title: GINTATE: Scalable and Extensible Deep Packet Inspection System for Encrypted Network Traffic: Session Resumption in Transport Layer Security Communication Considered Harmful to DPI

abstract: Deep packet inspection (DPI) is a basic monitoring technology, which realizes network traffic control based on application payload. The technology is used to prevent threats (e.g., intrusion detection systems, firewalls) and extract information (e.g., content filtering systems). Moreover, transport layer security (TLS) monitoring is required because of the increasing use of the TLS protocol, particularly by hypertext transfer protocol secure (HTTPS). TLS monitoring is different from TCP monitoring in two aspects. First, monitoring systems cannot inspect the content in TLS communication, which is encrypted. Second, TLS communication is a session unit composed of one or more TCP connections. In enterprise networks, dedicated TLS proxies are deployed to perform TLS monitoring. However, the proxies cannot be used when monitored devices are unable to use a custom certificate. Additionally, these networks contain problems of scale and complexity that affect the monitoring. Therefore, the DPI processing using another method requires high-speed processing and various protocol analyses across TCP connections in TLS monitoring. However, it is difficult to realize both simultaneously. We propose GINTATE, which decrypts TLS communication using shared keys and monitors the results. GINTATE is a scalable architecture that uses distributed computing and considers relational sessions across multiple TCP connections in TLS communication. Additionally, GINTATE achieves DPI processing by adding an extensible analysis module. By comparing GINTATE against other systems, we show that it can perform DPI processing by managing relational sessions via distributed computing and that it is scalable.


title: Inferring Networks of Diffusion and Influence

abstract: Information diffusion and virus propagation are fundamental processes talking place in networks. While it is often possible to directly observe when nodes become infected, observing individual transmissions (i.e., who infects whom or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and in practice gives provably near-optimal performance. We demonstrate the effectiveness of our approach by tracing information cascades in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.


title: Reduction by half: the impact on bees of 34 years of urbanization

abstract: Urban ecosystems are growing rapidly and urbanization is an important cause of the loss of biodiversity. Bees are declining in abundance worldwide, including urban areas, and this decline is alarming because of their global importance as plant pollinators. Here we examine that decline by comparing a bee assemblage sampled in the 1980s and again in 2015, in an urban area of the city of Curitiba. Both studies sampled assemblages with hand-nets every two weeks during one year of study. Bee species richness has declined by 45% (112 species then, 63 today). Two species that have disappeared, Gaesischia fulgurans (Holmberg, 1903) and Thectochlora basiatra (Strand, 1910), have also disappeared elsewhere in the city. Also, relative abundances of species have changed, notably with the increase of social bees. Large bees that nest in cavities have also increased relative to small bees that nest in the ground. These findings are similar with previous reports indicating the sensibility of bees to urbanization. The increase in paved areas, in urban population and in exotic plants are all probably responsible for the sharp decline in bee diversity and abundance.


title: AssistOn-Gait: An overground gait trainer with an active pelvis-hip exoskeleton

abstract: We propose AssistOn-Gait, a robot-assisted gait and balance trainer, for restoration and improvement of gait and balance of patients with disabilities affecting their lower extremities. In addition to overground gait and balance training, pelvis-hip exercises aimed to correct compensatory movements arising from abnormal gait patterns can be delivered with AssistOn-Gait, extending the type of therapies that can be administered using lower extremity exoskeletons. AssistOn-Gait consists of an active, self-aligning pelvis-hip exoskeleton supported by a series elastic holonomic mobile platform. The series elastic mobile platform enables compensation of the robot dynamics for transparent operation, while also allowing patients to start/stop motion, vary their speed, sidestep to maintain balance and turn to change walking direction. The pelvis-hip exoskeleton can independently actuate 6 degrees of freedom: the hip rotations in the sagittal plane, the pelvic rotation in the transverse plane, the pelvic tilt in the coronal plane, the lateral and the vertical pelvic displacements. Thanks to its backdriveable design, automatically adjusting the center of rotation of its joint axes, the self-aligning exoskeleton enables an ideal match between the hip rotation axes and the device axes, and can do so while allowing for the natural pelvic movements during walking. This feature not only guarantees ergonomy but also extends the usable range of motion of the hip joints.


title: Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation

abstract: Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to replicate some of these abilities with a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecologically naturalistic simulated environment in which the agent can move and interact with objects it sees, the agent learns a world model predicting the dynamic consequences of its actions. Simultaneously, the agent learns to take actions that adversarially challenge the developing world model, pushing the agent to explore novel and informative interactions with its environment. We demonstrate that this policy leads to the self-supervised emergence of a spectrum of complex behaviors, including ego motion prediction, object attention, and object gathering. Moreover, the world model that the agent learns supports improved performance on object dynamics prediction and localization tasks. Our results are a proof-of-principle that computational models of intrinsic motivation might account for key features of developmental visuomotor learning in infants.


title: Chinese character recognition: history, status and prospects

abstract: Chinese character recognition (CCR) is an important branch of pattern recognition. It was considered as an extremely difficult problem due to the very large number of categories, complicated structures, similarity between characters, and the variability of fonts or writing styles. Because of its unique technical challenges and great social needs, the last four decades witnessed the intensive research in this field and a rapid increase of successful applications. However, higher recognition performance is continuously needed to improve the existing applications and to exploit new applications. This paper first provides an overview of Chinese character recognition and the properties of Chinese characters. Some important methods and successful results in the history of Chinese character recognition are then summarized. As for classification methods, this article pays special attention to the syntactic-semantic approach for online Chinese character recognition, as well as the metasynthesis approach for discipline crossing. Finally, the remaining problems and the possible solutions are discussed.


title: Temperature dependent absorption spectroscopy of some tautomeric azo dyes and Schiff bases

abstract: The spectral properties of several aromatic azo dyes of fundamental importance (4-phenylazo-1-naphthol, 1-phenylazo-2-naphthol and 2-phenylazo-1-naphthol) and Schiff bases (N-(2-hydroxy-1-naphthylmethylidene)aniline and N-(1-hydroxy-2-naphthylmethylidene)aniline) are investigated at temperatures down to 100 K. The observed spectral changes are interpreted in terms of the existing tautomeric equilibrium and hydrogen-bonding (H-bonding) with the solvent. In non-polar solvents the decrease in temperature leads, in all compounds except 4-phenylazo-1-naphthol, to a full transformation of the enol tautomeric form (A) to the quinone form (H). The temperature dependence of the equilibrium A ::: ⇌ ::: H exhibits a shift around 240 K. In ethanol, due to intermolecular H-bonding, in the case of the azo dyes full transformation was not observed, while the behavior of the Schiff bases is the same as in non-polar solvents. The thermodynamic ::: parameters of the equilibrium A ::: ⇌ ::: H were estimated in both non-polar solvent and ethanol for all investigated compounds.


Communication optimizations for parallel computing using data access information


Constructive Language in News Comments


title: Low-Overhead Distributed Transaction Coordination

abstract: This paper presents Granola, a transaction coordination infrastructure for building reliable distributed storage applications. Granola provides a strong consistency model, while significantly reducing transaction coordination overhead. We introduce specific support for a new type of independent distributed transaction, which we can serialize with no locking overhead and no aborts due to write conflicts. Granola uses a novel timestamp-based coordination mechanism to order distributed transactions, offering lower latency and higher throughput than previous systems that offer strong consistency. ::: ::: Our experiments show that Granola has low overhead, is scalable and has high throughput. We implemented the TPC-C benchmark on Granola, and achieved 3× the throughput of a platform using a locking approach.


title: Paper-Based Bimodal Sensor for Electronic Skin Applications

abstract: We present the development of a flexible bimodal sensor using a paper platform and inkjet printing method, which are suited for low-cost fabrication processes and realization of flexible devices. In this study, we employed a vertically stacked bimodal device architecture in which a temperature sensor is stacked on top of a pressure sensor and operated on different principles, allowing the minimization of interference effects. For the temperature sensor placed in the top layer, we used the thermoelectric effect and formed a closed-loop thermocouple composed of two different printable inks (conductive PEDOT:PSS and silver nanoparticles on a flexible paper platform) and obtained temperature-sensing capability over a wide range (150 °C). For the pressure sensor positioned in the bottom layer, we used microdimensional pyramid-structured poly(dimethylsiloxane) coated with multiwall carbon nanotube conducting ink. Our pressure sensor exhibits a high-pressure sensitivity over a wide range (100 Pa to 5 kPa) and hi...


title: Mobile Robots Navigation in Indoor Environments Using Kinect Sensor

abstract: This paper presents the development of a perception system for indoor environments to allow autonomous navigation for surveillance mobile robots. The system is composed by two parts. The first part is a reactive navigation system in which a mobile robot moves avoiding obstacles in environment, using the distance sensor Kinect. The second part of this system uses a artificial neural network (ANN) to recognize different configurations of the environment, for example, path ahead, left path, right path and intersections. The ANN is trained using data captured by the Kinect sensor in indoor environments. This way, the robot becomes able to perform a topological navigation combining internal reactive behavior to avoid obstacles and the ANN to locate the robot in the environment, in a deliberative behavior. The topological map is represented by a graph which represents the configuration of the environment, where the hallways (path ahead) are the edges and locations (left path and intersection, for example) are the vertices. The system also works in the dark, which is a great advantage for surveillance systems. The experiments were performed with a Pioneer P3-AT robot equipped with a Kinect sensor in order to validate and evaluate this approach. The proposed method demonstrated to be a promising approach to autonomous mobile robots navigation.


title: An object-oriented high-level design-based class cohesion metric

abstract: Context: Class cohesion is an important object-oriented software quality attribute. Assessing class cohesion during the object-oriented design phase is one important way to obtain more comprehensible and maintainable software. In practice, assessing and controlling cohesion in large systems implies measuring it automatically. One issue with the few existing cohesion metrics targeted at the high-level design phase is that they are not based on realistic assumptions and do not fulfill expected mathematical properties. Objective: This paper proposes a High-Level Design (HLD) class cohesion metric, which is based on realistic assumptions, complies with expected mathematical properties, and can be used to automatically assess design quality at early stages using UML diagrams. Method: The notion of similarity between pairs of methods and pairs of attribute types in a class is introduced and used as a basis to introduce a novel high-level design class cohesion metric. The metric considers method-method, attribute-attribute, and attribute-method direct and transitive interactions. We validate this Similarity-based Class Cohesion (SCC) metric theoretically and empirically. The former includes a careful study of the mathematical properties of the metric whereas the latter investigates, using four open source software systems and 10 cohesion metrics, whether SCC is based on realistic assumptions and whether it better explains the presence of faults, from a statistical standpoint, than other comparable cohesion metrics, considered individually or in combination. Results: Results confirm that SCC is based on clearly justified theoretical principles, relies on realistic assumptions, and is an early indicator of quality (fault occurrences). Conclusion: It is concluded that SCC is both theoretically valid and supported by empirical evidence. It is a better alternative to measure class cohesion than existing HLD class cohesion metrics.


title: Coreference Resolution in a Modular, Entity-Centered Model

abstract: Coreference resolution is governed by syntactic, semantic, and discourse constraints. We present a generative, model-based approach in which each of these factors is modularly encapsulated and learned in a primarily unsu-pervised manner. Our semantic representation first hypothesizes an underlying set of latent entity types, which generate specific entities that in turn render individual mentions. By sharing lexical statistics at the level of abstract entity types, our model is able to substantially reduce semantic compatibility errors, resulting in the best results to date on the complete end-to-end coreference task.


title: Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay

abstract: This paper introduces a novel method for learning how to play the most difficult Atari 2600 games from the Arcade Learning Environment using deep reinforcement learning. The proposed method, human checkpoint replay, consists in using checkpoints sampled from human gameplay as starting points for the learning process. This is meant to compensate for the difficulties of current exploration strategies, such as epsilon-greedy, to find successful control policies in games with sparse rewards. Like other deep reinforcement learning architectures, our model uses a convolutional neural network that receives only raw pixel inputs to estimate the state value function. We tested our method on Montezuma's Revenge and Private Eye, two of the most challenging games from the Atari platform. The results we obtained show a substantial improvement compared to previous learning approaches, as well as over a random player. We also propose a method for training deep reinforcement learning agents using human gameplay experience, which we call human experience replay.


title: Adaptive Neural Networks for Efficient Inference

abstract: We present an approach to adaptively utilize deep neural networks in order to reduce the evaluation time on new examples without loss of accuracy. Rather than attempting to redesign or approximate existing networks, we propose two schemes that adaptively utilize networks. We first pose an adaptive network evaluation scheme, where we learn a system to adaptively choose the components of a deep network to be evaluated for each example. By allowing examples correctly classified using early layers of the system to exit, we avoid the computational time associated with full evaluation of the network. We extend this to learn a network selection system that adaptively selects the network to be evaluated for each example. We show that computational time can be dramatically reduced by exploiting the fact that many examples can be correctly classified using relatively efficient networks and that complex, computationally costly networks are only necessary for a small fraction of examples. We pose a global objective for learning an adaptive early exit or network selection policy and solve it by reducing the policy learning problem to a layer-by-layer weighted binary classification problem. Empirically, these approaches yield dramatic reductions in computational cost, with up to a 2.8x speedup on state-of-the-art networks from the ImageNet image recognition challenge with minimal (<1%) loss of top5 accuracy.


title: The Myth Incarnate: Recoupling Processes, Turmoil, and Inhabited Institutions in an Urban Elementary School.

abstract: The study of institutional myths has been central to organizational sociology, cultural sociology, and the sociology of education for 30 years. This article examines how the myth concept has been u...


title: An Integrative Theory of Prefrontal Cortex Function

abstract: ▪ Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed


title: Psychological targeting as an effective approach to digital mass persuasion

abstract: Abstract People are exposed to persuasive communication across many different contexts: Governments, companies, and political parties use persuasive appeals to encourage people to eat healthier, purchase a particular product, or vote for a specific candidate. Laboratory studies show that such persuasive appeals are more effective in influencing behavior when they are tailored to individuals’ unique psychological characteristics. However, the investigation of large-scale psychological persuasion in the real world has been hindered by the questionnaire-based nature of psychological assessment. Recent research, however, shows that people’s psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook Likes or Tweets. Capitalizing on this form of psychological assessment from digital footprints, we test the effects of psychological persuasion on people’s actual behavior in an ecologically valid setting. In three field experiments that reached over 3.5 million individuals with psychologically tailored advertising, we find that matching the content of persuasive appeals to individuals’ psychological characteristics significantly altered their behavior as measured by clicks and purchases. Persuasive appeals that were matched to people’s extraversion or openness-to-experience level resulted in up to 40% more clicks and up to 50% more purchases than their mismatching or unpersonalized counterparts. Our findings suggest that the application of psychological targeting makes it possible to influence the behavior of large groups of people by tailoring persuasive appeals to the psychological needs of the target audiences. We discuss both the potential benefits of this method for helping individuals make better decisions and the potential pitfalls related to manipulation and privacy.


title: Click shaping to optimize multiple objectives

abstract: Recommending interesting content to engage users is important for web portals (e.g. AOL, MSN, Yahoo!, and many others). Existing approaches typically recommend articles to optimize for a single objective, i.e., number of clicks. However a click is only the starting point of a user's journey and subsequent downstream utilities such as time-spent and revenue are important. In this paper, we call the problem of recommending links to jointly optimize for clicks and post-click downstream utilities click shaping. We propose a multi-objective programming approach in which multiple objectives are modeled in a constrained optimization framework. Such a formulation can naturally incorporate various application-driven requirements. We study several variants that model different requirements as constraints and discuss some of the subtleties involved. We conduct our experiments on a large dataset from a real system by using a newly proposed unbiased evaluation methodology [17]. Through extensive experiments we quantify the tradeoff between different objectives under various constraints. Our experimental results show interesting characteristics of different formulations and our findings may provide valuable guidance to the design of recommendation engines for web portals.


title: FREE AND OPEN-SOURCE SIMULATION SOFTWARE "URURAU"

abstract: Ururau is a free and open-source software written in the Java programming language. The Ururau is able to develop models of simulation at any of the component layers of the software structure. This means that models can be constructed on the layer of the library (lower level), in the core of the software (new palettes, for example) or top layer of the graphical interface. Then, if you want to build simulation models with non-commercial software, with the option to either write its code both in Java as in a graphical user interface of simple operation, you have the option of using Ururau.


title: Text Clustering Using Frequent Weighted Utility Itemsets

abstract: Text clustering is an important topic in text mining. One of the most effective methods for text clustering is an approach based on frequent itemsets FIs, and thus, there are many related algorithms that aim to improve the accuracy of text clustering. However, these do not focus on the weights of terms in documents, even though the frequency of each term in each document has a great impact on the results. In this work, we propose a new method for text clustering based on frequent weighted utility itemsets FWUI. First, we calculate the Term Frequency TF for each term in documents to create a weight matrix for all documents. The weights of terms in documents are based on the Inverse Document Frequency. Next, we use the Modification Weighted Itemset Tidset MWIT-FWUI algorithm for mining FWUI from a number matrix and the weights of terms in documents. Finally, based on frequent utility itemsets, we cluster documents using the MC Maximum Capturing algorithm. The proposed method has been evaluated on three data sets consisting of 1,600 documents covering 16 topics. The experimental results show that our method, using FWUI, improves the accuracy of the text clustering compared to methods using FIs.


title: Analysis of length‐weight relationship of sympatric hill stream teleosts Barilius bendelisis (Hamilton, 1807) and Barilius vagra (Hamilton, 1822) from Garhwal Himalaya, India

abstract: Summary ::: ::: The present study describes the length–weight relationships (LWRs) of two important ornamental hill stream fishes, Barilius bendelisis and Barilius vagra (Hamilton), of northern India. A total of 630 specimens were collected from January to June 2011 in a spring-fed stream in Garhwal Himalaya, India. Information regarding LWRs of these species was not yet available in FishBase.


title: EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning

abstract: Over the last few years, deep learning techniques have yielded significant improvements in image inpainting. However, many of these techniques fail to reconstruct reasonable structures as they are commonly over-smoothed and/or blurry. This paper develops a new approach for image inpainting that does a better job of reproducing filled regions exhibiting fine details. We propose a two-stage adversarial model EdgeConnect that comprises of an edge generator followed by an image completion network. The edge generator hallucinates edges of the missing region (both regular and irregular) of the image, and the image completion network fills in the missing regions using hallucinated edges as a priori. We evaluate our model end-to-end over the publicly available datasets CelebA, Places2, and Paris StreetView, and show that it outperforms current state-of-the-art techniques quantitatively and qualitatively. Code and models available at: this https URL


title: 5G: Personal mobile internet beyond what cellular did to telephony

abstract: Cellular technology has dramatically changed our society and the way we communicate. First it impacted voice telephony, and then has been making inroads into data access, applications, and services. However, today potential capabilities of the Internet have not yet been fully exploited by cellular systems. With the advent of 5G we will have the opportunity to leapfrog beyond current Internet capabilities.


title: Exploring and engineering the cell surface interface.

abstract: Cells are inherently sensitive to local mesoscale, microscale, and nanoscale patterns of chemistry and topography. We review current approaches to control cell behavior through the nanoscale engineering of materials surfaces. Far-reaching implications are emerging for applications including medical implants, cell supports, and materials that can be used as instructive three-dimensional environments for tissue regeneration.


title: Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells

abstract: Cytosine methylation is required for mammalian development and is often perturbed in human cancer. To determine how this epigenetic modification is distributed in the genomes of primary and transformed cells, we used an immunocapturing approach followed by DNA microarray analysis to generate methylation profiles of all human chromosomes at 80-kb resolution and for a large set of CpG islands. In primary cells we identified broad genomic regions of differential methylation with higher levels in gene-rich neighborhoods. Female and male cells had indistinguishable profiles for autosomes but differences on the X chromosome. The inactive X chromosome (Xi) was hypermethylated at only a subset of gene-rich regions and, unexpectedly, overall hypomethylated relative to its active counterpart. The chromosomal methylation profile of transformed cells was similar to that of primary cells. Nevertheless, we detected large genomic segments with hypomethylation in the transformed cell residing in gene-poor areas. Furthermore, analysis of 6,000 CpG islands showed that only a small set of promoters was methylated differentially, suggesting that aberrant methylation of CpG island promoters in malignancy might be less frequent than previously hypothesized.


title: Spirituality and Religion in the Care of Surgical Oncology Patients with Life-Threatening or Advanced Illnesses

abstract: Spiritual and religious concerns often become of central importance in the care of surgical oncology patients confronted with their mortality. Unfortunately, surgeons are often ill prepared or reluctant to address the spiritual and religious needs of their patients. In this article, working definitions of spirituality versus religiosity will be developed in the context of the three largest monotheistic religions in America: Christianity, Islam, and Judaism. Disease, dying, and death will be explored with respect to these faiths and examples of how to address religious beliefs in practical clinical settings will be given. Finally, specific suggestions will be made for surgeons to better understand, empathize with, and address the needs of their seriously ill patients in a holistic manner.


title: A Logic-Based Computational Framework for Inferring Cognitive Affordances

abstract: The concept of “affordance” refers to the relationship between human perceivers and aspects of their environment. Being able to infer affordances is central to commonsense reasoning, tool use and creative problem solving in artificial agents. Existing approaches to inferring affordances have focused on functional aspects, relying on either static ontologies or statistical formalisms to extract relationships between physical features of objects, actions, and the corresponding effects of their interaction. These approaches do not provide flexibility with which to reason about affordances in the open world, where affordances are influenced by changing context, social norms, historical precedence, and uncertainty. We develop a computational framework comprising a probabilistic rules-based logical representation coupled with a computational architecture (cognitive affordances logically expressed) to reason about affordances in a more general manner than described in the existing literature. Our computational architecture allows robotic agents to make deductive and abductive inferences about functional and social affordances, collectively and dynamically, thereby allowing the agent to adapt to changing conditions. We demonstrate our approach with experiments, and show that an agent can successfully reason through situations that involve a tight interplay between various social and functional norms.


title: Biochemical and structural changes in mitochondria and other cellular components of pea cotyledons during germination.

abstract: Integrated studies comprising biochemical and electron microscopic analysis suggested that the increase in respiratory activity of pea cotyledon mitochondria during germination results from further development of the original mitochondria present in dormant seeds. Electron microscopy of isolated mitochondria as well as mitochondria in situ has revealed that membranes are scarce in the mitochondria present in dormant seeds. Mitochondrial cristae become well developed during the initial stages of germination. Crude mitochondrial preparations from pea cotyledons were fractionated by sucrose density gradient centrifugation and analyzed through electron microscopy. These studies showed that, at all stages of germination, "peroxisome"-like structures were present in the fractions of higher sucrose densities than that containing mitochondria. Biochemical studies revealed that the activities of catalase (H2O2:H2O2 oxidoreductase, EC 1.11.1.6) and peroxidase (guaicol:H2O2 oxidoreductase, EC 1.11.1.7) were associat...


title: Training Products of Experts by Minimizing Contrastive Divergence

abstract: It is possible to combine multiple latent-variable models of the same data by multiplying their probability distributions together and then renormalizing. This way of combining individual "expert" models makes it hard to generate samples from the combined model but easy to infer the values of the latent variables of each expert, because the combination rule ensures that the latent variables of different experts are conditionally independent when given the data. A product of experts (PoE) is therefore an interesting candidate for a perceptual system in which rapid inference is vital and generation is unnecessary. Training a PoE by maximizing the likelihood of the data is difficult because it is hard even to approximate the derivatives of the renormalization term in the combination rule. Fortunately, a PoE can be trained using a different objective function called "contrastive divergence" whose derivatives with regard to the parameters can be approximated accurately and efficiently. Examples are presented of contrastive divergence learning using several types of expert on several types of data.


title: Moment and curvature preserving technique for accurate ellipse boundary detection

abstract: Circles and their elliptic projections are very commonly used image features in computer vision applications. Thus, it is very important to be able to determine their location in an accurate manner. A technique for determining an ellipse boundary with subpixel precision is proposed. The technique, called the moment and curvature preserving detection (MCP), utilizes the first three intensity moments and the intensity gradient of the image. The idea of using moments for subpixel edge detection is not new, but in the case of ellipses the moments do not provide sufficient information for precise detection. However, if the local curvature is augmented to the observations, the ellipse boundary can be determined reliably.


title: Personal tracking as lived informatics

abstract: This paper characterises the use of activity trackers as "lived informatics". This characterisation is contrasted with other discussions of personal informatics and the quantified self. The paper reports an interview study with activity tracker users. The study found: people do not logically organise, but interweave various activity trackers, sometimes with ostensibly the same functionality; that tracking is often social and collaborative rather than personal; that there are different styles of tracking, including goal driven tracking and documentary tracking; and that tracking information is often used and interpreted with reference to daily or short term goals and decision making. We suggest there will be difficulties in personal informatics if we ignore the way that personal tracking is enmeshed with everyday life and people's outlook on their future.


title: Management of Sacral Dimples Detected on Routine Newborn Examination: A Case Series and Review

abstract: Isolated sacral dimples are a common occurrence in Caucasian newborns and there has been a concern about their association with occult spinal dysraphism. A retrospective study was carried out in which infants born in a regional hospital during the year 2003 with a diagnosis of sacral dimple were examined. Twenty-six infants (0.5%) were identified from 5,440 live births. There was a female predominance (61.5%) and all infants were born at term. A tuft of hair close to the dimple was described in 6 babies, but none had any neurological deficit. Only 4 infants underwent ultrasonography or magnetic resonance imaging. No abnormality was detected. None of the 16 children who had been followed up (median 25.7 months) had any neurological deficit. A review of the current literature strongly indicates that isolated sacral dimples are innocuous and imaging study for occult spinal dysraphism is not indicated. A diagnostic strategy on the selective use of ultrasonography as a screening examination in atypical cases is proposed.


title: Acute and subchronic toxicity studies of kernel extract of Sclerocarya birrea in rats

abstract: This study was conducted to explore the relationship among body measurements in 3 strains of broilers chicken (Arbor Acre, Marshal and Ross) using principal component analysis with the view of identifying those components that define body conformation in broilers. A total of 180 birds were used, 60 per strain. The parameters recorded at 8 weeks of age were body weight, shank length, thigh length, drumstick length, body length, body width, breast width and wing length. Principal component analysis with variance maximizing orthogonal rotation was used to extract the components. Two principal components were extracted in Arbor Acre which explained 65% of the total variation in the original variables. Similarly three principal components each were extracted in Marshal and Ross strains accounting for 74.76% and 70% of the total variance respectively. Generally, PC1 had the largest share of the total variance and correlated highly with breast width, wing length, thigh length, shank length and body length. PC1 was termed the generalized form of broilers. PC2 had its loadings on drum stick length, breast width, thigh length and wing length while PC3 had positive loading on drumstick length. These components could be used as selection criteria for improving body size of broilers. Key words: Body parameters, Broilers, Loadings, Variance maximizing rotation.


title: Alignment of professional, academic and industrial development needs for quantity surveyors

abstract: The academic, professional and training needs of Quantity Surveyors are pulled by different stakeholders in different directions. Academics are interested in producing a rounded graduate with the basic foundation in knowledge for further development whereas professional bodies are interested in graduates capable of progression to full professional status through the achievement of the required core competencies (RICS, 2009). The industry is looking for a graduate who can straight away contribute to the growth and daily functions of business activity. Hence, there is a three directional pull on the development needs of the Quantity Surveyor (QS). The present education system of the QS does not recognise these multi-directional needs and hence often produces a graduate whom the industry sees as not fulfilling their requirements. This leads to many problems with greater levels of employer and graduate dissatisfaction and obstacles to early career development of the QS graduate. ::: This research aims at investigating the changing development needs of Quantity Surveyors within a post recession industrial environment that satisfies the aspirations of industrial, professional and academic stakeholders. The paper will present the initial findings of the research based on a series of stakeholder interviews examining RICS competencies and academic curricular.


title: HARRISON: A Benchmark on HAshtag Recommendation for Real-world Images in Social Networks

abstract: Simple, short, and compact hashtags cover a wide range of information on social networks. Although many works in the field of natural language processing (NLP) have demonstrated the importance of hashtag recommendation, hashtag recommendation for images has barely been studied. In this paper, we introduce the HARRISON dataset, a benchmark on hashtag recommendation for real world images in social networks. The HARRISON dataset is a realistic dataset, composed of 57,383 photos from Instagram and an average of 4.5 associated hashtags for each photo. To evaluate our dataset, we design a baseline framework consisting of visual feature extractor based on convolutional neural network (CNN) and multi-label classifier based on neural network. Based on this framework, two single feature-based models, object-based and scene-based model, and an integrated model of them are evaluated on the HARRISON dataset. Our dataset shows that hashtag recommendation task requires a wide and contextual understanding of the situation conveyed in the image. As far as we know, this work is the first vision-only attempt at hashtag recommendation for real world images in social networks. We expect this benchmark to accelerate the advancement of hashtag recommendation.


title: Machine learning for intrusion detection in MANET: a state-of-the-art survey

abstract: Machine learning consists of algorithms that are first trained with reference input to "learn" its specifics and then used on unseen input for classification purposes. Mobile ad-hoc wireless networks (MANETs) have drawn much attention to research community due to their advantages and growing demand. However, they appear to be more susceptible to various attacks harming their performance than any other kind of network. Intrusion Detection Systems represent the second line of defense against malevolent behavior to MANETs, since they monitor network activities in order to detect any malicious attempt performed by intruders. Due to the inherent distributed architecture of MANET, traditional cryptography schemes cannot completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying machine learning methods for IDS these challenges can be overcome. In this paper, we present the most prominent models for building intrusion detection systems by incorporating machine learning in the MANET scenario. We have structured our survey into four directions of machine learning methods: classification approaches, association rule mining techniques, neural networks and instance based learning approaches. We analyze the most well-known approaches and present notable achievements but also drawbacks or flaws that these methods have. Finally, in concluding our survey we provide some findings of paramount importance identifying open issues in the MANET field of interest.


title: Smart water quality monitoring system

abstract: Nowadays Internet of Things (IoT) and Remote Sensing (RS) techniques are used in different area of research for monitoring, collecting and analysis data from remote locations. Due to the vast increase in global industrial output, rural to urban drift and the over-utilization of land and sea resources, the quality of water available to people has deteriorated greatly. The high use of fertilizers in farms and also other chemicals in sectors such as mining and construction have contributed immensely to the overall reduction of water quality globally. Water is an essential need for human survival and therefore there must be mechanisms put in place to vigorously test the quality of water that made available for drinking in town and city articulated supplies and as well as the rivers, creeks and shoreline that surround our towns and cities. The availability of good quality water is paramount in preventing outbreaks of water-borne diseases as well as improving the quality of life. Fiji Islands are located in the vast Pacific Ocean which requires a frequent data collecting network for the water quality monitoring and IoT and RS can improve the existing measurement. This paper presents a smart water quality monitoring system for Fiji, using IoT and remote sensing technology.


title: Semi-supervised Learning via Gaussian Processes

abstract: We present a probabilistic approach to learning a Gaussian Process classifier in the presence of unlabeled data. Our approach involves a "null category noise model" (NCNM) inspired by ordered categorical noise models. The noise model reflects an assumption that the data density is lower between the class-conditional densities. We illustrate our approach on a toy problem and present comparative results for the semi-supervised classification of handwritten digits.


title: Assessing technical debt by identifying design flaws in software systems

abstract: Tough time-to-market constraints and unanticipated integration or evolution issues lead to design tradeoffs that usually cause flaws in the structure of a software system. Thus, maintenance costs grow significantly. The impact of these design decisions, which provide short-term benefits at the expense of the system's design integrity, is usually referred to as technical debt. In this paper, I propose a novel framework for assessing technical debt using a technique for detecting design flaws, i.e., specific violations of well-established design principles and rules. To make the framework comprehensive and balanced, it is built on top of a set of metrics-based detection rules for well-known design flaws that cover all of the major aspects of design such as coupling, complexity, and encapsulation. I demonstrate the effectiveness of the framework by assessing the evolution of technical debt symptoms over a total of 63 releases of two popular Eclipse® projects. The case study shows how the framework can detect debt symptoms and past refactoring actions. The experiment also reveals that in the absence of such a framework, restructuring actions are not always coherent and systematic, not even when performed by very experienced developers.


title: Motor performance and jet propulsion in Nautilus: implications for cephalopod paleobiology and evolu

abstract: A variable displacement pump includes a plurality of pistons and a swash plate arranged therewith, with the swash plate displaced in response to pump discharge manifold pressure for controlling the stroke of the pistons to maintain a predetermined substantially constant pressure with a variable flow of fluid through the pump.


title: Multi-cue Zero-Shot Learning with Strong Supervision

abstract: Scaling up visual category recognition to large numbers of classes remains challenging. A promising research direction is zero-shot learning, which does not require any training data to recognize new classes, but rather relies on some form of auxiliary information describing the new classes. Ultimately, this may allow to use textbook knowledge that humans employ to learn about new classes by transferring knowledge from classes they know well. The most successful zero-shot learning approaches currently require a particular type of auxiliary information – namely attribute annotations performed by humans – that is not readily available for most classes. Our goal is to circumvent this bottleneck by substituting such annotations by extracting multiple pieces of information from multiple unstructured text sources readily available on the web. To compensate for the weaker form of auxiliary information, we incorporate stronger supervision in the form of semantic part annotations on the classes from which we transfer knowledge. We achieve our goal by a joint embedding framework that maps multiple text parts as well as multiple semantic parts into a common space. Our results consistently and significantly improve on the state-of-the-art in zero-short recognition and retrieval.


title: A Novel Feature Selection Approach Based on FODPSO and SVM

abstract: A novel feature selection approach is proposed to address the curse of dimensionality and reduce the redundancy of hyperspectral data. The proposed approach is based on a new binary optimization method inspired by fractional-order Darwinian particle swarm optimization (FODPSO). The overall accuracy (OA) of a support vector machine (SVM) classifier on validation samples is used as fitness values in order to evaluate the informativity of different groups of bands. In order to show the capability of the proposed method, two different applications are considered. In the first application, the proposed feature selection approach is directly carried out on the input hyperspectral data. The most informative bands selected from this step are classified by the SVM. In the second application, the main shortcoming of using attribute profiles (APs) for spectral–spatial classification is addressed. In this case, a stacked vector of the input data and an AP with all widely used attributes are created. Then, the proposed feature selection approach automatically chooses the most informative features from the stacked vector. Experimental results successfully confirm that the proposed feature selection technique works better in terms of classification accuracies and CPU processing time than other studied methods without requiring the number of desired features to be set a priori by users.


title: How Blockchain Could Empower eHealth: An Application for Radiation Oncology

abstract: Electronic medical records (EMRs) contain critical, highly sensitive private healthcare information, and need to be frequently shared among peers. Blockchain provides a shared, immutable and transparent history of all the transactions to build applications with trust, accountability and transparency. This provides a unique opportunity to develop a secure and trustable EMR data management and sharing system using blockchain. In this paper, we discuss our perspectives on blockchain based healthcare data management and present a prototype of a framework for managing and sharing EMR data for cancer patient care.


title: Adversarial Robustness: Softmax versus Openmax

abstract: Deep neural networks (DNNs) provide state-of-the-art results on various tasks and are widely used in real world applications. However, it was discovered that machine learning models, including the best performing DNNs, suffer from a fundamental problem: they can unexpectedly and confidently misclassify examples formed by slightly perturbing otherwise correctly recognized inputs. Various approaches have been developed for efficiently generating these so-called adversarial examples, but those mostly rely on ascending the gradient of loss. In this paper, we introduce the novel logits optimized targeting system (LOTS) to directly manipulate deep features captured at the penultimate layer. Using LOTS, we analyze and compare the adversarial robustness of DNNs using the traditional Softmax layer with Openmax, which was designed to provide open set recognition by defining classes derived from deep representations, and is claimed to be more robust to adversarial perturbations. We demonstrate that Openmax provides less vulnerable systems than Softmax to traditional attacks, however, we show that it can be equally susceptible to more sophisticated adversarial generation techniques that directly work on deep representations.


title: Autonomous golf cars for public trial of mobility-on-demand service

abstract: We detail the design of autonomous golf cars which were used in public trials in Singapore's Chinese and Japanese Gardens, for the purpose of raising public awareness and gaining user acceptance of autonomous vehicles. The golf cars were designed to be robust, reliable, and safe, while operating under prolonged durations. Considerations that went in to the overall system design included the fact that any member of the public had to not only be able to easily use the system, but to also not have the option to use the system in an unintended manner. This paper details the hardware and software components of the golf cars with these considerations, and also how the booking system and mission planner facilitated users to book for a golf car from any of ten stations within the gardens. We show that the vehicles performed robustly throughout the prolonged operations with a small localization variance, and that users were very receptive from the user survey results.


title: The coming commoditization of processes

abstract: It's all very well to be king, compassionate, and charismatic. But the most crucial predictor of executive success has nothing to do with personality or style. It's brainpower. Here's how to find those people with the sheer intelligence to become business stars.


title: A worldwide flock of Condors: load sharing among workstation clusters

abstract: Abstract Condor is a distributed batch system for sharing the workload of compute-intensive jobs in a pool of unix workstations connected by a network. In such a Condor pool, idle machines are spotted by Condor and allocated to queued jobs, thus putting otherwise unutilized capacity to efficient use. When institutions owning Condor pools cooperate, they may wish to exploit the joint capacity of their pools in a similar way. So the need arises to extend the Condor load-sharing and protection mechanisms beyond the boundaries of Condor pools, or in other words, to create a flock of Condors. Such a flock may include Condor pools connected by local-area networks as well as by wide-area networks. In this paper we describe the design and implementation of a distributed, layered Condor flocking mechanism. The main concept in this design is the Gateway Machine that represents in each pool idle machines from other pools in the flock and allows job transfers across pool boundaries. Our flocking design is transparent to the workstation owners, to the users, and to Condor itself. We also discuss our experiences with an intercontinental Condor flock.


The hospitalist debate.


title: Unraveling the Effect of Textured Contact Lenses on Iris Recognition

abstract: The presence of a contact lens, particularly a textured cosmetic lens, poses a challenge to iris recognition as it obfuscates the natural iris patterns. The main contribution of this paper is to present an in-depth analysis of the effect of contact lenses on iris recognition. Two databases, namely, the IIIT-D Iris Contact Lens database and the ND-Contact Lens database, are prepared to analyze the variations caused due to contact lenses. We also present a novel lens detection algorithm that can be used to reduce the effect of contact lenses. The proposed approach outperforms other lens detection algorithms on the two databases and shows improved iris recognition performance.


title: Volume xx (200y), Number z, pp. 1–12 Surface Reconstruction Based on Hierarchical Floating Radial Basis Functions

abstract: In this paper we address the problem of optimal centre placement for scattered data approximation using radial basis functions (RBFs) by introducing the concept of floating centres. Given an initial least-squares solution, we optimize the positions and the weights of the RBF centres by minimizing a non-linear error function. By optimizing the centre positions, we obtain better approximations with a lower number of centres, which improves the numerical stability of the fitting procedure. We combine the non-linear RBF fitting with a hierarchical domain decomposition technique. This provides a powerful tool for surface reconstruction from oriented point samples. By directly incorporating point normal vectors into the optimization process, we avoid the use of off-surface points which results in less computational overhead and reduces undesired surface artefacts. We demonstrate that the proposed surface reconstruction technique is as robust as recent methods, which compute the indicator function of the solid described by the point samples. In contrast to indicator function based methods, our method computes a global distance field that can directly be used for shape registration.


title: Extracting code from programming tutorial videos

abstract: The number of programming tutorial videos on the web increases daily. Video hosting sites such as YouTube host millions of video lectures, with many programming tutorials for various languages and platforms. These videos contain a wealth of valuable information, including code that may be of interest. However, two main challenges have so far prevented the effective indexing of programming tutorial videos: (i) code in tutorials is typically written on-the-fly, with only parts of the code visible in each frame, and (ii) optical character recognition (OCR) is not precise enough to produce quality results from videos. We present a novel approach for extracting code from videos that is based on: (i) consolidating code across frames, and (ii) statistical language models for applying corrections at different levels, allowing us to make corrections by choosing the most likely token, combination of tokens that form a likely line structure, and combination of lines that lead to a likely code fragment in a particular language. We implemented our approach in a tool called ACE, and used it to extract code from 40 Android video tutorials on YouTube. Our evaluation shows that ACE extracts code with high accuracy, enabling deep indexing of video tutorials.


title: DeepFi: Deep learning for indoor fingerprinting using channel state information

abstract: With the fast growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted a lot of interest due to its high accuracy. In this paper, we present a novel deep learning based indoor fingerprinting system using Channel State Information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an off-line training phase and an on-line localization phase. In the off-line training phase, deep learning is utilized to train all the weights as fingerprints. Moreover, a greedy learning algorithm is used to train all the weights layer-by-layer to reduce complexity. In the on-line localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error compared with three existing methods in two representative indoor environments.


title: A robust lip tracking algorithm using localized color active contours and deformable models

abstract: Lip tracking is crucial to the success of a lipreading recognition system. This paper presents a robust lip tracking algorithm using localized color active contours and deformable models. The proposed approach utilizes a combined semi-ellipse as the initial evolving curve and computes the localized energies in color space for evolving such that a separation of the original lip image into lip and non-lip regions can be found. Moreover, the dynamic radius selection of the local region is presented with a 16-point deformable model (Wang et al. 2004) to achieve the lip tracking. The proposed approach is adaptive to the movement of the lips from frame to frame, and robust against the appearance of the teeth and tongue. Experiments have shown the promising results.


title: SAR Ship Detection and Self-Reporting Data Fusion Based on Traffic Knowledge

abstract: The improvement in Maritime Situational Awareness, the capability of understanding events, circumstances, and activities within and impacting the maritime environment, is nowadays of paramount importance for safety and security. The integration of spaceborne synthetic aperture radar (SAR) data and automatic identification system (AIS) information has the appealing potential to provide a better picture of what is happening at sea by detecting vessels that are not reporting their positioning data or, on the other side, by validating ships detected in satellite imagery. In this letter, we propose a novel architecture that is able to increase the quality of SAR/AIS fusion by exploiting knowledge of historical vessel positioning information. Experimental results are presented, testing the algorithm in the specific area of Dover Strait using real SAR and AIS data.


title: The Psychology of Compensatory and Retributive Justice

abstract: How do observers respond when the actions of one individual inflict harm on another? The primary reaction to carelessly inflicted harm is to seek restitution; the offender is judged to owe compensation to the harmed individual. The primary reaction to harm inflicted intentionally is moral outrage producing a desire for retribution; the harm-doer tust be punished. Reckless conduct, an intermediate case, provokes reactions that involve elements of both careless and intentional harm. The moral outrage felt by those who witness transgressions is a product of both cognitive interpretations of the event and emotional reactions to it. Theory about the exact nature of the emotional reactions is considered, along with suggestions for directions forfuture research.


title: Experiencing interactivity in public spaces (eips)

abstract: Mobile and ubiquitous systems create opportunities for new kinds of interactivity in public spaces. Examples of human-technology interactions in public spaces include interactive displays on different scales; mobile systems enabling projection in public environments; smart interactive and reactive objects; tangible interfaces; and public media arts. Human-system and mediated human-human interactions become public and visible to the people around the same space. This creates many possibilities and challenges for designing the user experience that arise primarily from the social and physical context. This workshop will bring together researchers, designers, practitioners and media artists to discuss elements and viewpoints of such new forms of experiences. The results of the workshop will be an "experience design space" and a research agenda for experiences with interactive systems used in public spaces.


title: MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking

abstract: In the recent past, the computer vision community has developed centralized benchmarks for the performance evaluation of a variety of tasks, including generic object and pedestrian detection, 3D reconstruction, optical flow, single-object short-term tracking, and stereo estimation. Despite potential pitfalls of such benchmarks, they have proved to be extremely helpful to advance the state of the art in the respective area. Interestingly, there has been rather limited work on the standardization of quantitative benchmarks for multiple target tracking. One of the few exceptions is the well-known PETS dataset, targeted primarily at surveillance applications. Despite being widely used, it is often applied inconsistently, for example involving using different subsets of the available data, different ways of training the models, or differing evaluation scripts. This paper describes our work toward a novel multiple object tracking benchmark aimed to address such issues. We discuss the challenges of creating such a framework, collecting existing and new data, gathering state-of-the-art methods to be tested on the datasets, and finally creating a unified evaluation system. With MOTChallenge we aim to pave the way toward a unified evaluation framework for a more meaningful quantification of multi-target tracking.


title: Feature-based facial expression recognition: Sensitivity analysis and experiments with a multilayer perceptron

abstract: In this paper, we report our experiments on feature-based facial expression recognition within an architecture based on a two-layer perceptron. We investigate the use of two types of features extracted from face images: the geometric positions of a set of fiducial points on a face, and a set of multiscale and multiorientation Gabor wavelet coefficients at these points. They can be used either independently or jointly. The recognition performance with different types of features has been compared, which shows that Gabor wavelet coefficients are much more powerful than geometric positions. Furthermore, since the first layer of the perceptron actually performs a nonlinear reduction of the dimensionality of the feature space, we have also studied the desired number of hidden units, i.e. the appropriate dimension to represent a facial expression in order to achieve a good recognition rate. It turns out that five to seven hidden units are probably enough to represent the space of facial expressions. Then, we have investigated the importance of each individual fiducial point to facial expression recognition. Sensitivity analysis reveals that points on cheeks and on forehead carry little useful information. After discarding them, not only the computational efficiency increases, but also the generalization performance slightly improves. Finally, we have studied the significance of image scales. Experiments show that facial expression recognition is mainly a low frequency process, and a spatial resolution of 64 pixels × 64 pixels is probably enough.


title: A Systems Approach to Achieving the Benefits of Artificial Intelligence in UK Defence

abstract: The ability to exploit the opportunities offered by AI within UK Defence calls for an understanding of systemic issues required to achieve an effective operational capability. This paper provides the authors' views of issues which currently block UK Defence from fully benefitting from AI technology. These are situated within a reference model for the AI Value Train, so enabling the community to address the exploitation of such data and software intensive systems in a systematic, end to end manner. The paper sets out the conditions for success including: Researching future solutions to known problems and clearly defined use cases; Addressing achievable use cases to show benefit; Enhancing the availability of Defence-relevant data; Enhancing Defence 'know how' in AI; Operating Software Intensive supply chain eco-systems at required breadth and pace; Governance and, the integration of software and platform supply chains and operating models.


title: Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels

abstract: It is challenging to train deep neural networks robustly on the industrial-level data, since labels of such data are heavily noisy, and their label generation processes are normally agnostic. To handle these issues, by using the memorization effects of deep neural networks, we may train deep neural networks on the whole dataset only the first few iterations. Then, we may employ early stopping or the small-loss trick to train them on selected instances. However, in such training procedures, deep neural networks inevitably memorize some noisy labels, which will degrade their generalization. In this paper, we propose a meta algorithm called Pumpout to overcome the problem of memorizing noisy labels. By using scaled stochastic gradient ascent, Pumpout actively squeezes out the negative effects of noisy labels from the training model, instead of passively forgetting these effects. We leverage Pumpout to upgrade two representative methods: MentorNet and Backward Correction. Empirical results on benchmark datasets demonstrate that Pumpout can significantly improve the robustness of representative methods.


title: How Powerful are Graph Neural Networks?

abstract: Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their representational properties and limitations. Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures. Our results characterize the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures. We then develop a simple architecture that is provably the most expressive among the class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism test. We empirically validate our theoretical findings on a number of graph classification benchmarks, and demonstrate that our model achieves state-of-the-art performance.


title: QuQn map: Qualitative-Quantitative mapping of scientific papers

abstract: Mathematical Expressions (ME) and words are carefully bonded in technical writing to characterize physical concepts and their interactions quantitatively, and qualitatively. This paper proposes the Qualitative-Quantitative (QuQn) map as an abstraction of scientific papers to depict the dependency among MEs and their most related adjacent words. QuQn map aims to offer a succinct representation of the reasoning logic flow in a paper. Various filters can be applied to a QuQn map to reduce redundant/indirect links, control the display of problem settings (simple ME variables with declaration), and prune nodes with specific topological properties such as the largest connected subgraph. We developed a visualization tool prototype to support interactive browsing of the technical contents at different granularities of detail.


title: Deep Convolution Neural Network Based Speech Recognition for Chhattisgarhi

abstract: The existing ASR for Chhattisgarhi using conventional machine learning technique was implemented for speaker dependent speech recognition. However, the conventional machine learning based speech recognition is incapable to handle the spectral variations as well as the spectral correlation of acoustic signals. Therefore, to overcome the aforementioned limitations, authors have implemented the deep convolution neural network (DCNN) based ASR for Chhattisgarhi dialect. Unlike other deep learning models, DCNN can efficiently handle the spectral variations and spectral correlation of speech signal with the less computational burden. The experiment of isolated Chhattisgarhi word recognition was implemented on self-recorded dataset acquired from 150 subjects from various geographical parts of Chhattisgarh state. The implemented algorithm is promisingly achieving 99.49% of accuracy for isolated word recognition. The different performance paraments are presented to validate the performed experiment.


title: A Simple Operator for Very Precise Estimation of Ellipses

abstract: This paper presents a simple linear operator that accurately estimates the position and parameters of ellipse features. Based on the dual conic model, the operator avoids the intermediate stage of precisely extracting individual edge points by exploiting directly the raw gradient information in the neighborhood of an ellipse's boundary. Moreover, under the dual representation, the dual conic can easily be constrained to a dual ellipse when minimizing the algebraic distance. The new operator is assessed and compared to other estimation approaches in simulation as well as in real situation experiments and shows better accuracy than the best approaches, including those limited to the center position.


title: Improving Event Extraction via Multimodal Integration

abstract: In this paper, we focus on improving Event Extraction (EE) by incorporating visual knowledge with words and phrases from text documents. We first discover visual patterns from large-scale text-image pairs in a weakly-supervised manner and then propose a multimodal event extraction algorithm where the event extractor is jointly trained with textual features and visual patterns. Extensive experimental results on benchmark data sets demonstrate that the proposed multimodal EE method can achieve significantly better performance on event extraction: absolute 7.1% F-score gain on event trigger labeling and 8.5% F-score gain on event argument labeling.


title: Comparison of European Olive Production Systems

abstract: (1) Background: Spain, Italy, and Greece are the world’s top olive oil producers. In recent decades, these countries have gradually diversified their farming system in the olive groves. The element of innovation with respect to the state of the art is that this paper aims to compare the environmental performance of different farming systems in a European context by performing a simplified Life Cycle Assessment; (2) Methods: Environmental performance was calculated according to the methodology of Life Cycle Assessment and the “Guidance for the implementation of the Product Environmental Footprint (PEF)”. Average data were considered in order to describe a system with a great degree of complexity and high spatial heterogeneity; (3) Results: The study highlights the difficulty of identifying the farming method that presents the best environmental performance in each of the impact categories considered. In Greece, the lowest use of diesel, electricity, and water brings about advantages for many impact categories, albeit with low yields. While the highest olive yield obtained in Italy has positive consequences in terms of global warming, the highest use of fertilisers, in many cases, entails higher environmental impacts. On the other hand, in Spain the highest use of organo-phosphorous pesticides entails the highest impacts of eco-toxicity; (4) Conclusion: the reduction of the use of fertilizers and pesticides, as well as water conservation, are important issues which require the optimization of timing and techniques in order to achieve environmental advantages.


title: A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives

abstract: The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers’ structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain.


title: Semantic-preserving word clouds by seam carving

abstract: Word clouds are proliferating on the Internet and have received much attention in visual analytics. Although word clouds can help users understand the major content of a document collection quickly, their ability to visually compare documents is limited. This paper introduces a new method to create semantic-preserving word clouds by leveraging tailored seam carving, a well-established content-aware image resizing operator. The method can optimize a word cloud layout by removing a left-to-right or top-to-bottom seam iteratively and gracefully from the layout. Each seam is a connected path of low energy regions determined by a Gaussian-based energy function. With seam carving, we can pack the word cloud compactly and effectively, while preserving its overall semantic structure. Furthermore, we design a set of interactive visualization techniques for the created word clouds to facilitate visual text analysis and comparison. Case studies are conducted to demonstrate the effectiveness and usefulness of our techniques.


title: Ontology-based user modeling for Knowledge Management Systems

abstract: This paper is presenting a generic ontology-based user modeling architecture, (OntobUM), applied in the context of a Knowledge Management System (KMS). Due to their powerful knowledge representation formalism and associated inference mechanisms, ontology-based systems are emerging as a natural choice for the next generation of KMSs operating in organizational, interorganizational as well as community contexts. User models, often addressed as user profiles, have been included in KMSs mainly as simple ways of capturing the user preferences and/or competencies. We extend this view by including other characteristics of the users relevant in the KM context and we explain the reason for doing this. The proposed user modeling system relies on a user ontology, using Semantic Web technologies, based on the IMS LIP specifications, and it is integrated in an ontology-based KMS called Ontologging. We are presenting a generic framework for implicit and explicit ontology-based user modeling.


title: Question Dependent Recurrent Entity Network for Question Answering

abstract: Question Answering is a task which requires building models capable of providing answers to questions expressed in human language. Full question answering involves some form of reasoning ability. We introduce a neural network architecture for this task, which is a form of $Memory\ Network$, that recognizes entities and their relations to answers through a focus attention mechanism. Our model is named $Question\ Dependent\ Recurrent\ Entity\ Network$ and extends $Recurrent\ Entity\ Network$ by exploiting aspects of the question during the memorization process. We validate the model on both synthetic and real datasets: the $bAbI$ question answering dataset and the $CNN\ \&\ Daily\ News$ $reading\ comprehension$ dataset. In our experiments, the models achieved a State-of-The-Art in the former and competitive results in the latter.


title: Real-Time Deep Hair Matting on Mobile Devices

abstract: Augmented reality is an emerging technology in many application domains. Among them is the beauty industry, where live virtual try-on of beauty products is of great importance. In this paper, we address the problem of live hair color augmentation. To achieve this goal, hair needs to be segmented quickly and accurately. We show how a modified MobileNet CNN architecture can be used to segment the hair in real-time. Instead of training this network using large amounts of accurate segmentation data, which is difficult to obtain, we use crowd sourced hair segmentation data. While such data is much simpler to obtain, the segmentations there are noisy and coarse. Despite this, we show how our system can produce accurate and fine-detailed hair mattes, while running at over 30 fps on an iPad Pro tablet.


title: Evaluation of a libutp-based Tor Datagram Implementation

abstract: Datagram designs are a promising approach to overcome Tor’s performance-related problems. Advantages of datagram designs over stream designs are that they allow better end-to-end congestion management, reduce queue lengths on nodes, and prevent cell loss on one circuit delaying cells on other circuits. In earlier reports we compared possible Tor datagram designs [1] and outlined a testing plan [2]. In this report we evaluate our implementation of a libutp-based Tor datagram design. However, this evaluation does not focus on performance improvements over the current streambased Tor implementation, because we found that our datagram-based Tor implementation is not mature enough for such a comparison. The focus is rather on our approach to integrate a datagram design into the Tor source code and on setting up test environments to evaluate the new design. We hope that our findings will be useful when further tweaking our libutp-based design or implementing other datagram designs. In the next section we describe our libutp-based Tor implementation in sufficient detail for others to understand our source code and to act as blueprint for implementing other datagram designs. Sections 3 to 5 outline the experimental setup that we used to evaluate our implementation and point out roadblocks that kept us busy longer than necessary. Section 6 concludes the report by sketching out possible next steps.


title: Using Light Polarization in Laser Scanning

abstract: We use polarization analysis in triangulation-based laser scanning to disambiguate the true laser stripe from spurious inter-reflections caused by holes and concavities on metal surfaces. Stripe candidates are discriminated by projecting linearly polarized laser light onto the objects, and measuring the polarization state of the reflected light at each pixel of the various candidates. We sketch a model for predicting the polarization state of the true stripe, and report several experimental results.


title: A parallel corpus of Python functions and documentation strings for automated code documentation and code generation

abstract: Automated documentation of programming source code and automated code generation from natural language are challenging tasks of both practical and scientific interest. Progress in these areas has been limited by the low availability of parallel corpora of code and natural language descriptions, which tend to be small and constrained to specific domains. In this work we introduce a large and diverse parallel corpus of a hundred thousands Python functions with their documentation strings ("docstrings") generated by scraping open source repositories on GitHub. We describe baseline results for the code documentation and code generation tasks obtained by neural machine translation. We also experiment with data augmentation techniques to further increase the amount of training data. We release our datasets and processing scripts in order to stimulate research in these areas.


title: Dynamics of Multiple-Seller and Multiple-Buyer Spectrum Trading in Cognitive Radio Networks: A Game-Theoretic Modeling Approach

abstract: We consider the problem of spectrum trading with multiple licensed users (i.e., primary users) selling spectrum opportunities to multiple unlicensed users (i.e., secondary users). The secondary users can adapt the spectrum buying behavior (i.e., evolve) by observing the variations in price and quality of spectrum offered by the different primary users or primary service providers. The primary users or primary service providers can adjust their behavior in selling the spectrum opportunities to secondary users to achieve the highest utility. In this paper, we model the evolution and the dynamic behavior of secondary users using the theory of evolutionary game. An algorithm for the implementation of the evolution process of a secondary user is also presented. To model the competition among the primary users, a noncooperative game is formulated where the Nash equilibrium is considered as the solution (in terms of size of offered spectrum to the secondary users and spectrum price). For a primary user, an iterative algorithm for strategy adaptation to achieve the solution is presented. The proposed game-theoretic framework for modeling the interactions among multiple primary users (or service providers) and multiple secondary users is used to investigate network dynamics under different system parameter settings and under system perturbation.


title: Teaching in 3D: Pedagogical Affordances and Constraints of 3D Virtual Worlds for Synchronous Distance Learning

abstract: Three-dimensional (3D) virtual worlds are a new technology that holds some promise as constructivist learning environments for distance education. This investigation presents an evaluative case study of the pedagogical implications of using one 3D virtual world, Active Worlds, for synchronous distance education. The research design for this qualitative study focuses on the pedagogical affordances and constraints. Methods employed in the data collection include participatory observations, class logs, and formal and informal interviews with the instructor of a synchronous distance learning course offered through Active Worlds University. Findings reveal that although Active Worlds provides tools that support constructivist learning environments, the affordances and constraints of the tools (discourse, experiential, and resource) may, to varying degrees, impact the pragmatic use of this medium. While this initial investigation reveals that this technology supports constructivist learning environments, more r...


title: Understanding convolutional neural networks using a minimal model for handwritten digit recognition

abstract: The contribution of this paper is to bridge the gap on understanding the mathematical structure and the computational implementation of a convolutional neural network (CNN) using a minimal model (Minimal CNN). The proposed minimal CNN is presented using a layering approach. This approach provides a concise and accessible understanding of the main mathematical operations of a CNN. Hence, it benefits beginners and non-mathematical prolific learners to acquire foundational knowledge that informs a principled understanding of CNNs without having an intimidating experience. A handwritten digit recognition using MNIST handwritten digit dataset is used to experiment the performance of the proposed minimal CNN with other neural networks.


title: A Comparative Evaluation of Log-Based Process Performance Analysis Techniques

abstract: Process mining has gained traction over the past decade and an impressive body of research has resulted in the introduction of a variety of process mining approaches measuring process performance. Having this set of techniques available, organizations might find it difficult to identify which approach is best suited considering context, performance indicator, and data availability. In light of this challenge, this paper aims at introducing a framework for categorizing and selecting performance analysis approaches based on existing research. We start from a systematic literature review for identifying the existing works discussing how to measure process performance based on information retrieved from event logs. Then, the proposed framework is built starting from the information retrieved from these studies taking into consideration different aspects of performance analysis.


A unified approach for projections onto the intersection of 𝓁 1 and 𝓁 2 balls or spheres.


title: Real-time estimation of drivable image area based on monocular vision

abstract: Camera-based estimation of drivable image areas is still in evolution. These systems have been developed for improved safety and convenience, without the need to adapt itself to the environment. Machine Vision is an important tool to identify the region that includes the road in images. Road detection is the major task of autonomous vehicle guidance. In this way, this work proposes a drivable region detection algorithm that generates the region of interest from a dynamic threshold search method and from a drag process (DP). Applying the DP to estimation of drivable image areas has not been done yet, making the concept unique. Our system was has been evaluated from real data obtained by intelligent platforms and tested in different types of image texture, which include occlusion case, obstacle detection and reactive navigation.


title: On the Speed Limits of Permanent-Magnet Machines

abstract: Permanent-magnet (PM) machines are considered the most suitable machine type for very high speed applications. Still, due to the growing demand for the ever higher rotational speeds, PM machines are approaching their limits. The focus of this paper is the different factors that lie behind the inherent speed limitations of PM machines. The limits-thermal, elastic, and rotor dynamical-are defined, classified, and correlated to basic machine parameters.


Thermal Characteristics of Overwintering Habitats for the Blanding ’ s Turtle ( Emydoidea blandingii ) Across Three Study Areas in Ontario , Canada


title: Rater reliability and construct validity of a mobile application for posture analysis

abstract: [Purpose] Measurement of posture is important for those with a clinical diagnosis as well as researchers aiming to understand the impact of faulty postures on the development of musculoskeletal disorders. A reliable, cost-effective and low tech posture measure may be beneficial for research and clinical applications. The purpose of this study was to determine rater reliability and construct validity of a posture screening mobile application in healthy young adults. [Subjects and Methods] Pictures of subjects were taken in three standing positions. Two raters independently digitized the static standing posture image twice. The app calculated posture variables, including sagittal and coronal plane translations and angulations. Intra- and inter-rater reliability were calculated using the appropriate ICC models for complete agreement. Construct validity was determined through comparison of known groups using repeated measures ANOVA. [Results] Intra-rater reliability ranged from 0.71 to 0.99. Inter-rater reliability was good to excellent for all translations. ICCs were stronger for translations versus angulations. The construct validity analysis found that the app was able to detect the change in the four variables selected. [Conclusion] The posture mobile application has demonstrated strong rater reliability and preliminary evidence of construct validity. This application may have utility in clinical and research settings.


title: From Marketing Mix to E-Marketing Mix: A Literature Overview and Classification

abstract: The marketing mix paradigm, in its famous version of the 4Ps, went all the way through the evolution of marketing theory: from the marketing concept, through relationship marketing, to digital economy, being object of discussion both in academic literature and managerial practice. If it is a fact that the 4Ps marketing mix is a milestone of marketing theory, it is also true that the evolution of business contexts has created, in many fields, the need to review the "controllable factors" which form the marketing mix. The digital business represents the more recent of the business contexts and the one with the greater needs of differentiation of the mix. Throughout this evolutionary process, researchers have always been divided between the "conservatives," who think the 4Ps paradigm is able to adapt to the environmental changes by including new elements inside each "P," and the "revisionists," who affirm that the 4Ps paradigm is obsolete and propose new paradigms. This paper aims to clarify these two different approaches to marketing mix evolution through a review of the main literature on e-marketing mix, focusing on the development of marketing mix theory for the digital context.


title: Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam

abstract: We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an index for cardiac autonomic activity) were subsequently quantified and compared to corresponding measurements using Food and Drug Administration-approved sensors. High degrees of agreement were achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine.


title: Adopting virtual private network for electronic commerce: An economic analysis

abstract: Purpose – To identify the content of virtual private network, the suitability of virtual private network for e‐commerce transactions, and the economics issue of virtual private network.Design/methodology/approach – A range of virtual private network concepts and technologies are identified and compared. The capability, suitability, and pros and cons of adopting virtual private network for electronic commerce are discussed. An economic analysis is used to compare the cost and benefit of adopting virtual private network in organizations.Findings – This paper provides information about virtual private network technology. It also indicates the advantages and disadvantages of adopting virtual private network for electronic commerce practices. The economic analysis provides a real example of technology adoption decision making. Strategic implications of adopting virtual private network are detected.Practical implications – The economic analysis on adopting virtual private network provides an example of informat...


title: Sleep studies and neurochemical correlates in panic disorder and agoraphobia

abstract: Abstract Although panic disorder is classified by the DSM III among the anxiety disorders, there is evidence from epidemiological and neurochemical studies that links panic to the affective disorders. In addition several of the effective pharmacological treatments suggest, as in the depressive disorders, serotonergic involvement. As part of the evaluation of patients who would take part in the Cross. National Collaborative Panic study, polysomnography, the dexametnasone suppression test and platelet imipramine binding and 5HT-uptake were scheduled. 44 patients who met DSM III criteria on the SCID interview schedule for Panic Disorder consented to enter the study. After being medication free for two weeks, these patients had three consecutive all night polyscmnogranis, followed by a dexamethasone suppression test and platelet study of imipramine binding and 5HT-uptake. In our studies we were unable to find for the panic group as a whole a correlation with reported parameters of sleep architecture in endogenous depression. Similarly the imipramine binding differentiated the panickers from the depressed group. Only the 5HT-uptake was similar in both groups. We also found a subgroup of panickers who resembled depressives in terms of the imipramine binding, 5HT-uptake platelet studies and REM latency. In the analysis of the imipramine and 5HT-uptake over the treatment period, we noted changes which suggest a role for serotonin, certainly in a subgroup of panickers and possibly for the group of panic disorder as a whole.


title: Neural Headline Generation with Sentence-wise Optimization

abstract: Recently, neural models have been proposed for headline generation by learning to map documents to headlines with recurrent neural networks. Nevertheless, as traditional neural network utilizes maximum likelihood estimation for parameter optimization, it essentially constrains the expected training objective within word level rather than sentence level. Moreover, the performance of model prediction significantly relies on training data distribution. To overcome these drawbacks, we employ minimum risk training strategy in this paper, which directly optimizes model parameters in sentence level with respect to evaluation metrics and leads to significant improvements for headline generation. Experiment results show that our models outperforms state-of-the-art systems on both English and Chinese headline generation tasks.


Recommendation System Based on Collaborative Filtering


title: Estimating Human Body Configurations using Shape Context Matching

abstract: The problem we consider in this paper is to take a single two-dimensional image containing a human body, locate the joint positions, and use these to estimate the body configuration and pose in three-dimensional space. The basic approach is to store a number of exemplar 2D views of the human body in a variety of different configurations and viewpoints with respect to the camera. On each of these stored views, the locations of the body joints (left elbow, right knee, etc.) are manually marked and labelled for future use. The test shape is then matched to each stored view, using the technique of shape context matching in conjunction with a kinematic chain-based deformation model. Assuming that there is a stored view sufficiently similar in configuration and pose, the correspondence process will succeed. The locations of the body joints are then transferred from the exemplar view to the test shape. Given the joint locations, the 3D body configuration and pose are then estimated. We can apply this technique to video by treating each frame independently - tracking just becomes repeated recognition! We present results on a variety of datasets.


title: 3DUI 2010 Contest Grand Prize Winners

abstract: The 2010 IEEE Symposium on 3D User Interfaces ran the symposium's first 3DUI Grand Prize, a contest for innovative, practical solutions to classic 3DUI problems. The authors describe the rationale for the first contest and give an analysis of all submissions. Each category's winners also discuss their solutions.


title: Theoretical Study of Factors Affecting Ball Velocity in Instep Soccer Kicking

abstract: The objective of this study was to investigate the factors affecting ball velocity at the final instant of the impact phase (t1) in full instep soccer kicking. Five experienced male university soccer players performed maximal full instep kicks for various foot impact points using a one-step approach. The kicking motions were captured two dimensionally by a high-speed camera at 2,500 fps. The theoretical equation of the ball velocity at t1 given in the article was derived based on the impact dynamics theory. The validity of the theoretical equation was verified by comparing the theoretical relationship between the impact point and the ball velocity with the experimental one. Using this theoretical equation, the relationship between the impact point and the ball velocity was simulated. The simulation results indicated that the ball velocity is more strongly affected by the foot velocity at the initial instant of the impact phase than by other factors. The simulation results also indicated that decreasing the ankle joint reaction force during ball impact shifts the impact point that produces the greatest ball velocity to the toe side and decreasing the ankle joint torque during ball impact shifts the impact point that produces the greatest ball velocity to the ankle side.