title: Secure Mobile Cloud Computing and Security Issues

abstract: The proliferation of mobile devices, coupled by the increase in their capabilities, have enabled the establishment of a rich mobile computing platform that can be utilized in conjunction with cloud services. In this chapter, we overview the latest mobile computing models and architectures focusing on their security properties. In particular, we study a wide range of threats against the availability, privacy and integrity of mobile cloud computing architectures in which the mobile devices and the cloud jointly perform computation. We then present defense mechanisms that ensure the security of mobile cloud computing architectures and their applications. Throughout the chapter, we identify potential threats as well as possible opportunities for defenses.


title: Arab Spring: from newspaper

abstract: Agent-based simulation models are an important methodology for explaining social behavior and forecasting social change. However, a major drawback to using such models is that they are difficult to instantiate for specific cases and so are rarely reused. We describe a text-mining network analytic approach for rapidly instantiating a model for predicting the tendency toward revolution and violence based on social and cultural characteristics of a large collection of actors. We illustrate our approach using an agent-based dynamic network framework, Construct, and newspaper data for the 16 countries associated with the Arab Spring. We assess the overall accuracy of the base model across independent runs for 20 different months during the Arab Spring, observing that although predictions led to several false positives, the model is able to predict revolution before it occurs in three of the four nations in which the government was successfully overthrown.


title: Validation and Use of SIMULINK Integrated, High Fidelity, Engine-In-Vehicle Simulation of the International Class VI Truck

abstract: This work presents the development, validation and use of a SIMULINK integrated vehicle system simulation composed of engine, driveline and vehicle dynamics modules. The engine model links the appropriate number of single-cylinder modules, featuring thermodynamic models of the in-cylinder processes with transient capabilities to ensure high fidelity predictions. A detailed fuel injection control module is also included. The engine is coupled to the driveline, which consists of the torque converter, transmission, differential and prop shaft and drive shafts. An enhanced version of the point mass model is used to account for vehicle dynamics in the longitudinal and heave directions. A vehicle speed controller replaces the operator and allows the feedforward simulation to follow a prescribed vehicle speed schedule. For the particular case reported here, the simulation is configured for the International 4700 series, Class VI, 4x2 delivery truck powered by a V8 turbocharged, intercooled diesel engine. The integrated vehicle simulation is validated against transient data measured on the proving ground. Comparisons of predicted and measured responses of engine and vehicle variables during vehicle acceleration from 0 to 60 mph and from 30 to 50 mph show very good agreement. The simulation is also used to study trade-offs involved in redesigning control strategies for improved performance of the vehicle system.


title: Preparing instructors for quality online instruction

abstract: With a growing number of courses offered online and degrees offered through the Internet, there is a considerable interest in online education, particularly as it relates to the quality of online instruction. The major concerns are centering on the following questions: What will be the new role for instructors in online education? How will students' learning outcomes be assured and improved in online learning environment? How will effective communication and interaction be established with students in the absence of face-to-face instruction? How will instructors motivate students to learn in the online learning environment? This paper will examine new challenges and barriers for online instructors, highlight major themes prevalent in the literature related to “quality control or assurance” in online education, and provide practical strategies for instructors to design and deliver effective online instruction. Recommendations will be made on how to prepare instructors for quality online instruction.


title: Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation

abstract: This paper presents a first attempt towards finding an abstractive compression generation system for a set of related sentences which jointly models sentence fusion and paraphrasing using continuous vector representations. Our paraphrastic fusion system improves the informativity and the grammaticality of the generated sentences. Our system can be applied to various real world applications such as text simplification, microblog, opinion and newswire summarization. We conduct our experiments on human generated multi-sentence compression datasets and evaluate our system on several newly proposed Machine Translation (MT) evaluation metrics. Our experiments demonstrate that our method brings significant improvements over the state of the art systems across different metrics.


title: Blockchain Based Distributed Control System for Edge Computing

abstract: Edge computing proposes a novel model for providing computational resources close to end devices that are connected to the network. It has numerous applications in Internet of Things, as well as smart grids, healthcare, smart home, etc. This paper presents ongoing research regarding the use of blockchain technology as a platform hierarchical and distributed control systems based on IEC 61499 standard. Hyperledger Fabric was selected as the blockchain solution, where function blocks are to be implemented as smart contracts on a supervisor level. The integration with the edge nodes that perform on the executive level responsible for actual process control is based on a micro-services architecture where Docker containers implement function blocks, and Kubernetes platform is used for orchestrating the execution of containers across the edge resources.


title: An Operating System for the Home

abstract: Network devices for the home such as remotely controllable locks, lights, thermostats, cameras, and motion sensors are now readily available and inexpensive. In theory, this enables scenarios like remotely monitoring cameras from a smartphone or customizing climate control based on occupancy patterns. However, in practice today, such smarthome scenarios are limited to expert hobbyists and the rich because of the high overhead of managing and extending current technology. ::: ::: We present HomeOS, a platform that bridges this gap by presenting users and developers with a PC-like abstraction for technology in the home. It presents network devices as peripherals with abstract interfaces, enables cross-device tasks via applications written against these interfaces, and gives users a management interface designed for the home environment. HomeOS already has tens of applications and supports a wide range of devices. It has been running in 12 real homes for 4-8 months, and 42 students have built new applications and added support for additional devices independent of our efforts.


title: Twitter sentiment analysis

abstract: Social media have received more attention nowadays. Public and private opinion about a wide variety of subjects are expressed and spread continually via numerous social media. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers' perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers' perceptions. This paper reports on the design of a sentiment analysis, extracting a vast amount of tweets. Prototyping is used in this development. Results classify customers' perspective via tweets into positive and negative, which is represented in a pie chart and html page. However, the program has planned to develop on a web application system, but due to limitation of Django which can be worked on a Linux server or LAMP, for further this approach need to be done.


title: Tactical Cloudlets: Moving Cloud Computing to the Edge

abstract: Soldiers and front-line personnel operating in tactical environments increasingly make use of handheld devices to help with tasks such as face recognition, language translation, decision-making, and mission planning. These resource constrained edge environments are characterized by dynamic context, limited computing resources, high levels of stress, and intermittent network connectivity. Cyber-foraging is the leverage of external resource-rich surrogates to augment the capabilities of resource-limited devices. In cloudlet-based cyber-foraging, resource-intensive computation and data is offloaded to cloudlets. Forward-deployed, discoverable, virtual-machine-based tactical cloudlets can be hosted on vehicles or other platforms to provide infrastructure to offload computation, provide forward data staging for a mission, perform data filtering to remove unnecessary data from streams intended for dismounted users, and serve as collection points for data heading for enterprise repositories. This paper describes tactical cloudlets and presents experimentation results for five different cloudlet provisioning mechanisms. The goal is to demonstrate that cyber-foraging in tactical environments is possible by moving cloud computing concepts and technologies closer to the edge so that tactical cloudlets, even if disconnected from the enterprise, can provide capabilities that can lead to enhanced situational awareness and decision making at the edge.


title: Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses: Implications for Clinical Trials and Clinical Practice

abstract: The placebo effect has often been considered a nuisance in basic and particularly clinical research. This view has gradually changed in recent years due to deeper insight into the neuro-bio-behavioral mechanisms steering both the placebo and nocebo responses, the evil twin of placebo. For the neuroscientist, placebo and nocebo responses have evolved as indispensable tools to understand brain mechanisms that link cognitive and emotional factors with symptom perception as well as peripheral physiologic systems and end organ functioning. For the clinical investigator, better understanding of the mechanisms driving placebo and nocebo responses allow the control of these responses and thereby help to more precisely define the efficacy of a specific pharmacological intervention. Finally, in the clinical context, the systematic exploitation of these mechanisms will help to maximize placebo responses and minimize nocebo responses for the patient's benefit. In this review, we summarize and critically examine the neuro-bio-behavioral mechanisms underlying placebo and nocebo responses that are currently known in terms of different diseases and physiologic systems. We subsequently elaborate on the consequences of this knowledge for pharmacological treatments of patients and the implications for pharmacological research, the training of healthcare professionals, and for the health care system and future research strategies on placebo and nocebo responses.


title: A dynamically growing self-organizing tree (DGSOT) for hierarchical clustering gene expression profiles

abstract: Motivation: The increasing use of microarray technologies is generating large amounts of data that must be processed in order to extract useful and rational fundamental patterns of gene expression. Hierarchical clustering technology is one method used to analyze gene expression data, but traditional hierarchical clustering algorithms suffer from several drawbacks (e.g. fixed topology structure; mis-clustered data which cannot be reevaluated). In this paper, we introduce a new hierarchical clustering algorithm that overcomes some of these drawbacks. ::: ::: Result: We propose a new tree-structure self-organizing neural network, called dynamically growing self-organizing tree (DGSOT) algorithm for hierarchical clustering. The DGSOT constructs a hierarchy from top to bottom by division. At each hierarchical level, the DGSOT optimizes the number of clusters, from which the proper hierarchical structure of the underlying dataset can be found. In addition, we propose a new cluster validation criterion based on the geometric property of the Voronoi partition of the dataset in order to find the proper number of clusters at each hierarchical level. This criterion uses the Minimum Spanning Tree (MST) concept of graph theory and is computationally inexpensive for large datasets. A K-level up distribution (KLD) mechanism, which increases the scope of data distribution in the hierarchy construction, was used to improve the clustering accuracy. The KLD mechanism allows the data misclustered in the early stages to be reevaluated at a later stage and increases the accuracy of the final clustering result. The clustering result of the DGSOT is easily displayed as a dendrogram for visualization. Based on a yeast cell cycle microarray expression dataset, we found that our algorithm extracts gene expression patterns at different levels. Furthermore, the biological functionality enrichment in the clusters is considerably high and the hierarchical structure of the clusters is more reasonable. ::: ::: Availability: DGSOT is available upon request from the authors.


title: Trust and mistrust in organizations: An exploration using an organizational justice framework.

abstract: This article commences with an overview of trust and mistrust, focusing on the debate about whether these are two ends of a continuum or distinct but interrelated concepts. Building on this review, the relationship between employees' perceptions of organizational justice and their self-categorized feelings of trust and mistrust is considered. It is suggested that organizational justice offers a useful means through which to explain and understand employees' feelings of trust and mistrust. Using case study data drawn from a UK public sector organization, the relationship between employees' feelings of trust and mistrust is explored within a change context. The data suggest that, whereas some employees perceive trust and mistrust as two ends of a continuum, others see them as distinct concepts. These findings are conceptualized as a trust - mistrust - absence triangle. Drawing on organizational justice as an explanatory theory, reasons for these findings are offered. The article concludes with a discussion ...


title: Depth Selective Camera: A Direct, On-Chip, Programmable Technique for Depth Selectivity in Photography

abstract: Time of flight (ToF) cameras use a temporally modulated light source and measure correlation between the reflected light and a sensor modulation pattern, in order to infer scene depth. In this paper, we show that such correlational sensors can also be used to selectively accept or reject light rays from certain scene depths. The basic idea is to carefully select illumination and sensor modulation patterns such that the correlation is non-zero only in the selected depth range -- thus light reflected from objects outside this depth range do not affect the correlational measurements. We demonstrate a prototype depth-selective camera and highlight two potential applications: imaging through scattering media and virtual blue screening. This depthselectivity can be used to reject back-scattering and reflection from media in front of the subjects of interest, thereby significantly enhancing the ability to image through scattering media-critical for applications such as car navigation in fog and rain. Similarly, such depth selectivity can also be utilized as a virtual blue-screen in cinematography by rejecting light reflecting from background, while selectively retaining light contributions from the foreground subject.


A Semantic Approach to Automated Text Sentiment Analysis


title: Argumentation in AI and Law: Editors' Introduction

abstract: 1. Overview Argument is central to law: legal disputes arise out of a disagreement between two parties and, since the disappearance of trials by ordeal and combat, such disputes are resolved by the parties to the dispute presenting arguments for their position to an agreed arbiter, who will typically justify the choice of the arguments he accepts with an argument of his own, intended to convince superior courts and the public at large. Given the centrality of argument to law, it is unsurprising that AI systems intended to model legal reasoning have found it necessary to model argument. This volume contains a collection of papers representing some of the very latest work on argumentation in AI and Law. The papers derive from a workshop run in conjunction with the Tenth International Conference on AI and Law, held in Bologna in June 2005. The papers have since been significantly extended and revised for publication here. In this introduction we will try to provide some of the context in which this work was done. We will not pretend to give a complete survey, but rather to introduce the key concerns and issues relating to argumentation which have arisen in previous work in AI and Law. Major topics which have emerged as important in AI and Law and argumentation include: – Arguing on the basis of precedent cases – Using argumentation to resolve rule conflicts and explore defeasibility – Dialogue and dialectics – Argument Schemes – Determining whether an attack on an argument is successful We will briefly consider each of these in turn.


title: The evolution of human skin coloration

abstract: Abstract Skin color is one of the most conspicuous ways in which humans vary and has been widely used to define human races. Here we present new evidence indicating that variations in skin color are adaptive, and are related to the regulation of ultraviolet (UV) radiation penetration in the integument and its direct and indirect effects on fitness. Using remotely sensed data on UV radiation levels, hypotheses concerning the distribution of the skin colors of indigenous peoples relative to UV levels were tested quantitatively in this study for the first time. The major results of this study are: (1) skin reflectance is strongly correlated with absolute latitude and UV radiation levels. The highest correlation between skin reflectance and UV levels was observed at 545 nm, near the absorption maximum for oxyhemoglobin, suggesting that the main role of melanin pigmentation in humans is regulation of the effects of UV radiation on the contents of cutaneous blood vessels located in the dermis. (2) Predicted skin reflectances deviated little from observed values. (3) In all populations for which skin reflectance data were available for males and females, females were found to be lighter skinned than males. (4) The clinal gradation of skin coloration observed among indigenous peoples is correlated with UV radiation levels and represents a compromise solution to the conflicting physiological requirements of photoprotection and vitamin D synthesis. The earliest members of the hominid lineage probably had a mostly unpigmented or lightly pigmented integument covered with dark black hair, similar to that of the modern chimpanzee. The evolution of a naked, darkly pigmented integument occurred early in the evolution of the genus Homo . A dark epidermis protected sweat glands from UV-induced injury, thus insuring the integrity of somatic thermoregulation. Of greater significance to individual reproductive success was that highly melanized skin protected against UV-induced photolysis of folate (Branda & Eaton, 1978, Science 201, 625–626; Jablonski, 1992, Proc. Australas. Soc. Hum. Biol. 5, 455–462, 1999, Med. Hypotheses 52, 581–582), a metabolite essential for normal development of the embryonic neural tube (Bower & Stanley, 1989, The Medical Journal of Australia 150, 613–619; Medical Research Council Vitamin Research Group, 1991, The Lancet 338, 31–37) and spermatogenesis (Cosentino et al ., 1990, Proc. Natn. Acad. Sci. U.S.A. 87, 1431–1435; Mathur et al ., 1977, Fertility Sterility 28, 1356–1360). As hominids migrated outside of the tropics, varying degrees of depigmentation evolved in order to permit UVB-induced synthesis of previtamin D 3 . The lighter color of female skin may be required to permit synthesis of the relatively higher amounts of vitamin D 3 necessary during pregnancy and lactation. Skin coloration in humans is adaptive and labile. Skin pigmentation levels have changed more than once in human evolution. Because of this, skin coloration is of no value in determining phylogenetic relationships among modern human groups.


title: Audit and Analysis of Impostors: An experimental approach to detect fake profile in online social network

abstract: In the present generation, the social life of every person has become associated with online social networks (OSN). These sites have made drastic changes in the way we socialize. Making friends and keeping in contact with them as well as being updated of their activities, has become easier. But with their rapid growth, problems like fake profiles, online impersonation have also increased. The risk lies in the fact that anybody can create a profile to impersonate a real person on the OSN. The fake profile could be exploited to build online relationship with a targeted person purely through online interactions with the friends of victim. In present work, we have proposed experimental framework with which detection of fake profile is feasible within the friend list, however this framework is restricted to a specific online social networking site namely Facebook. This framework extracts data from the friend list and uses it to classify them as real or fake by using unsupervised and supervised machine learning.


title: Discrete Gaussian Sampling Reduces to CVP and SVP

abstract: The discrete Gaussian $D_{L- t, s}$ is the distribution that assigns to each vector $x$ in a shifted lattice $L - t$ probability proportional to $e^{-\pi \|x\|^2/s^2}$. It has long been an important tool in the study of lattices. More recently, algorithms for discrete Gaussian sampling (DGS) have found many applications in computer science. In particular, polynomial-time algorithms for DGS with very high parameters $s$ have found many uses in cryptography and in reductions between lattice problems. And, in the past year, Aggarwal, Dadush, Regev, and Stephens-Davidowitz showed $2^{n+o(n)}$-time algorithms for DGS with a much wider range of parameters and used them to obtain the current fastest known algorithms for the two most important lattice problems, the Shortest Vector Problem (SVP) and the Closest Vector Problem (CVP). Motivated by its increasing importance, we investigate the complexity of DGS itself and its relationship to CVP and SVP. Our first result is a polynomial-time dimension-preserving reduction from DGS to CVP. There is a simple reduction from CVP to DGS, so this shows that DGS is equivalent to CVP. Our second result, which we find to be more surprising, is a polynomial-time dimension-preserving reduction from centered DGS (the important special case when $ t = 0$) to SVP. In the other direction, there is a simple reduction from $\gamma$-approximate SVP for any $\gamma = \Omega(\sqrt{n/\log n})$, and we present some (relatively weak) evidence to suggest that this might be the best achievable approximation factor. We also show that our CVP result extends to a much wider class of distributions and even to other norms.


title: Performance Comparison between Five NoSQL Databases

abstract: Recently NoSQL databases and their related technologies are developing rapidly and are widely applied in many scenarios with their BASE (Basic Availability, Soft state, Eventual consistency) features. At present, there are more than 225 kinds of NoSQL databases. However, the overwhelming amount and constantly updated versions of databases make it challenging for people to compare their performance and choose an appropriate one. This paper is trying to evaluate the performance of five NoSQL clusters (Redis, MongoDB, Couchbase, Cassandra, HBase) by using a measurement tool – YCSB (Yahoo! Cloud Serving Benchmark), explain the experimental results by analyzing each database's data model and mechanism, and provide advice to NoSQL developers and users.


title: Desktop vs. mobile: A comparative study of augmented reality systems for engineering visualizations in education

abstract: This paper describes the results of a comparative pilot study of three different approaches to deliver three-dimensional content in an Engineering Design Graphics textbook using Augmented Reality: Desktop Augmented Reality, Mobile Augmented Reality, and an interactive 3D viewer. This work builds on a previous version of our augmented book, which only implements Desktop Augmented Reality. In this extended version, two new visualization mechanisms for mobiles devices were incorporated with the purpose of improving the students' visualization skills. A small group of users was selected to evaluate the usability of the technology and the effectiveness of the materials. In general, participants showed mixed reactions in terms of their preferred method to interact with the content. Nevertheless, we observed a general positive attitude, excitement, and a high level of user satisfaction, which suggests that augmented reality can be a valuable method for self-directed learning and self-assessment.


title: Online engagement factors on Facebook brand pages

abstract: Social networks have become an additional marketing channel that could be integrated with the traditional ones as a part of the marketing mix. The change in the dynamics of the marketing interchange between companies and consumers as introduced by social networks has placed a focus on the non-transactional customer behavior. In this new marketing era, the terms engagement and participation became the central non-transactional constructs, used to describe the nature of participants’ specific interactions and/or interactive experiences. These changes imposed challenges to the traditional one-way marketing, resulting in companies experimenting with many different approaches, thus shaping a successful social media approach based on the trial-and-error experiences. To provide insights to practitioners willing to utilize social networks for marketing purposes, our study analyzes the influencing factors in terms of characteristics of the content communicated by the company, such as media type, content type, posting day and time, over the level of online customer engagement measured by number of likes, comments and shares, and interaction duration for the domain of a Facebook brand page. Our results show that there is a different effect of the analyzed factors over individual engagement measures. We discuss the implications of our findings for social media marketing.


title: Computing With Words in fuzzy cognitive maps

abstract: We extend our previously proposed new structure for fuzzy cognitive maps (FCM) using the weighted power mean (WPM) operator to the domain of Computing With Words (CWW), where all node activations and link strengths are described using interval type-2 fuzzy membership functions which represent words. This FCM structure is both intuitive and easier to specify than crisp FCM structures, as it allows human experts to account for the imprecision that is typical of their judgments of the network parameters. We describe a computationally feasible approach to implementing this new FCM, and provide examples illustrating its behavior, including a comparison of its converged word states with the converged states of the corresponding scalar version.


title: Document Language Models, Query Models, and Risk Minimization for Information Retrieval

abstract: We present a framework for information retrieval that combines document models and query models using a probabilistic ranking function based on Bayesian decision theory. The framework suggests an operational retrieval model that extends recent developments in the language modeling approach to information retrieval. A language model for each document is estimated, as well as a language model for each query, and the retrieval problem is cast in terms of risk minimization. The query language model can be exploited to model user preferences, the context of a query, synonomy and word senses. While recent work has incorporated word translation models for this purpose, we introduce a new method using Markov chains defined on a set of documents to estimate the query models. The Markov chain method has connections to algorithms from link analysis and social networks. The new approach is evaluated on TREC collections and compared to the basic language modeling approach and vector space models together with query expansion using Rocchio. Significant improvements are obtained over standard query expansion methods for strong baseline TF-IDF systems, with the greatest improvements attained for short queries on Web data.


Author ' s personal copy Narcissism and recollections of early life experiences


title: A Novel Distributed SDN-Secured Architecture for the IoT

abstract: Due to their rapid evolution, mobile devices demand for more dynamic and flexible networking services. A major challenges of future mobile networks is the increased mobile traffic. With the recent upcoming technologies of network programmability like Software-Defined Network (SDN), it may be integrated to create a new communication platform for Internet of Things (IoT). In this work, we present how to determine the effectiveness of an approach to build a new secured network architecture based on SDN and clusters. Our proposed scheme is a starting point for some experiments providing perspective over SDN deployment in a cluster environment. With this aim in mind, we suggest a routing protocol that manages routing tasks over Cluster-SDN. By using network virtualization and OpenFlow technologies to generate virtual nodes, we simulate a prototype system controlled by SDN. With our testbed, we are able to manage 500 things. We can analyze every OpenFlow messages and we have discovered that with a particular flow, the things can exchange information unlike the routing principle.


title: Community design of DLESE's collections review policy: a technological frames analysis

abstract: In this paper, I describe the design of a collection review policy for the Digital Library for Earth System Education (DLESE). A distinctive feature of DLESE as a digital library is the “DLESE community”, composed of voluntary members who contribute metadata and resource reviews to DLESE. As the DLESE community is open, the question of how to evaluate community contributions is a crucial part of the review policy design process. In this paper, technological frames theory is used to analyse this design process by looking at how the designers work with two differing definitions of the “peer reviewer”, (a) peer reviewer as arbiter or editor, and (b) peer reviewer as colleague. Content analysis of DLESE documents shows that these frames can in turn be related to two definitions that DLESE offers of itself: DLESE as a library, and DLESE as a digital artifact. The implications of the presence of divergent technological frames for the design process are summarised, and some suggestions for future research are outlined.


title: An update on the physiology of two- and three-toed sloths

abstract: Physiological and pharmacological research undertaken on sloths during the past 30 years is comprehensively reviewed. This includes the numerous studies carried out upon the respiratory and cardiovascular systems, anesthesia, blood chemistry, neuromuscular responses, the brain and spinal cord, vision, sleeping and waking, water balance and kidney function and reproduction. Similarities and differences between the physiology of sloths and that of other mammals are discussed in detail.


- Based Twitter User Geolocation Prediction


title: Deep Inception Generative Network for Cognitive Image Inpainting

abstract: Recent advances in deep learning have shown exciting promise in filling large holes and lead to another orientation for image inpainting. However, existing learning-based methods often create artifacts and fallacious textures because of insufficient cognition understanding. Previous generative networks are limited with single receptive type and give up pooling in consideration of detail sharpness. Human cognition is constant regardless of the target attribute. As multiple receptive fields improve the ability of abstract image characterization and pooling can keep feature invariant, specifically, deep inception learning is adopted to promote high-level feature representation and enhance model learning capacity for local patches. Moreover, approaches for generating diverse mask images are introduced and a random mask dataset is created. We benchmark our methods on ImageNet, Places2 dataset, and CelebA-HQ. Experiments for regular, irregular, and custom regions completion are all performed and free-style image inpainting is also presented. Quantitative comparisons with previous state-of-the-art methods show that ours obtain much more natural image completions.


title: Fast and flexible: Parallel packet processing with GPUs and click

abstract: We introduce Snap, a framework for packet processing that outperforms traditional software routers by exploiting the parallelism available on modern GPUs. While obtaining high performance, it remains extremely flexible, with packet processing tasks implemented as simple modular elements that are composed to build fully functional routers and switches. Snap is based on the Click modular router, which it extends by adding new architectural features that support batched packet processing, memory structures optimized for offloading to coprocessors, and asynchronous scheduling with in-order completion. We show that Snap can run complex pipelines at high speeds on commodity PC hardware by building an IP router incorporating both an IDS-like full-packet string matcher and an SDN-like packet classifier. In this configuration, Snap is able to forward 40 million packets per second, saturating four 10 Gbps NICs at packet sizes as small as 128 byes. This represents an increase in throughput of nearly 4x over the baseline Click running comparable elements on the CPU.


title: Blur image detection using Laplacian operator and Open-CV

abstract: With the increased usage of digital cameras and picture clicking devices, the number of digital images increases rapidly, which in return demand for image quality assessment in terms of blur. Based on the edge type and sharpness analysis using Laplacian operator, an effective representation of blur image detection scheme is proposed in this paper, which can determine that whether the image is blurred or not, and what is the extent of blur through Variance of Laplacian. In this project a simple, reliable and fast algorithm for image noise estimation is presented. Images taken for the testing is assumed to be corrupted by additive zero mean Gaussian noise. To exclude structures or details from contributive to the noise variance estimation, an easy edge detection algorithm inculcating first-order gradients is applied first. Then a Laplacian operator followed by an averaging over the whole image will provide very accurate noise variance estimation. There is only one parameter which is self-determined and adaptive to the image contents. Simulation results show that the proposed algorithm performs well for different types of images over a large range of noise variances. Performance comparisons against alternative approaches are also provided. Results obtain through screenshots of the experiments demonstrate the effectiveness of the proposed scheme.


title: History of Cannabis and Its Preparations in Saga, Science, and Sobriquet

abstract: Cannabis sativa L. is possibly one of the oldest plants cultivated by man, but has remained a source of controversy throughout its history. Whether pariah or panacea, this most versatile botanical has provided a mirror to medicine and has pointed the way in the last two decades toward a host of medical challenges from analgesia to weight loss through the discovery of its myriad biochemical attributes and the endocannabinoid system wherein many of its components operate. This study surveys the history of cannabis, its genetics and preparations. A review of cannabis usage in Ancient Egypt will serve as an archetype, while examining first mentions from various Old World cultures and their pertinence for contemporary scientific investigation. Cannabis historians of the past have provided promising clues to potential treatments for a wide array of currently puzzling medical syndromes including chronic pain, spasticity, cancer, seizure disorders, nausea, anorexia, and infectious disease that remain challenges for 21st century medicine. Information gleaned from the history of cannabis administration in its various forms may provide useful points of departure for research into novel delivery techniques and standardization of cannabis-based medicines that will allow their prescription for treatment of these intractable medical conditions.


Ontology Learning Process as a Bottom-up Strategy for Building Domain-specific Ontology from Legal Texts.


The Simulated Annealing Group Assignment (SAGA) Application


title: Concept-Based Relevance Models for Medical and Semantic Information Retrieval

abstract: Relevance models provide an important approach for estimating probabilities of words in the relevant class. However, the associated bag-of-words assumption breaks dependencies between words, especially between those within a phrase. If such dependencies could be preserved, it would permit matching the query terms with document terms having the same dependencies. Additionally, during the estimation of relevance, relevance models are unable to distinguish relevant and non-relevant information in a feedback document, and hence take the entire document into account, which potentially hurts the accuracy of estimation. In this paper, we define the notion of "concept", and design a concept-based information retrieval framework. Using this framework, we transform documents and queries from term space into concept space, and propose a concept-based relevance model for improved estimation of relevance. Our approach has three advantages. First, this approach only assumes independence between concepts, so is able to keep the strong dependencies between the words of a concept. Second, it unifies synonyms or different surface forms of a concept, leading to reduced dimensionality of the space, increased sample size of a concept, and consequently more accurate and reliable estimates of the relevance. Third, when knowledge bases are available, our approach enables the semantic analysis of query concepts, and thus identifies concepts related to the query, from which a more accurate distribution of relevance can be estimated. This work is aligned with semantic search methods. We apply our concept-based relevance model to information retrieval in the medical domain, where concepts are abundant and their variations are numerous. We compare with relevance models, BM25 with pseudo relevance feedback, and the state of the art conceptual language models, on several data collections. The proposed model demonstrates consistent and statistically significant improvements across collections, outperforming top benchmark conceptual language models by at least 9% and up to 20% on a number of metrics.


title: Unsupervised approach for shallow domain ontology construction from corpus

abstract: In this work we propose an unsupervised approach to construct a domain-specific ontology from corpus. It is essential for Information Retrieval systems to identify important domain concepts and relationships between them. We identify important domain terms of which multi-words form an important component. Our approach identifies 40% of the domain terms, compared to 22% identified by WordNet on manually annotated smartphone data. We propose an approach to construct a shallow ontology from discovered domain terms by identifying four domain relations namely, Synonyms ('similar-to'), Type-Of ('is-a'), Action-On ('methods') and Feature-Of ('attributes'), where we achieve an F-Score of 49.14%, 65.5%, 65% and 80% respectively


title: CoMICon: A Co-Operative Management System for Docker Container Images

abstract: Docker containers are becoming an attractive implementation choice for next-generation microservices-based applications. When provisioning such an application, container (microservice) instances need to be created from individual container images. Starting a container on a node, where images are locally available, is fast but it may not guarantee the quality of service due to insufficient resources. When a collection of nodes are available, one can select a node with sufficient resources. However, if the selected node does not have the required image, downloading the image from a different registry increases the provisioning time. Motivated by these observations, in this paper, we present CoMICon, a system for co-operative management of Docker images among a set of nodes. The key features of CoMICon are: (1) it enables a co-operative registry among a set of nodes, (2) it can store or delete images partially in the form of layers, (3) it facilitates the transfer of image layers between registries, and (4) it enables distributed pull of an image while starting a container. Using these features, we describe—(i) high availability management of images and (ii) provisioning management of distributed microservices based applications. We extensively evaluate the performance of CoMICon using 142 real, publicly available images from Docker hub. In contrast to state-of-the-art full image based approach, CoMICon can increase the number of highly available images up to 3x while reducing the application provisioning time by 28% on average.


title: Craniopagus parasiticus. Everard Home's Two-Headed Boy of Bengal and some other cases.

abstract: Craniopagus parasiticus, or épicome, is a rare teratological type, of which only six cases have been recorded in the medical literature. It differs from craniopagus conjoined twins in that the body and limbs of the parasitic twin are underdeveloped, leaving in some cases only a parasitic head, inserted on the crown of the autositic twin. The first case of this malformation was Everard Home's famous Twin-Headed Boy of Bengal, whose skull is preserved at the Hunterian Museum. In this historical review, Home's case is presented in some detail, and the later cases are used to explain further some of its particulars.


title: Serine-threonine kinases and transcription factors active in signal transduction are detected at high levels of phosphorylation during mitosis in preimplantation embryos and trophoblast stem cells.

abstract: Serine-threonine kinases and transcription factors play important roles in the G1-S phase progression of the cell cycle. Assays that use quantitative fluorescence by immunocytochemical means, or that measure band strength during Western blot analysis, may have confused interpretations if the intention is to measure G1-S phase commitment of a small subpopulation of phosphorylated proteins, when a larger conversion of the same population of proteins can occur during late G2 and M phases. In mouse trophoblast stem cells (TSC), a human placental cell line (HTR), and/or mouse preimplantation embryos, 8/19 serine-threonine and tyrosine kinases, 3/8 transcription factors, and 8/14 phospho substrate and miscellaneous proteins were phosphorylated at higher levels in M phase than in interphase. Most phosphoproteins appeared to associate with the spindle complex during M phase, but one (p38MAPK) associated with the spindle pole and five (Cdx2, MEK1, 2, p27, and RSK1) associated with the DNA. Phosphorylation was detected throughout apparent metaphase, anaphase and telophase for some proteins, or for only one of these segments for others. The phosphorylation was from 2.1- to 6.2-fold higher during M phase compared with interphase. These data suggest that, when planning and interpreting quantitative data and perturbation experiments, consideration must be given to the role of serine-threonine kinases and transcription factors during decision making in M phase as well as in G1-S phase.


title: The value of B2B relationships

abstract: Purpose – The purpose of the paper is to contribute to the knowledge of how relationship value, trust, commitment, satisfaction and loyalty intentions are defined and relate to each other. It explores these relationships in the business‐to‐business (B2B) context by analysing manufacturing companies regarding to their main supplier.Design/methodology/approach – After the literature review and several in‐depth interviews, a method of empirical analysis consisting of quantitative intervention with an ad hoc survey using a structured questionnaire has been developed. Structural equations modeling is used to contrast the hypotheses on the links between the constructs analysed.Findings – Confirmatory factor analysis provided satisfactory results. With regard to the direct effects of the relationship value, the three relationships being considered were verified: relationship value has a positive influence on trust, commitment and satisfaction towards the supplier. Also, and as already contrasted in previous stud...


title: PageRank beyond the Web

abstract: Google's PageRank method was developed to evaluate the importance of web-pages via their link structure. The mathematics of PageRank, however, are entirely general and apply to any graph or network in any domain. Thus, PageRank is now regularly used in bibliometrics, social and information network analysis, and for link prediction and recommendation. It's even used for systems analysis of road networks, as well as biology, chemistry, neuroscience, and physics. We'll see the mathematics and ideas that unite these diverse applications.


title: Analysis of food images: Features and classification

abstract: In this paper we investigate features and their combinations for food image analysis and a classification approach based on k-nearest neighbors and vocabulary trees. The system is evaluated on a food image dataset consisting of 1453 images of eating occasions in 42 food categories which were acquired by 45 participants in natural eating conditions. The same image dataset is used to test the classification system proposed in the previously reported work [1]. Experimental results indicate that using our combination of features and vocabulary trees for classification improves the food classification performance about 22% for the Top 1 classification accuracy and 10% for the Top 4 classification accuracy.


title: Measuring and fingerprinting click-spam in ad networks

abstract: Advertising plays a vital role in supporting free websites and smartphone apps. Click-spam, i.e., fraudulent or invalid clicks on online ads where the user has no actual interest in the advertiser's site, results in advertising revenue being misappropriated by click-spammers. While ad networks take active measures to block click-spam today, the effectiveness of these measures is largely unknown. Moreover, advertisers and third parties have no way of independently estimating or defending against click-spam. ::: In this paper, we take the first systematic look at click-spam. We propose the first methodology for advertisers to independently measure click-spam rates on their ads. We also develop an automated methodology for ad networks to proactively detect different simultaneous click-spam attacks. We validate both methodologies using data from major ad networks. We then conduct a large-scale measurement study of click-spam across ten major ad networks and four types of ads. In the process, we identify and perform in-depth analysis on seven ongoing click-spam attacks not blocked by major ad networks at the time of this writing. Our findings highlight the severity of the click-spam problem, especially for mobile ads.


title: Automatic Ontology Matching via Upper Ontologies: A Systematic Evaluation

abstract: ?Ontology matching? is the process of finding correspondences between entities belonging to different ontologies. This paper describes a set of algorithms that exploit upper ontologies as semantic bridges in the ontology matching process and presents a systematic analysis of the relationships among features of matched ontologies (number of simple and composite concepts, stems, concepts at the top level, common English suffixes and prefixes, and ontology depth), matching algorithms, used upper ontologies, and experiment results. This analysis allowed us to state under which circumstances the exploitation of upper ontologies gives significant advantages with respect to traditional approaches that do no use them. We run experiments with SUMO-OWL (a restricted version of SUMO), OpenCyc, and DOLCE. The experiments demonstrate that when our ?structural matching method via upper ontology? uses an upper ontology large enough (OpenCyc, SUMO-OWL), the recall is significantly improved while preserving the precision obtained without upper ontologies. Instead, our ?nonstructural matching method? via OpenCyc and SUMO-OWL improves the precision and maintains the recall. The ?mixed method? that combines the results of structural alignment without using upper ontologies and structural alignment via upper ontologies improves the recall and maintains the F-measure independently of the used upper ontology.


title: Big Data: Issues and Challenges Moving Forward

abstract: Big data refers to data volumes in the range of exabytes (1018) and beyond. Such volumes exceed the capacity of current on-line storage systems and processing systems. Data, information, and knowledge are being created and collected at a rate that is rapidly approaching the exabyte/year range. But, its creation and aggregation are accelerating and will approach the zettabyte/year range within a few years. Volume is only one aspect of big data; other attributes are variety, velocity, value, and complexity. Storage and data transport are technology issues, which seem to be solvable in the near-term, but represent longterm challenges that require research and new paradigms. We analyze the issues and challenges as we begin a collaborative research program into methodologies for big data analysis and design.


title: An accurate Matlab/Simulink based SiC MOSFET model for power converter applications

abstract: In this paper, an analytical model of a silicon carbide power DMOSFET is developed in Matlab/Simulink environment, intended for high performance converter simulations. The proposed Simulink-based model is a highly useful tool that allows users to study system response to transient phenomena and calculate energy losses in a variety of topologies, control strategies and operating conditions. The static characteristics of the model and its performance during hard switching are validated through experimental testing and comparison with similar models in other commercially available simulation platforms. A 1 kW single phase, high frequency inverter is developed using SiC MOSFETs to investigate the accuracy of the proposed model when simulating a complete power converter.


title: Chronic Low Back Pain: Toward an Integrated Psychosocial Assessment Model.

abstract: Recovery processes in leisure time influence the effect of psychosocial work factors on health issues. However, this function of recovery has been neglected in research regarding the influence of work-related risk factors on low back pain (LBP) development. The aim of this prospective study was to examine the function of psychological detachment - a relevant recovery experience - concerning the influence of psychosocial work factors on LBP development. A moderating function of detachment for the interplay of work factors and LBP was assumed.


title: Bittorrent is an auction: analyzing and improving bittorrent's incentives

abstract: Incentives play a crucial role in BitTorrent, motivating users to upload to others to achieve fast download times for all peers. Though long believed to be robust to strategic manipulation, recent work has empirically shown that BitTorrent does not provide its users incentive to follow the protocol. We propose an auction-based model to study and improve upon BitTorrent's incentives. The insight behind our model is that BitTorrent uses, not tit-for-tat as widely believed, but an auction to decide which peers to serve. Our model not only captures known, performance-improving strategies, it shapes our thinking toward new, effective strategies. For example, our analysis demonstrates, counter-intuitively, that BitTorrent peers have incentive to intelligently under-report what pieces of the file they have to their neighbors. We implement and evaluate a modification to BitTorrent in which peers reward one another with proportional shares of bandwidth. Within our game-theoretic model, we prove that a proportional-share client is strategy-proof. With experiments on PlanetLab, a local cluster, and live downloads, we show that a proportional-share unchoker yields faster downloads against BitTorrent and BitTyrant clients, and that under-reporting pieces yields prolonged neighbor interest.


title: Generative Modeling of Convolutional Neural Networks

abstract: The convolutional neural networks (CNNs) have proven to be a powerful tool for discriminative learning. Recently researchers have also started to show interest in the generative aspects of CNNs in order to gain a deeper understanding of what they have learned and how to further improve them. This paper investigates generative modeling of CNNs. The main contributions include: (1) We construct a generative model for the CNN in the form of exponential tilting of a reference distribution. (2) We propose a generative gradient for pre-training CNNs by a non-parametric importance sampling scheme, which is fundamentally different from the commonly used discriminative gradient, and yet has the same computational architecture and cost as the latter. (3) We propose a generative visualization method for the CNNs by sampling from an explicit parametric image distribution. The proposed visualization method can directly draw synthetic samples for any given node in a trained CNN by the Hamiltonian Monte Carlo (HMC) algorithm, without resorting to any extra hold-out images. Experiments on the challenging ImageNet benchmark show that the proposed generative gradient pre-training consistently helps improve the performances of CNNs, and the proposed generative visualization method generates meaningful and varied samples of synthetic images from a large-scale deep CNN.


title: Mining concept-drifting data streams using ensemble classifiers

abstract: Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. Conventional knowledge discovery tools are facing two challenges, the overwhelming volume of the streaming data, and the concept drifts. In this paper, we propose a general framework for mining concept-drifting data streams using weighted ensemble classifiers. We train an ensemble of classification models, such as C4.5, RIPPER, naive Beyesian, etc., from sequential chunks of the data stream. The classifiers in the ensemble are judiciously weighted based on their expected classification accuracy on the test data under the time-evolving environment. Thus, the ensemble approach improves both the efficiency in learning the model and the accuracy in performing classification. Our empirical study shows that the proposed methods have substantial advantage over single-classifier approaches in prediction accuracy, and the ensemble framework is effective for a variety of classification models.


title: The Effect of Color Space Selection on Detectability and Discriminability of Colored Objects

abstract: In this paper, we investigate the effect of color space selection on detectability and discriminability of colored objects under various conditions. 20 color spaces from the literature are evaluated on a large dataset of simulated and real images. We measure the suitability of color spaces from two different perspectives: detectability and discriminability of various color groups. Through experimental evaluation, we found that there is no single optimal color space suitable for all color groups. The color spaces have different levels of sensitivity to different color groups and they are useful depending on the color of the sought object. Overall, the best results were achieved in both simulated and real images using color spaces C1C2C3, UVW and XYZ. In addition, using a simulated environment, we show a practical application of color space selection in the context of top-down control in active visual search. The results indicate that on average color space C1C2C3 followed by HSI and XYZ achieve the best time in searching for objects of various colors. Here, the right choice of color space can improve time of search on average by 20%. As part of our contribution, we also introduce a large dataset of simulated 3D objects


title: No compromises: distributed transactions with consistency, availability, and performance

abstract: Transactions with strong consistency and high availability simplify building and reasoning about distributed systems. However, previous implementations performed poorly. This forced system designers to avoid transactions completely, to weaken consistency guarantees, or to provide single-machine transactions that require programmers to partition their data. In this paper, we show that there is no need to compromise in modern data centers. We show that a main memory distributed computing platform called FaRM can provide distributed transactions with strict serializability, high performance, durability, and high availability. FaRM achieves a peak throughput of 140 million TATP transactions per second on 90 machines with a 4.9 TB database, and it recovers from a failure in less than 50 ms. Key to achieving these results was the design of new transaction, replication, and recovery protocols from first principles to leverage commodity networks with RDMA and a new, inexpensive approach to providing non-volatile DRAM.


title: Pax3 Stimulates p53 Ubiquitination and Degradation Independent of Transcription

abstract: Background: Pax3 is a developmental transcription factor that is required for neural tube and neural crest development. We previously showed that inactivating the p53 tumor suppressor protein prevents neural tube and cardiac neural crest defects in Pax3-mutant mouse embryos. This demonstrates that Pax3 regulates these processes by blocking p53 function. Here we investigated the mechanism by which Pax3 blocks p53 function. Methodology/Principal Findings: We employed murine embryonic stem cell (ESC)-derived neuronal precursors as a cell culture model of embryonic neuroepithelium or neural crest. Pax3 reduced p53 protein stability, but had no effect on p53 mRNA levels or the rate of p53 synthesis. Full length Pax3 as well as fragments that contained either the DNA-binding paired box or the homeodomain, expressed as GST or FLAG fusion proteins, physically associated with p53 and Mdm2 both in vitro and in vivo. In contrast, Splotch Pax3, which causes neural tube and neural crest defects in homozygous embryos, bound weakly, or not at all, to p53 or Mdm2. The paired domain and homeodomain each stimulated Mdm2-mediated ubiquitination of p53 and p53 degradation in the absence of the Pax3 transcription regulatory domains, whereas Splotch Pax3 did not stimulate p53 ubiquitination or degradation. Conclusions/Significance: Pax3 inactivates p53 function by stimulating its ubiquitination and degradation. This process utilizes the Pax3 paired domain and homeodomain but is independent of DNA-binding and transcription regulation. Because inactivating p53 is the only required Pax3 function during neural tube closure and cardiac neural crest development, and inactivating p53 does not require Pax3-dependent transcription regulation, this indicates that Pax3 is not required to function as a transcription factor during neural tube closure and cardiac neural crest development. These findings further suggest novel explanations for PAX3 functions in human diseases, such as in neural crest-derived cancers and Waardenburg syndrome types 1 and 3.


title: A Fast Traversal Heuristic and Optimal Algorithm for Effective Environmental Coverage

abstract: Abstract : Tasks such as street mapping and security surveillance seek a route that traverses a given space to perform a function. These task functions may involve mapping the space for accurate modeling, sensing the space for unusual activity or processing the space for object detection. With a prior map an optimal path can be computed using a graph to represent the environment and generating the solution using known graph algorithms. However, the prior map may be inaccurate due to factors such as occlusion, outdatedness, dynamic objects, and resolution limitations. In this work, we address the NP-hard problem of optimal environmental coverage with incomplete prior map information. To utilize related algorithms in graph theory, we represent the environment as a graph. Using this representation, we present a graph coverage approach for optimal plan generation based on the Undirected Chinese Postman and Rural Postman problems. This approach produces a tractable solution through the use of low complexity algorithms in a branch-and-bound framework. Additionally, as the robot receives sensor updates during traversal of the environment, we update the graph to reflect those changes. The updated graph can be highly disconnected so computing an optimal solution can be NP-hard. To combat this, we introduce a heuristic for coverage path generation that helps maximize the connectivity of the updated graph. We evaluate our approach on a set of comparison tests in simulation.


title: Understanding tables on the web

abstract: The Web contains a wealth of information, and a key challenge is to make this information machine processable. In this paper, we study how to "understand" HTML tables on the Web, which is one step further from finding the schemas of tables. From 0.3 billion Web documents, we obtain 1.95 billion tables, and 0.5-1% of these contain information of various entities and their properties. We argue that in order for computers to understand these tables, computers must first have a brain --- a general purpose knowledge taxonomy that is comprehensive enough to cover the concepts (of worldly facts) in a human mind. Second, we argue that the process of understanding a table is the process of finding the right position for the table in the knowledge taxonomy. Once a table is associated with a concept in the knowledge taxonomy, it will be automatically linked to all other tables that are associated with the same concept, as well as tables associated with concepts related to this concept. In other words, understanding occurs when computers will understand the semantics of the tables through the interconnections of concepts in the knowledge base. In this paper, we illustrate a two phase process. Our experimental results show that the approach is feasible and it may benefit many useful applications such as web search.


title: Visual Fashion-Product Search at SK Planet

abstract: We build a large-scale visual search system which finds similar product images given a fashion item. Defining similarity among arbitrary fashion-products is still remains a challenging problem, even there is no exact ground-truth. To resolve this problem, we define more than 90 fashion-related attributes, and combination of these attributes can represent thousands of unique fashion-styles. The fashion-attributes are one of the ingredients to define semantic similarity among fashion-product images. To build our system at scale, these fashion-attributes are again used to build an inverted indexing scheme. In addition to these fashion-attributes for semantic similarity, we extract colour and appearance features in a region-of-interest (ROI) of a fashion item for visual similarity. By sharing our approach, we expect active discussion on that how to apply current computer vision research into the e-commerce industry.


title: A Data Mining Approach for Retailing Bank Customer Attrition Analysis

abstract: Deregulation within the financial service industries and the widespread acceptance of new technologies is increasing competition in the finance marketplace. Central to the business strategy of every financial service company is the ability to retain existing customers and reach new prospective customers. Data mining is adopted to play an important role in these efforts. In this paper, we present a data mining approach for analyzing retailing bank customer attrition. We discuss the challenging issues such as highly skewed data, time series data unrolling, leaker field detection etc, and the procedure of a data mining project for the attrition analysis for retailing bank customers. We use lift as a proper measure for attrition analysis and compare the lift of data mining models of decision tree, boosted naive Bayesian network, selective Bayesian network, neural network and the ensemble of classifiers of the above methods. Some interesting findings are reported. Our research work demonstrates the effectiveness and efficiency of data mining in attrition analysis for retailing bank.


title: Linking recycling behavior to waste management planning: A case study of office workers in Taiwan

abstract: Abstract In recent years, planners have become increasingly involved in issues related to solid waste and the need to develop comprehensive waste management programs. Policy options have been suggested for reducing consumption—an important mechanism for addressing the solid waste disposal problem. These options range from educational programs which encourage individuals and organizations to voluntarily minimize consumer waste to regulations that mandate waste reduction behavior, including recycling, resource reduction, and re-use. In deciding among policy options, planners need to understand those factors that are likely to influence these behaviors in different settings. Although there is a growing body of research covering factors which influence household recycling, determinants of recycling in the workplace are largely unknown. In this paper, a model displaying factors that may contribute to recycling behavior in the office is presented. Components of the model are then analyzed using data from questionnaires administered to 1788 office workers in 32 organizations in the Taipei metropolitan area. The role of prior recycling experience at home and the organizational and physical context of workplaces in determining office recycling rates are analyzed, as are relationships between environmental attitudes and motivations and recycling practices. Finally, policy options (i.e. educational programs, financial incentives, establishing social norms) for conserving resources through waste management are discussed in light of the findings.


Augmented Reality in Schools: Preliminary Evaluation Results from a Summer School


title: Fingerprint recognition using Polynomial Discrete Radon Transform

abstract: This paper presents a new approach to identify persons using their fingerprints. The proposed approach is based on the Polynomial Discrete Radon Transform (PDRT). The PDRT extends the Classical Radon Transform (RT) by generalizing the curves used for the projection. In fact, the classical Radon projection is restricted to straight lines. However, the PDRT method projects the image along polynomial curves following different directions. The proposed technique enables the extraction of directional characteristics from the fingerprint of the hand. Since several methods in the literature reached a level of success on fingerprint recognition, this paper proposes a new identification method which is invariant to geometrical transformations such as translation and rotation. In addition, it is robust to noise. To evaluate the performance of this approach, we have tested it on a database containing 128 fingerprint images. The obtained result confirms the efficiency of the method.


title: FAAST: The Flexible Action and Articulated Skeleton Toolkit

abstract: The Flexible Action and Articulated Skeleton Toolkit (FAAST) is middleware to facilitate integration of full-body control with virtual reality applications and video games using OpenNI-compliant depth sensors (currently the PrimeSensor and the Microsoft Kinect). FAAST incorporates a VRPN server for streaming the user's skeleton joints over a network, which provides a convenient interface for custom virtual reality applications and games. This body pose information can be used for goals such as realistically puppeting a virtual avatar or controlling an on-screen mouse cursor. Additionally, the toolkit also provides a configurable input emulator that detects human actions and binds them to virtual mouse and keyboard commands, which are sent to the actively selected window. Thus, FAAST can enable natural interaction for existing off-the-shelf video games that were not explicitly developed to support input from motion sensors. The actions and input bindings are configurable at run-time, allowing the user to customize the controls and sensitivity to adjust for individual body types and preferences. In the future, we plan to substantially expand FAAST's action lexicon, provide support for recording and training custom gestures, and incorporate real-time head tracking using computer vision techniques.


title: Design of an FFT/IFFT Processor for MIMO OFDM Systems

abstract: In this paper, we present a novel 128/64 point fast Fourier transform (FFT)/ inverse FFT (IFFT) processor for the applications in a multiple-input multiple-output orthogonal frequency-division multiplexing based IEEE 802.11n wireless local area network baseband processor. The unfolding mixed-radix multipath delay feedback FFT architecture is proposed to efficiently deal with multiple data sequences. The proposed processor not only supports the operation of FFT/IFFT in 128 points and 64 points but can also provide different throughput rates for 1-4 simultaneous data sequences to meet IEEE 802.11n requirements. Furthermore, less hardware complexity is needed in our design compared with traditional four-parallel approach. The proposed FFT/IFFT processor is designed in a 0.13-mum single-poly and eight-metal CMOS process. The core area is 660times2142 mum2 , including an FFT/IFFT processor and a test module. At the operation clock rate of 40 MHz, our proposed processor can calculate 128-point FFT with four independent data sequences within 3.2 mus meeting IEEE 802.11n standard requirements


title: Quantum sort algorithm based on entanglement qubits {00, 11}

abstract: Quantum algorithms have gained a lot of consideration especially for the need of searching unsorted database in a faster way rather than using the classical search mechanisms. This paper introduces a new quantum sort algorithm by employing the quantum features to expedite the searching process in conventional environments. The proposed efficient sorting algorithm emphasizes entanglement qubits property to sort N positive integers.


title: Energy-Saving Virtual Machine Placement in Cloud Data Centers

abstract: In cloud data centers, different mapping relationships between virtual machines (VMs) and physical machines (PMs) cause different resource utilization, therefore, how to place VMs on PMs to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. The existing VM placement schemes are to optimize physical server resources utilization or network resources utilization, but few of them focuses on optimizing multiple resources utilization simultaneously. To address the issue, this paper proposes a VM placement scheme meeting multiple resource constraints, such as the physical server size (CPU, memory, storage, bandwidth, etc.) and network link capacity to improve resource utilization and reduce both the number of active physical servers and network elements so as to finally reduce energy consumption. Since VM placement problem is abstracted as a combination of bin packing problem and quadratic assignment problem, which is also known as a classic combinatorial optimization and NP-hard problem, we design a novel greedy algorithm by combining minimum cut with the best-fit, and the simulations show that our solution achieves better results.


title: Improving multi-label classification using scene cues

abstract: Multi-label classification is one of the most challenging tasks in the computer vision community, owing to different composition and interaction (e.g. partial visibility or occlusion) between objects in multi-label images. Intuitively, some objects usually co-occur with some specific scenes, e.g. the sofa often appears in a living room. Therefore, the scene of a given image may provides informative cues for identifying those embedded objects. In this paper, we propose a novel scene-aware deep framework for addressing the challenging multi-label classification task. In particular, we incorporate two sub-networks that are pre-trained for different tasks (i.e. object classification and scene classification) into a unified framework, so that informative scene-aware cues can be leveraged for benefiting multi-label object classification. In addition, we also present a novel one vs. all multiple-cross-entropy (MCE) loss for optimizing the proposed scene-aware deep framework by independently penalizing the classification error for each label. The proposed method can be learned in an end-to-end manner and extensive experimental results on Pascal VOC 2007 and MS COCO demonstrate that our approach is able to make a noticeable improvement for the multi-label classification task.


title: Hand-Eye Calibration

abstract: Whenever a sensor is mounted on a robot hand, it is important to know the relationship between the sensor and the hand. The problem of determining this relationship is referred to as the hand-eye calibration problem. Hand-eye calibration is impor tant in at least two types of tasks: (1) map sensor centered measurements into the robot workspace frame and (2) tasks allowing the robot to precisely move the sensor. In the past some solutions were proposed, particularly in the case of the sensor being a television camera. With almost no exception, all existing solutions attempt to solve a homogeneous matrix equation of the form AX = X B. This article has the following main contributions. First we show that there are two possible formulations of the hand-eye calibration problem. One formu lation is the classic one just mentioned. A second formulation takes the form of the following homogeneous matrix equation: MY = M'YB. The advantage of the latter formulation is that the extrinsic and intrinsic parameters of t...


title: Programming and Runtime Support to Blaze FPGA Accelerator Deployment at Datacenter Scale

abstract: With the end of CPU core scaling due to dark silicon limitations, customized accelerators on FPGAs have gained increased attention in modern datacenters due to their lower power, high performance and energy efficiency. Evidenced by Microsoft's FPGA deployment in its Bing search engine and Intel's 16.7 billion acquisition of Altera, integrating FPGAs into datacenters is considered one of the most promising approaches to sustain future datacenter growth. However, it is quite challenging for existing big data computing systems---like Apache Spark and Hadoop---to access the performance and energy benefits of FPGA accelerators. In this paper we design and implement Blaze to provide programming and runtime support for enabling easy and efficient deployments of FPGA accelerators in datacenters. In particular, Blaze abstracts FPGA accelerators as a service (FaaS) and provides a set of clean programming APIs for big data processing applications to easily utilize those accelerators. Our Blaze runtime implements an FaaS framework to efficiently share FPGA accelerators among multiple heterogeneous threads on a single node, and extends Hadoop YARN with accelerator-centric scheduling to efficiently share them among multiple computing tasks in the cluster. Experimental results using four representative big data applications demonstrate that Blaze greatly reduces the programming efforts to access FPGA accelerators in systems like Apache Spark and YARN, and improves the system throughput by 1.7× to 3× (and energy efficiency by 1.5× to 2.7×) compared to a conventional CPU-only cluster.


title: Grounded theory in practice: issues and discussion for new qualitative researchers

abstract: Purpose – The purpose of this paper is to clarify four common confusions in grounded theory (GT) use by demonstrating an example of the author's doctoral study into conceptualising higher education students' learning and knowledge sharing by exploring blogging phenomena. It seeks not only to present the author's personal experience and views, but also to shed light on the causes of the confusion for novice qualitative researchers.Design/methodology/approach – A review of literature on GT and the author's practical experience of undertaking an empirical study into the blogging phenomenon in LIS discipline form the approach to addressing the issue.Findings – The paper summarises four common issues that hinder inexperienced qualitative researchers when they undertake GT research: using GT as a methodology or method; how to use literature review; how to use coding strategy; and what is the generated theory.Practical implications – The paper provides practical suggestions of what matters when adopting GT appro...


title: Microbial Biosensors: Engineered Microorganisms as the Sensing Machinery

abstract: Whole-cell biosensors are a good alternative to enzyme-based biosensors since they offer the benefits of low cost and improved stability. In recent years, live cells have been employed as biosensors for a wide range of targets. In this review, we will focus on the use of microorganisms that are genetically modified with the desirable outputs in order to improve the biosensor performance. Different methodologies based on genetic/protein engineering and synthetic biology to construct microorganisms with the required signal outputs, sensitivity, and selectivity will be discussed.


title: A family with the Coffin Lowry syndrome revisited: localization of CLS to Xp21-pter.

abstract: We revisited a family with the Coffin-Lowry syndrome (CLS) first reported by Procopis and Turner in 1972. Twelve affected members are now known in 3 generations of which 9 were seen personally. DNA marker studies supported X-linkage with localization of CLS to Xp near DXS43 at p22.2-22.1 (theta = 0.001 Z = 2.71). Such linkage is reinforced by positive lod scores for DXS28 (theta = 0.00, Z = 0.90) and for DXS84 (theta = 0.09, Z = 1.56). Recombination with DXS84 and DXS164 places CLS distal to DMD in Xp21-pter.


title: Absenteeism among medical and health science undergraduate students at Hawassa University, Ethiopia

abstract: BackgroundStudent absenteeism is a major concern for university education worldwide. This study was conducted to determine the prevalence and causes of absenteeism among undergraduate medical and health sciences students at Hawassa University.MethodsWe conducted a cross-sectional study using a pretested self-administered structured questionnaire from May-June 2013. The primary outcome indicator was self-reported absenteeism from lectures in the semester preceding the study period. The study included all regular undergraduate students who were enrolled in the University for at least one semester. The data was entered and analyzed using SPSS version 20. The association between class absenteeism and socio-demographic and behavioral correlates of absenteeism was determined by bivariate and multivariate analyses. Results were reported as crude odds ratios (COR), adjusted odds ratios (AOR) and 95% confidence intervals (CI).Results1200 students consented and filled the questionnaire. Of these students, 43.7% had missed three or more lectures and 14.1% (95% CI = 12.2-16.2) missed more than 8 lectures in the preceding semester. There was a significant association between missing more than 8 lectures and age of students, chosen discipline (medicine), and social drug use. The main reasons reported for missing lectures were preparing for another examination, lack of interest, lecturer’s teaching style, and availability of lecture material.ConclusionAt Hawassa University College of Medicine and Health Science student habits and teacher performance play a role in absenteeism from lectures. A university culture that promotes discipline and integrity especially among medical and older students discourages social drug use will likely improve motivation and attendance. Training in teaching methodologies to improve the quality and delivery of lectures should also help increase attendance.


title: Wearable, Wireless EEG Solutions in Daily Life Applications: What are we Missing?

abstract: Monitoring human brain activity has great potential in helping us understand the functioning of our brain, as well as in preventing mental disorders and cognitive decline and improve our quality of life. Noninvasive surface EEG is the dominant modality for studying brain dynamics and performance in real-life interaction of humans with their environment. To take full advantage of surface EEG recordings, EEG technology has to be advanced to a level that it can be used in daily life activities. Furthermore, users have to see it as an unobtrusive option to monitor and improve their health. To achieve this, EEG systems have to be transformed from stationary, wired, and cumbersome systems used mostly in clinical practice today, to intelligent wearable, wireless, convenient, and comfortable lifestyle solutions that provide high signal quality. Here, we discuss state-of-the-art in wireless and wearable EEG solutions and a number of aspects where such solutions require improvements when handling electrical activity of the brain. We address personal traits and sensory inputs, brain signal generation and acquisition, brain signal analysis, and feedback generation. We provide guidelines on how these aspects can be advanced further such that we can develop intelligent wearable, wireless, lifestyle EEG solutions. We recognized the following aspects as the ones that need rapid research progress: application driven design, end-user driven development, standardization and sharing of EEG data, and development of sophisticated approaches to handle EEG artifacts.


title: Fog and IoT: An Overview of Research Opportunities

abstract: Fog is an emergent architecture for computing, storage, control, and networking that distributes these services closer to end users along the cloud-to-things continuum. It covers both mobile and wireline scenarios, traverses across hardware and software, resides on network edge but also over access networks and among end users, and includes both data plane and control plane. As an architecture, it supports a growing variety of applications, including those in the Internet of Things (IoT), fifth-generation (5G) wireless systems, and embedded artificial intelligence (AI). This survey paper summarizes the opportunities and challenges of fog, focusing primarily in the networking context of IoT.


Alignment-Based Neural Machine Translation


title: Skin Detection and Segmentation of Human Face in Color Images

abstract: This paper presents the procedures of face segmentation in the color image based on skin detection through the establishment of skin model and the segmentation of skin region. Firstly, in order to reduce the effect of factors on the segmentation of face region, a method for compensating the color of input images is used to alleviate the interferences from bad illuminating conditions. Secondly, Gaussian model about skin information is established which can be used to detect skin pixels in color images and transform color images to gray-scale images. Thirdly, a new algorithm of Otsu is used to find out the skin regions in binary images. Finally, mathematical morphology operator and prior knowledge are used to find out the face regions and discard regions that are similar to the skin in color. This method can deal with various sizes of faces, different illumination conditions, diverse poses and changeable expressions. In particular, the scheme significantly increases the execution speed of the face segmentation algorithm in the case of complex backgrounds. The experiments show that this method reduces the computation of the procedure, and at the same time improves the detection speed and efficiency.


title: First ocean survey with synthetic aperture sonar on neutral trim tow-fish

abstract: For the purpose of surveying ocean bottom in detail and quickly, Japan Agency for the Marine-Earth Science and Technology developed a neutral trim tow-fish as a stable platform equipped with synthetic aperture sonar. The two patents is applied to the synthesizing process. In August 2010 at Sagami bay, we carried out a sea trial of the SAS on the tow-fish. The motion compensation on the synthesizing worked effectively and the sonar images are stabilized or clear after the process in the trial. In October 2010 at Kagoshima bay, we surveyed a total of 7 km2 area including a mound and a bottom of caldera with the SAS on the tow-fish. We could found hydrothermal vent and bubbles welling out at the mound and the bottom, and unevenness of the caldera was also visible.


title: Clinical Implications and Methodological Challenges in the Study of the Neuropsychological Correlates of Cannabis, Stimulant, and Opioid Abuse

abstract: Chronic consumption of several drugs of abuse (cannabis, stimulants, opioids) has been associated with the presence of neuropsychological impairments in a broad range of functions. Nevertheless, in recent years neuropsychological research on substance abuse has focused on the study of impairments in the executive functions linked to the prefrontal cortex and their influence on the personality, cognitions, and behaviors of the substance abusers. The aim of our review is, first, to summarize the main neuropsychological impairments shown by classic studies, as well as these new discoveries in executive functioning; second, to consider the mediating role of neuropsychological status on treatment outcomes and analyze the impact of these impairments in clinical practice with drug addicts; and third, to review the principal methodological challenges associated with research in the field of the neuropsychology of substance abuse. We also highlight the convenience of intervening in those functions most relevant to the abusers' persistence in consumption and risk of relapse.


title: Face versus non-face object perception and the ‘other-race’ effect: a spatio-temporal event-related potential study

abstract: Abstract Objective : To investigate a modulation of the N170 face-sensitive component related to the perception of other-race (OR) and same-race (SR) faces, as well as differences in face and non-face object processing, by combining different methods of event-related potential (ERP) signal analysis. Methods : Sixty-two channel ERPs were recorded in 12 Caucasian subjects presented with Caucasian and Asian faces along with non-face objects. Surface data were submitted to classical waveforms and ERP map topography analysis. Underlying brain sources were estimated with two inverse solutions (BESA and LORETA). Results : The N170 face component was identical for both race faces. This component and its topography revealed a face specific pattern regardless of race. However, in this time period OR faces evoked significantly stronger medial occipital activity than SR faces. Moreover, in terms of maps, at around 170 ms face-specific activity significantly preceded non-face object activity by 25 ms. These ERP maps were followed by similar activation patterns across conditions around 190–300 ms, most likely reflecting the activation of visually derived semantic information. Conclusions : The N170 was not sensitive to the race of the faces. However, a possible pre-attentive process associated to the relatively stronger unfamiliarity for OR faces was found in medial occipital area. Moreover, our data provide further information on the time-course of face and non-face object processing.


title: Research Article Learning in Color: How Color and Affect Influence Learning Outcomes

abstract: Research problem: The purpose of the study is to understand how affective reactions to color impact learning attitudes and outcomes in a computer-mediated learning environment. Research question: How do color differences change affective processes and outcomes in computer-mediated communication? Literature review: Several previous studies exploring particular characteristics and learning in computer-mediated environments influenced the review of the literature. The literature on color psychology indicates that color preferences and affective reactions to color can influence behaviors and attitudes. The literature on goal achievement motivation posits that affective dispositions influence goal orientation, motivation, and individual outcomes. The literature on affect infers that affective reactions are responses to events, and these reactions influence attitudes and behaviors. The current study draws on these prior studies to examine affective reaction to color and learning outcomes in a computer-mediated learning environment. Methodology: We conducted a quasiexperimental study with 79 participants, who listened to a visual presentation lecture with either blue or yellow background and then completed a survey on their affective reactions, learning attitudes, and outcomes. Results and discussion: The results of our study indicate that color is not neutral and may influence learning attitudes and outcomes and, hence, the color of computer technology interface design can influence learning outcomes. Practitioners and academics must take people's affective reactions to color into account in designs and studies of visual information presentations. The sample size and the focus on two color hues (yellow and blue) may have some limitations on the conclusions and generalizability of this study. Future studies should examine more color hues and color saturation to further our understanding of affective reactions to colors and consequent impact on attitudes and behavioral outcomes.


title: Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact

abstract: Ponzi schemes are financial frauds where, under the promise of high profits, users put their money, recovering their investment and interests only if enough users after them continue to invest money. Originated in the offline world 150 years ago, Ponzi schemes have since then migrated to the digital world, approaching first on the Web, and more recently hanging over cryptocurrencies like Bitcoin. Smart contract platforms like Ethereum have provided a new opportunity for scammers, who have now the possibility of creating "trustworthy" frauds that still make users lose money, but at least are guaranteed to execute "correctly". We present a comprehensive survey of Ponzi schemes on Ethereum, analysing their behaviour and their impact from various viewpoints. Perhaps surprisingly, we identify a remarkably high number of Ponzi schemes, despite the hosting platform has been operating for less than two years.


title: A CMOS bandpass filter with wide-tuning range for wireless applications

abstract: A 2nd order RF bandpass filter with Q-enhancement active inductor is presented. In the filter, a design technique for a tunable Q-enhancement CMOS active inductor operating in the wide RF-band is described. Moreover, issues related to noise and stability of the active inductor is addressed. Simulated performance presented shows that the filter has about 50.6dB spurious free dynamic range (SFDR) at 2.45GHz with Q/spl ap/30, and the center frequency of the filter using a 0.25-/spl mu/m CMOS process can be operated at the 1.60G-2.45GHz frequency band under a 1.8V power supply. The filter is suited for multi-band wireless applications.


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title: Behaviour analysis of machine learning algorithms for detecting P2P botnets

abstract: Botnets have emerged as a powerful threat on the Internet as it is being used to carry out cybercrimes. In this paper, we have analysed some machine learning techniques to detect peer to peer (P2P) botnets. As the detection of P2P botnets is widely unexplored area, we have focused on it. We experimented with different machine learning (ML) algorithms to compare their ability to classify the botnet traffic from the normal traffic by selecting distinguishing features of the network traffic. Experiments are performed on the dataset containing the traces of various P2P botnets. Results and tradeoffs obtained of different ML algorithms on different metrics are presented at the end of the paper.


title: Image Privacy Protection with Secure JPEG Transmorphing

abstract: Thanks to advancements in smart mobile devices and social media platforms, sharing photos and experiences has significantly bridged our lives, allowing us to stay connected despite distance and other barriers. However, concern on privacy has also been raised, due to not only mistakes or ignorance of impact of careless sharing but also complex infrastructures and cross-use of social media content. In this paper, we present secure JPEG Transmorphing, a flexible framework for protecting image visual privacy in a secure, reversible, fun and personalized manner. With secure JPEG Transmorphing, the protected image is also backwards compatible with JPEG, the most commonly used image format. Experiments have been performed and results show that the proposed method provides a near lossless image reconstruction, a controllable level of storage overhead, and a good degree of privacy protection and subjective pleasantness.


title: Competition favours reduced cost of plasmids to host bacteria

abstract: Conjugative plasmids of Gram-negative bacteria have both vertical and horizontal modes of transmission: they are segregated to daughter cells during division, and transferred between hosts by plasmid-encoded conjugative machinery. Despite maintaining horizontal mobility, many plasmids carry fertility inhibition (fin) systems that repress their own conjugative transfer. To assess the ecological basis of self-transfer repression, we compared the invasion of bacterial populations by fin(+) and fin(-) variants of the plasmid R1 using a computational model and co-culture competitions. We observed that the fin(+) variant had a modest cost to the host (measured by reduction in growth rate), while the fin(-) variant incurred a larger cost. In simulations and empirical competitions the fin(-) plasmid invaded cultures quickly, but was subsequently displaced by the fin(+) plasmid. This indicated a competitive advantage to reducing horizontal transmission and allowing increased host replication. Computational simulations predicted that the advantage associated with reduced cost to the host would be maintained over a wide range of environmental conditions and plasmid costs. We infer that vertical transmission in concert with competitive exclusion favour decreased horizontal mobility of plasmids. Similar dynamics may exert evolutionary pressure on parasites, such as temperate bacteriophages and vertically transmitted animal viruses, to limit their rates of horizontal transfer.


title: Advances in engineering hydrogels

abstract: Hydrogels are formed from hydrophilic polymer chains surrounded by a water-rich environment. They have widespread applications in various fields such as biomedicine, soft electronics, sensors, and actuators. Conventional hydrogels usually possess limited mechanical strength and are prone to permanent breakage. Further, the lack of dynamic cues and structural complexity within the hydrogels has limited their functions. Recent developments include engineering hydrogels that possess improved physicochemical properties, ranging from designs of innovative chemistries and compositions to integration of dynamic modulation and sophisticated architectures. We review major advances in designing and engineering hydrogels and strategies targeting precise manipulation of their properties across multiple scales.


title: A particle filter for monocular vision-aided odometry

abstract: We propose a particle filter-based algorithm for monocular vision-aided odometry for mobile robot localization. The algorithm fuses information from odometry with observations of naturally occurring static point features in the environment. A key contribution of this work is a novel approach for computing the particle weights, which does not require including the feature positions in the state vector. As a result, the computational and sample complexities of the algorithm remain low even in feature-dense environments. We validate the effectiveness of the approach extensively with both simulations as well as real-world data, and compare its performance against that of the extended Kalman filter (EKF) and FastSLAM. Results from the simulation tests show that the particle filter approach is better than these competing approaches in terms of the RMS error. Moreover, the experiments demonstrate that the approach is capable of achieving good localization accuracy in complex environments.