance, its elements and how it works, some net prototypes are created to better understand the Mininet tool and an evaluation is done to demonstrate its advantages and disadvantages.


title: Traffic Morphing: An Efficient Defense Against Statistical Traffic Analysis

abstract: Recent work has shown that properties of network traffic that remain observable after encryption, namely packet sizes and timing, can reveal surprising information about the traffic’s contents (e.g., the language of a VoIP call [29], passwords in secure shell logins [20], or even web browsing habits [21, 14]). While there are some legitimate uses for encrypted traffic analysis, these techniques also raise important questions about the privacy of encrypted communications. A common tactic for mitigating such threats is to pad packets to uniform sizes or to send packets at fixed timing intervals; however, this approach is often inefficient. In this paper, we propose a novel method for thwarting statistical traffic analysis algorithms by optimally morphing one class of traffic to look like another class. Through the use of convex optimization techniques, we show how to optimally modify packets in real-time to reduce the accuracy of a variety of traffic classifiers while incurring much less overhead than padding. Our evaluation of this technique against two published traffic classifiers for VoIP [29] and web traffic [14] shows that morphing works well on a wide range of network data—in some cases, simultaneously providing better privacy and lower overhead than naive


title: Incremental multi-step Q-learning

abstract: This paper presents a novel incremental algorithm that combines Q-learning, a well-known dynamic- programming based reinforcement learning method, with the TD(A) return estimation process, which is typically used in actor-critic learning, another well-known dynamic-programming based reinforcement learning method. The parameter A is used to distribute credit throughout sequences of actions, leading to faster learning and also helping to alleviate the non-Markovian effect of coarse state-space quantization. The resulting algorithm, Q(A)-learning, thus combines some of the best features of the Q-learning and actor-critic learning paradigms. The behavior of this algorithm has been demonstrated through computer simulations.


title: An Armband Wearable Device for Overnight and Cuff-Less Blood Pressure Measurement

abstract: 24-h blood pressure (BP) has significant prognostic value for cardiovascular risk screening, but the present BP devices are mainly cuff-based, which are unsuitable for long-term BP measurement, especially during nighttime. In this paper, we developed an armband wearable pulse transit time (PTT) system for 24-h cuff-less BP measurement and evaluated it in an unattended out-of-laboratory setting. Ten healthy young subjects participated in this ambulatory study, where PTT was measured at 30-min interval by this wearable system and the reference BP was measured by a standard oscillometric ambulatory BP monitor. Due to the misalignment of BP and PTT on their recording time caused by the different measurement principles of the two BP devices, a new estimation method has been proposed: transients in PTT were removed from the raw data by defined criteria, and then evenly interpolated, low-pass filtered, and resampled to synchronize at the time when BP was recorded. The results show that with the proposed method, the correlation between PTT and systolic BP (SBP) during nighttime with dynamic range of 21.8 ± 14.2 mmHg has improved from −0.50 ± 0.24 to −0.62 ± 0.20 $(p , and the difference between the estimated and reference SBP has improved from 0.7 ± 10.7 to 2.8 ± 8.2 mmHg with root mean square error reduced from 10.7 to 8.7 mmHg. In addition, the correlation between a very low frequency component of SBP and PTT obtained from the proposed method during nighttime is −0.80 ± 0.10 and the difference is 2.4 ± 5.7 mmHg for a dynamic BP range of 13.5 ± 8.0 mmHg. It is therefore concluded from this study that the proposed wearable system has great potential to be used for overnight SBP monitoring, especially to measure the averaged SBP over a long period.


title: Manifold Density Peaks Clustering Algorithm

abstract: Cluster analysis is a popular technique in statistics and computer science with the objective of grouping similar observations in relatively distinct groups generally known as clusters. In this paper we propose an approach called Manifold Density Peaks Clustering to improve the basic density peaks clustering. It mainly concerns three aspects. First, geodesic distance is adopted to calculate manifold distance matrix. Then, cluster centers are identified automatically by the number of clusters, which improves the inconvenient way of identifying cluster centers by dragging a rectangle manually. Moreover, isometric mapping is introduced to map high dimensional datasets into lower dimension, so as to achieve the goal of dimensionality reduction. Experimental results indicate that the proposed algorithm has achieved the expected results on both low and high dimensional datasets.


title: The Invisible QR Code

abstract: QR (Quick Response) Codes are widely used as a convenient unidirectional communication channel to convey information, such as emails, hyperlinks, or phone numbers, from publicity materials to mobile devices. But the QR Code is not visually appealing and takes up valuable space of publicity materials. In this paper, we propose a new method to embed QR Code on digital screen via temporal psychovisual modulation (TPVM). By exploiting the difference between human eyes and semiconductor imaging sensors in temporal convolution of optical signals, we make QR Code perceptually transparent to human but detectable for mobile devices. Based on the idea of invisible QR Code, many applications can be implemented, e.g., "physical hyperlink" for something interesting on TV or digital signage , "invisible watermark" for anti-piracy in theater. A prototype system introduced in this paper serves as a proof-of-concept of the invisible QR Code and can be improved in future works.


title: A Call For Research On Home Users’ Information Security Behaviour

abstract: The number of home computer users is increasing faster than ever. Home users’ security should be an important research topic in IS security research, not only from the perspective of protecting home users’ personal or work information on their home computers, but also because hijacked home computers have become an ideal breading ground for hackers attacking organizations, and distributing illegal or morally questionable material. Despite the importance of studying home users’ security behaviour, the primary focus of the behavioural IS security research has been on an organizational context. While this research at an organizational context is important, we argue that the “home users” context require more attention by scholars. While there are similarities between “home users’ IS security behaviour” and “employees’ compliance with IS security procedures at organizational context”, it is necessary to understand their differences, to allow research and practice on “home users security behaviour” to develop further. We argue that previous research has not paid attention to such differences. As a first step in remedying the gap in our understanding, we first theorise these differences, we consider, that there are at least nine contextual factors that may result in an individual’s behaviour inconsistency in the workplace and home, and because of this, we argue that the same theories may not explain the use of security features in home and organizational contexts. Based on this conceptualization, we present a research agenda for studying home users’ security behaviour.


title: Pathogens and Politics: Further Evidence That Parasite Prevalence Predicts Authoritarianism

abstract: According to a "parasite stress" hypothesis, authoritarian governments are more likely to emerge in regions characterized by a high prevalence of disease-causing pathogens. Recent cross-national evidence is consistent with this hypothesis, but there are inferential limitations associated with that evidence. We report two studies that address some of these limitations, and provide further tests of the hypothesis. Study 1 revealed that parasite prevalence strongly predicted cross-national differences on measures assessing individuals' authoritarian personalities, and this effect statistically mediated the relationship between parasite prevalence and authoritarian governance. The mediation result is inconsistent with an alternative explanation for previous findings. To address further limitations associated with cross-national comparisons, Study 2 tested the parasite stress hypothesis on a sample of traditional small-scale societies (the Standard Cross-Cultural Sample). Results revealed that parasite prevalence predicted measures of authoritarian governance, and did so even when statistically controlling for other threats to human welfare. (One additional threat—famine—also uniquely predicted authoritarianism.) Together, these results further substantiate the parasite stress hypothesis of authoritarianism, and suggest that societal differences in authoritarian governance result, in part, from cultural differences in individuals' authoritarian personalities.


title: What can i do around here? Deep functional scene understanding for cognitive robots

abstract: For robots that have the capability to interact with the physical environment through their end effectors, understanding the surrounding scenes is not merely a task of image classification or object recognition. To perform actual tasks, it is critical for the robot to have a functional understanding of the visual scene. Here, we address the problem of localizing and recognition of functional areas from an arbitrary indoor scene, formulated as a two-stage deep learning based detection pipeline. A new scene functionality testing-bed, which is complied from two publicly available indoor scene datasets, is used for evaluation. Our method is evaluated quantitatively on the new dataset, demonstrating the ability to perform efficient recognition of functional areas from arbitrary indoor scenes. We also demonstrate that our detection model can be generalized onto novel indoor scenes by cross validating it with the images from two different datasets.