title: The Cluster Between Internet of Things and Social Networks: Review and Research Challenges

abstract: The cluster between Internet of Things (IoT) and social networks (SNs) enables the connection of people to the ubiquitous computing universe. In this framework, the information coming from the environment is provided by the IoT, and the SN brings the glue to allow human-to-device interactions. This paper explores the novel paradigm for ubiquitous computing beyond IoT, denoted by Social Internet of Things (SIoT). Although there have been early-stage studies in social-driven IoT, they merely use one or some properties of SIoT to improve a number of specific performance variables. Therefore, this paper first addresses a complete view on SIoT and key perspectives to envision the real ubiquitous computing. Thereafter, a literature review is presented along with the evolutionary history of IoT research from Intranet of Things to SIoT. Finally, this paper proposes a generic SIoT architecture and presents a discussion about enabling technologies, research challenges, and open issues.


title: An Analysis of Human Motion Detection Systems Use During Elder Exercise Routines

abstract: Human motion analysis provides motion pattern and body pose estimations. This study integrates computer-vision techniques and explores a markerless human motion analysis system. Using human-computer interaction (HCI) methods and goals, researchers use a computer interface to provide feedback about range of motion to users. A total of 35 adults aged 65 and older perform three exercises in a public gym while human motion capture methods are used. Following exercises, participants are shown processed human motion images captured during exercises on a customized interface. Standardized questionnaires are used to elicit responses from users during interactions with the interface. A matrix of HCI goals (effectiveness, efficiency, and user satisfaction) and emerging themes are used to describe interactions. Sixteen users state the interface would be useful, but not necessarily for safety purposes. Users want better image quality, when expectations are matched satisfaction increases, and unclear meaning of motion measures decreases satisfaction.


title: Interrater Reliability of a Modified Ashworth Scale of Muscle Spasticity

abstract: We undertook this investigation to determine the interrater reliability of manual tests of elbow flexor muscle spasticity graded on a modified Ashworth scale. We each independently graded the elbow flexor muscle spasticity of 30 patients with intracranial lesions. We agreed on 86.7% of our ratings. The Kendall's tau correlation between our grades was .847 (p less than .001). Thus, the relationship between the raters' judgments was significant and the reliability was good. Although the results were limited to the elbow flexor muscle group, we believe them to be positive enough to encourage further trials of the modified Ashworth scale for grading spasticity.


title: Expression transfer: A system to build 3D blend shapes for facial animation

abstract: Blending face geometry in different expressions is a popular approach for facial animation in films and games. The quality of the animation relies on the set of blend shape expressions, and creating sufficient blend shapes takes a large amount of time and effort. This paper presents a complete pipeline to create a set of blend shapes in different expressions for a face mesh having only a neutral expression. A template blend shapes model having sufficient expressions is provided and the neutral expression of the template mesh model is registered into the target face mesh using a non-rigid ICP (iterative closest point) algorithm. Deformation gradients between the template and target neutral mesh are then transferred to each expression to form a new set of blend shapes for the target face. We solve optimization problem to consistently map the deformation of the source blend shapes to the target face model. The result is a new set of blend shapes for a target mesh having triangle-wise correspondences between the source face and target faces. After creating blend shapes, the blend shape animation of the source face is retargeted to the target mesh automatically.


title: Cross-Domain Academic Paper Recommendation by Semantic Linkage Approach Using Text Analysis and Recurrent Neural Networks

abstract: In this digital age, free-flow and exchange of knowledge and information are of paramount importance. This is the prime reason why we decided to tackle cross-domain linkage. Firstly, we build a system which recommends scholarly academic papers based on the content of news article a user is reading using text analysis techniques. We perform a human expert evaluation to test the system for relevance. Our judges show good agreement with a kappa value of 0.869. To improve the quality of recommendations further, we use an RNN-LSTM model trained on Wikipedia to measure document relevance. We reorder a list of academic papers based on their semantic similarity with the input document using our RNN-LSTM model. Our model achieves a slightly better performance than one of the best document embedding techniques doc2vec (paragraph vector). To the best of our knowledge, ours is the first study linking the domains of News Media and Academic landscape, and bridging the knowledge-gap.


title: Disseminating Cardiopulmonary Resuscitation Training by Distributing 35 000 Personal Manikins Among School Children

abstract: Background— Because most cardiac arrests occur at home, widespread training is needed to increase the incidence of cardiopulmonary resuscitation (CPR) by lay persons. The aim of this study was to evaluate the effect of mass distribution of CPR instructional materials among schoolchildren. Methods and Results— We distributed 35 002 resuscitation manikins to pupils (12 to 14 years of age) at 806 primary schools. Using the enclosed 24-minute instructional DVD, they trained in CPR and subsequently used the kit to train family and friends (second tier). They completed a questionnaire on who had trained in CPR using the kit. Teachers also were asked to evaluate the project. The incidence of bystander CPR in out-of-hospital cardiac arrest in the months following the project was compared with the previous year. In total, 6947 questionnaires (19.8%) were returned. The 6947 kits had been used to train 17 140 from the second tier (mean, 2.5 persons per pupil; 95% confidence interval, 2.4 to 2.5). The teachers had us...


title: Ontology based data integration of NoSQL datastores

abstract: NoSQL databases are suitable for network traffic loads experienced by today's websites. The important attributes that make them a favorable choice are inherent distributed nature, horizontal scalability and flexibility in data models. Hence, more data is now getting handled through NoSQL databases and sensible information extraction from these data stores is becoming a requirement. Information extraction may require sourcing data from multiple data sources, establishing relationship among them and querying across these data sources together. Ontology based semantic integration systems for Relational Database Management Systems (RDBMS) already exist that satisfies the above requirement. Many commercial systems are operational based on the above technique and tools and solutions for the above approach are very much mature. Hence, mapping the processing done in each stage of RDBMS based semantic integration systems to that of NoSQL systems can facilitate usage of existing tools and frameworks. The purpose of this work is to develop ontology based semantic integration system for a column-oriented NoSQL data store like HBase which is similar in architectural design to RDBMS.


title: Safe collaboration of humans and SCARA robots

abstract: This paper presents a method to determine distance between human operator and SCARA robot using computer vision in order to provide a safe workstation for human robot collaboration. Kinect sensor is used as the input device to the system. Kinect has four streams of data among which depth data is effectively used in this approach. The measured distance is used to calculate danger index. Online trajectory generation allows the robot to perform appropriate action. The results of applying this system to FUM SCARA is presented.


ACTUATED TRAFFIC SIGNAL OPTIMIZATION USING EVOLUTIONARY ALGORITHMS


title: Robust text classification in the presence of confounding bias

abstract: As text classifiers become increasingly used in real-time applications, it is critical to consider not only their accuracy but also their robustness to changes in the data distribution. In this paper, we consider the case where there is a confounding variable Z that influences both the text features X and the class variable Y . For example, a classifier trained to predict the health status of a user based on their online communications may be confounded by socioeconomic variables. When the influence of Z changes from training to testing data, we find that classifier accuracy can degrade rapidly. Our approach, based on Pearl's back-door adjustment, estimates the underlying effect of a text variable on the class variable while controlling for the confounding variable. Although our goal is prediction, not causal inference, we find that such adjustments are essential to building text classifiers that are robust to confounding variables. On three diverse text classifications tasks, we find that covariate adjustment results in higher accuracy than competing baselines over a range of confounding relationships (e.g., in one setting, accuracy improves from 60% to 81%).


Kwyjibo: automatic domain name generation


title: Structural elements required for coupling ion and substrate transport in the neurotransmitter transporter homolog LeuT

abstract: The coupled transport of ions and substrates allows transporters to accumulate substrates using the energy in transmembrane ion gradients and electrical potentials. During transport, conformational changes that switch accessibility of substrate and ion binding sites from one side of the membrane to the other must be controlled so as to prevent uncoupled movement of ions or substrates. In the Neurotransmitter:Sodium Symporter (NSS) family, Na+ stabilizes the transporter in an outward-open state, thus decreasing the likelihood of uncoupled Na+ transport. In a step essential for coupled transport, substrate binding must overcome the effect of Na+, allowing intracellular substrate and Na+ release from an inward-open state. However, it is unclear which specific elements of the protein mediate this conformational response to substrate binding. Previously, we showed that in the prokaryotic NSS transporter LeuT, the effect of Na+ on conformation occurs at the Na2 site, where it influences conformation by fostering interaction between two domains of the protein (JBC 291: 1456, 2016). Here, we identify a conserved tyrosine residue in the substrate binding site required for substrate to enable conversion to inward-open states by establishing an interaction between the two transporter domains. We further identify additional interactions between the two transporter domains in the extracellular pathway that are required. Together with our previous work on the conformational effect of Na+, these results identify mechanistic components underlying ion-substrate coupling in NSS transporters.


title: Fast and Efficient Compressive Sensing using Structurally Random Matrices

abstract: This paper introduces a new framework of fast and efficient sensing matrices for practical compressive sensing, called Structurally Random Matrix (SRM). In the proposed framework, we pre-randomize a sensing signal by scrambling its samples or flipping its sample signs and then fast-transform the randomized samples and finally, subsample the transform coefficients as the final sensing measurements. SRM is highly relevant for large-scale, real-time compressive sensing applications as it has fast computation and supports block-based processing. In addition, we can show that SRM has theoretical sensing performance comparable with that of completely random sensing matrices. Numerical simulation results verify the validity of the theory as well as illustrate the promising potentials of the proposed sensing framework.


title: Issues and progress with protein kinase inhibitors for cancer treatment

abstract: Identification of the key roles of protein kinases in cancer has led to extensive efforts to develop kinase inhibitors for the treatment of a wide range of cancers, and more than 30 such agents are now in clinical trials. Here, we consider the crucial issues in the development of kinase inhibitors for cancer, and discuss strategies to address the challenges raised by these issues in the light of preclinical and clinical experiences so far.


title: Self-organising software architectures for distributed systems

abstract: A self-organising software architecture is one in which components automatically configure their interaction in a way that is compatible with an overall architectural specification. The objective is to minimise the degree of explicit management necessary for construction and subsequent evolution whilst preserving the architectural properties implied by its specification. This paper examines the feasibility of using architectural constraints as the basis for the specification, design and implementation of self-organising architectures for distributed systems. Although we focus on organising the structure of systems, we show how component state can influence reconfiguration via interface attributes.


title: Fast predictive multimodal image registration

abstract: We introduce a deep encoder-decoder architecture for image deformation prediction from multimodal images. Specifically, we design an image-patch-based deep network that jointly (i) learns an image similarity measure and (ii) the relationship between image patches and deformation parameters. While our method can be applied to general image registration formulations, we focus on the Large Deformation Diffeomorphic Metric Mapping (LDDMM) registration model. By predicting the initial momentum of the shooting formulation of LDDMM, we preserve its mathematical properties and drastically reduce the computation time, compared to optimization-based approaches. Furthermore, we create a Bayesian probabilistic version of the network that allows evaluation of registration uncertainty via sampling of the network at test time. We evaluate our method on a 3D brain MRI dataset using both T1- and T2-weighted images. Our experiments show that our method generates accurate predictions and that learning the similarity measure leads to more consistent registrations than relying on generic multimodal image similarity measures, such as mutual information. Our approach is an order of magnitude faster than optimization-based LDDMM.


ADVANCED PROCESS CONTROL


title: Spectral Ensemble Clustering via Weighted K-Means: Theoretical and Practical Evidence

abstract: As a promising way for heterogeneous data analytics, consensus clustering has attracted increasing attention in recent decades. Among various excellent solutions, the co-association matrix based methods form a landmark, which redefines consensus clustering as a graph partition problem. Nevertheless, the relatively high time and space complexities preclude it from wide real-life applications. We, therefore, propose Spectral Ensemble Clustering (SEC) to leverage the advantages of co-association matrix in information integration but run more efficiently. We disclose the theoretical equivalence between SEC and weighted K-means clustering, which dramatically reduces the algorithmic complexity. We also derive the latent consensus function of SEC, which to our best knowledge is the first to bridge co-association matrix based methods to the methods with explicit global objective functions. Further, we prove in theory that SEC holds the robustness, generalizability, and convergence properties. We finally extend SEC to meet the challenge arising from incomplete basic partitions, based on which a row-segmentation scheme for big data clustering is proposed. Experiments on various real-world data sets in both ensemble and multi-view clustering scenarios demonstrate the superiority of SEC to some state-of-the-art methods. In particular, SEC seems to be a promising candidate for big data clustering.


title: Exploiting Friendship Relations for Efficient Routing in Mobile Social Networks

abstract: Routing in delay tolerant networks is a challenging problem due to the intermittent connectivity between nodes resulting in the frequent absence of end-to-end path for any source-destination pair at any given time. Recently, this problem has attracted a great deal of interest and several approaches have been proposed. Since Mobile Social Networks (MSNs) are increasingly popular type of Delay Tolerant Networks (DTNs), making accurate analysis of social network properties of these networks is essential for designing efficient routing protocols. In this paper, we introduce a new metric that detects the quality of friendships between nodes accurately. Utilizing this metric, we define the community of each node as the set of nodes having close friendship relations with this node either directly or indirectly. We also present Friendship-Based Routing in which periodically differentiated friendship relations are used in forwarding of messages. Extensive simulations on both real and synthetic traces show that the introduced algorithm is more efficient than the existing algorithms.


A Mapping Study to Investigate Spam Detection on Social Networks


title: On the pursue for a Fair Internet Regulation System: A blueprint for a content blocking system encouraging participation by the Internet users

abstract: This paper describes a blueprint for the proper design of a Fair Internet Regulation System (IRS), i.e. a system that will be implemented in national level and will encourage the participation of Internet users in enriching and correcting its “behavior”. Such a system will be easier to be accepted by Western democracies willing to implement a fair Internet regulation policy.


title: Learning discriminative binary codes for finger vein recognition

abstract: Finger vein recognition has drawn increasing attention from biometrics community due to its security and convenience. In this paper, a novel discriminative binary codes (DBC) learning method is proposed for finger vein recognition. First of all, subject relation graph is built to capture correlations among subjects. Based on the relation graph, binary templates are transformed to describe vein characteristics of subjects. To ensure that templates are discriminative and representative, graph transform is formulated into an optimization problem, in which the distance between templates from different subjects is maximized and templates provide maximum information about subjects. At last, supervised information for training instances is provided by the obtained binary templates, and SVMs are trained as the code learner for each bit. Compared with existing binary codes for finger vein recognition, DBC are more discriminative and shorter. In addition, they are generated with considering the relationships among subjects which may be useful to improve performance. Experimental results on PolyU database and MLA database demonstrate the effectiveness and efficiency of DBC for finger vein recognition and retrieval. A novel discriminative binary code learning method is proposed.Maximizing inter-class scatter and entropy is proposed to make codes discriminative.Proposed codes consider relationship among subjects due to use of subject graph.Length of proposed binary codes is smaller due to small size of relation graph.


title: Benefits of an active spine supported bounding locomotion with a small compliant quadruped robot

abstract: We studied the effect of the control of an active spine versus a fixed spine, on a quadruped robot running in bound gait. Active spine supported actuation led to faster locomotion, with less foot sliding on the ground, and a higher stability to go straight forward. However, we did no observe an improvement of cost of transport of the spine-actuated, faster robot system compared to the rigid spine.


title: High annoyance urban noise masking

abstract: Urban noise pollution is a growing concern worldwide as a cost of rapid economic growth and development. It affects and harms the human activity and health which brings serious annoyance to daily life. Noise masking technology is one of the efficient solutions to reduce the noise-induced annoyance. For this purpose, we conduct a subjective listening test to examine the masking capability of different masker sounds for different types of noise in Singapore. Acoustical characteristics of these maskers are studies with regarding to their performance based on subjective listening test. The results show that, for high sound pressure level noise, water sound may not be the dominant preferred masker for different urban noises.


title: Supporting Business Process Improvement through Business Process Weakness Pattern Collections

abstract: A main task of Business Process Management is to identify weak- nesses in business processes that may result in monetary or quality-related drawbacks in order to eliminate them subsequently. A common way to identify weaknesses is to analyze process models. While this has traditionally been done manually, recent work proposes using automatic model query approaches prom- ising time and money savings. Query approaches take (weakness) patterns as input and return all process model subsections that match these patterns, hence may be subject to weaknesses. Although numerous model query approaches have been developed, collections of weaknesses do virtually not exist. To ex- ploit the benefits of model querying, a weakness collection would be highly de- sirable. In this paper, we provide a first version of a weakness pattern collec- tion, which we identified in an empirical study on several hundreds of weak- ness-afflicted process models and assess its usefulness through applying it to another process model collection.


title: On-line Mobile Robot Model Identification using Integrated Perturbative Dynamics

abstract: We present an approach to the problem of real-time identification of vehicle motion models based on fitting, on a continuous basis, parametrized slip models to observed behavior. Our approach is unique in that we generate parametric models capturing the dynamics of systematic error (i.e. slip) and then predict trajectories for arbitrary inputs on arbitrary terrain. The integrated error dynamics are linearized with respect to the unknown parameters to produce an observer relating errors in predicted slip to errors in the parameters. An Extended Kalman filter is used to identify this model on-line. The filter forms innovations based on residual differences between the motion originally predicted using the present model and the motion ultimately experienced by the vehicle. Our results show that the models converge in a few seconds and they reduce prediction error for even benign maneuvers where errors might be expected to be small already. Results are presented for both a skid-steered and an Ackerman steer vehicle.


title: A Self-Timed Ring Based True Random Number Generator

abstract: Self-timed rings are oscillators in which several events can evolve evenly-spaced in time thanks to analog effects inherent to the ring stage structure. One of their interesting features is that they provide precise high-speed multiphase signals. This paper presents a true random number generator that exploits the jitter of events propagating in a self-timed ring with a high entropy. Designs implemented in Alter a Cyclone III and Xilinx Virtex 5 devices provide high quality random bit sequNIST SP 800-22 statistical testsences passing FIPS 140-1 and NIST SP 800-22 statistical tests at a high bit rate.


title: Are Classification Criteria for IgG4-RD Now Possible? The Concept of IgG4-Related Disease and Proposal of Comprehensive Diagnostic Criteria in Japan

abstract: Recent studies suggest simultaneous or metachronous lesions in multiorgans characterized by elevated serum levels of IgG4 and abundant infiltration of IgG4-positive plasma cells with various degrees of fibrosis. Two Japanese research committees for IgG4-RD, one from fibrosclerosis (Okazaki team) and the other from lymph proliferation (Umehara team) supported by the “Research Program for Intractable Disease” of the Ministry of Health, Labor, and Welfare of Japan, have agreed with the unified nomenclature as “IgG4-RD” and proposed the comprehensive diagnostic criteria (CDC) for IgG4-RD. Validation of the CDC demonstrated satisfactory sensitivity for the practical use of general physicians and nonspecialists but low sensitivity in the organs to be difficult in taking biopsy specimens such as type1 autoimmune pancreatitis (IgG4-related AIP), compared with IgG4-related sialadenitis/dacryoadenitis (Mikulicz's disease) and IgG4-related kidney disease. Although the diagnostic criteria covering all IgG4-RD are hard to be established, combination with the CDC and organ-specific diagnostic criteria should improve sensitivity.


title: Exploring Feature Reuse in DenseNet Architectures

abstract: Densely Connected Convolutional Networks (DenseNets) have been shown to achieve state-of-the-art results on image classification tasks while using fewer parameters and computation than competing methods. Since each layer in this architecture has full access to the feature maps of all previous layers, the network is freed from the burden of having to relearn previously useful features, thus alleviating issues with vanishing gradients. In this work we explore the question: To what extent is it necessary to connect to all previous layers in order to reap the benefits of feature reuse? To this end, we introduce the notion of local dense connectivity and present evidence that less connectivity, allowing for increased growth rate at a fixed network capacity, can achieve a more efficient reuse of features and lead to higher accuracy in dense architectures.


Iron Deficiency Anemia (IDA): A Review


title: Performance analysis of graphene based transistors: Modelling and simulation

abstract: Graphene Nanoribbon FETs and Graphene Nanoribbon Tunnel FETs channel current models are simulated in MATLAB followed by implementation in PSpice as equivalent circuit models. Several characteristics of these transistors are compared to those of MOSFETs, keeping identical gate length of 20 nm for performance analysis. Based on the simulation results, it is found that graphene based transistors exhibit superior performance compared to MOSFETs.


title: Augmenting Human Intellect and Amplifying Perception and Cognition

abstract: This first installment of the new Human Augmentation department looks at various technologies designed to augment the human intellect and amplify human perception and cognition. Linking back to early work in interactive computing, Albrecht Schmidt considers how novel technologies can create a new relationship between digital technologies and humans.


title: Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

abstract: Background - The process of generating raw genome sequence data continues to become cheaper, faster, and more accurate. However, assembly of such data into high-quality, finished genome sequences remains challenging. Many genome assembly tools are available, but they differ greatly in terms of their performance (speed, scalability, hardware requirements, acceptance of newer read technologies) and in their final output (composition of assembled sequence). More importantly, it remains largely unclear how to best assess the quality of assembled genome sequences. The Assemblathon competitions are intended to assess current state-of-the-art methods in genome assembly. Results - In Assemblathon 2, we provided a variety of sequence data to be assembled for three vertebrate species (a bird, a fish, and snake). This resulted in a total of 43 submitted assemblies from 21 participating teams. We evaluated these assemblies using a combination of optical map data, Fosmid sequences, and several statistical methods. From over 100 different metrics, we chose ten key measures by which to assess the overall quality of the assemblies. Conclusions - Many current genome assemblers produced useful assemblies, containing a significant representation of their genes, regulatory sequences, and overall genome structure. However, the high degree of variability between the entries suggests that there is still much room for improvement in the field of genome assembly and that approaches which work well in assembling the genome of one species may not necessarily work well for another.


title: Automatic table ground truth generation and a background-analysis-based table structure extraction method

abstract: We first describe an automatic table ground truth generation system which can efficiently generate a large amount of accurate table ground truth suitable for the development of table detection algorithms. Then a novel background analysis-based, coarse-to-fine table identification algorithm and an X-Y cut table decomposition algorithm are described. We discuss an experimental protocol to evaluate the table detection algorithms. For a total of 1,125 document pages having 518 table entities and a total of 10,941 cell entities, our table detection algorithm takes line, word segmentation results as input and obtains around 90% cell correct detection rates.


title: Three-level boost converters. Modes, sub-modes and asymmetrical regime of operation

abstract: A growing need for more efficient generation, distribution and conversion energy intensively drives exploration of new power converters topologies. The paper compares key design parameters of multilevel and three-level boost converters based on fly capacitor and clamping diodes. Main attention has been paid to three-level boost converter with clamping diode structures along with features under operation in various modes and sub-modes in static and dynamics as well. A theoretical analysis has been confirmed by large number modeling and experiments.


title: Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization

abstract: Online customer reviews is considered as a significant informative resource which is useful for both potential customers and product manufacturers. In web pages, the reviews are written in natural language and are unstructured-free-texts scheme. The task of manually scanning through large amounts of review one by one is computational burden and is not practically implemented with respect to businesses and customer perspectives. Therefore it is more efficient to automatically process the various reviews and provide the necessary information in a suitable form. The high-level problem of opinion summarization addresses how to determine the sentiment, attitude or opinion that an author expressed in natural language text with respect to a certain feature. In this paper, we dedicate our work to the main subtask of opinion summarization. The task of product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the performance of opinion orientation identification. It is important to properly identify the semantic relationships between product features and opinions. We proposed an approach for mining product feature and opinion based on the consideration of syntactic information and semantic information. By applying dependency relations and ontological knowledge with probabilistic based model, the result of our experiments shows that our approach is more flexible and effective.


title: Handwriting Profiling using Generative Adversarial Networks

abstract: Handwriting is a skill learned by humans from a very early age. The ability to develop one's own unique handwriting as well as mimic another person's handwriting is a task learned by the brain with practice. This paper deals with this very problem where an intelligent system tries to learn the handwriting of an entity using Generative Adversarial Networks (GANs). We propose a modified architecture of DCGAN (Radford, Metz, and Chintala 2015) to achieve this. We also discuss about applying reinforcement learning techniques to achieve faster learning. Our algorithm hopes to give new insights in this area and its uses include identification of forged documents, signature verification, computer generated art, digitization of documents among others. Our early implementation of the algorithm illustrates a good performance with MNIST datasets.


title: Cloud computing for education and learning: Education and learning as a service (ELaaS)

abstract: Cloud computing, despite its hype, is being widely deployed, with its dynamic scalability and usage of virtualized resources, in many organizations for several applications. It is envisioned that, in the near future, cloud computing will have a significant impact on the educational and learning environment, enabling their own users (i.e., learners, instructors, and administrators) to perform their tasks effectively with less cost by utilizing the available cloud-based applications offered by the cloud service providers. This paper discusses the use of cloud computing in the educational and learning arena, to be called “Education and Learning as a Service” (ELaaS), emphasizing its possible benefits and offerings. It is essential for an educational and learning organization, with its budget restrictions and sustainability challenges, to use the cloud formation best suited for a particular IT activity. The Jericho Forum proposes a cloud computing formation model, called the Cloud Cube Model (CCM), which is based on 4 criteria. To preserve the symmetry of the cube, a new cloud computing formation model, called the Complete Cloud Computing Formations (C3F), is proposed. The IT activities in the educational and learning organizations are then classified with respect to the two criteria: mission criticality and sensitivity. Each class is then mapped into the appropriate position in the C3F, creating ELaaS Quadrant. This essentially establishes a general conceptual framework for ELaaS.


title: Hybrid simulation for modelling large systems: an example of integrated care model

abstract: Developing models for large systems is not a trivial task. Using only Discrete Event Simulation (DES) as a modelling technique may mean that the complexity of the underlying model will increase exponentially with the size of the model. An alternative to this is the use of System Dynamics (SD) for modeling large systems using the positive and negative feedback loops. However, for modelling a human-centric system like healthcare, DES is important as it provides individuality analysis; similarly, SD is important as it facilitates the whole systems approach. The combined application of OR/Simulation methods enable the symbiotic realization of the strengths of individual techniques, while reducing their limitations; in this paper it is suggested that a combined SD-DES approach (also referred to as hybrid technique) can be effectively used for modelling large systems. The example being used in this context is the modelling of an Integrated Care (IC) system in healthcare.


title: Detecting and Extracting Events from Text Documents

abstract: Events of various kinds are mentioned and discussed in text documents, whether they are books, news articles, blogs or microblog feeds. The paper starts by giving an overview of how events are treated in linguistics and philosophy. We follow this discussion by surveying how events and associated information are handled in computationally. In particular, we look at how textual documents can be mined to extract events and ancillary information. These days, it is mostly through the application of various machine learning techniques. We also discuss applications of event detection and extraction systems, particularly in summarization, in the medical domain and in the context of Twitter posts. We end the paper with a discussion of challenges and future directions.


title: Code Randomization: Haven’t We Solved This Problem Yet?

abstract: Two decades since the idea of using software diversity for security was put forward, ASLR is the only technique to see widespread deployment. This is puzzling since academic security researchers have published scores of papers claiming to advance the state of the art in the area of code randomization. Unfortunately, these improved diversity techniques are generally less deployable than integrity-based techniques, such as control-flow integrity, due to their limited compatibility with existing optimization, development, and distribution practices. This paper contributes yet another diversity technique called pagerando. Rather than trading off practicality for security, we first and foremost aim for deployability and interoperability. Most code randomization techniques interfere with memory sharing and deduplication optimization across processes and virtual machines, ours does not. We randomize at the granularity of individual code pages but never rewrite page contents. This also avoids incompatibilities with code integrity mechanisms that only allow signed code to be mapped into memory and prevent any subsequent changes. On Android, pagerando fully adheres to the default SELinux policies. All practical mitigations must interoperate with unprotected legacy code, our implementation transparently interoperates with unmodified applications and libraries. To support our claims of practicality, we demonstrate that our technique can be integrated into and protect all shared libraries shipped with stock Android 6.0. We also consider hardening of non-shared libraries and executables and other concerns that must be addressed to put software diversity defenses on par with integrity-based mitigations such as CFI.


title: Bias in random forest variable importance measures: Illustrations, sources and a solution

abstract: Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories. Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand. We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and documented thoroughly in an application re-analyzing data from a study on RNA editing. Therefore the suggested method can be applied straightforwardly by scientists in bioinformatics research.


title: Design for emotion detection of punjabi text using hybrid approach

abstract: In the era of autonomous technology, Emotion detection is a growing research area to develop an autonomous system for the detection of human emotions. There is the existence of various kinds of media on internet for articulation of human emotions like speech, images, face expressions, text etc. One of the ubiquitous sources of communication on internet is in the form of textual reviews, comments and other informational data. This textual data can be present in any of the language. The presence of data on internet is mainly in English language that lacks the other regional languages like Punjabi, Bengali, Telugu etc. In this paper, we are considering Punjabi language based textual data for emotion detection using hybrid concept of Keyword Based Approach and Machine Learning Approach. For keyword based approach, we have considered Rule Based Engine which detect whether the emotion is present in the input dataset or not. For machine learning approach, we considered Support Vector Machine (SVM) & Naive Bayes (NB) as classifier to detect the Ekman's six types of basic emotions (happy, fear, anger, sadness, disgust and surprise). For this experimentation, Punjabi textual dataset HC Corpora is used which consists of online Punjabi websites, Punjabi newspaper news, blogs etc. The proposed concept is implemented in python and dataset files are considered as the Unicode format.


title: Local food and tourism: An entrepreneurial network approach

abstract: ABSTRACTThis article investigates the question: Why local food networks succeed or fail in collaborating with local tourism actors to create more tourism based on local food? The article focuses on entrepreneurial local food networks and their collaboration with local tourism actors. Emphasis is on the actions and attitude logics of local food networks and tourism actors, and whether their respective logics fit as a factor to explain why or why not development of local food concepts lead to increased local tourism. Six local food networks and their collaboration with local tourism actors are studied by using observation supplemented with other qualitative methods. Analysis of these networks reveals that successful collaboration is characterised by the food networks and tourism actors having at least one logic in common. The fitting logics that lead to success are primarily celebrity and civic logics, which emphasises the community aspect (i.e. that both parties want to support the local community and bran...


title: AquaLog: An Ontology-Portable Question Answering System for the Semantic Web

abstract: As semantic markup becomes ubiquitous, it will become important to be able to ask queries and obtain answers, using natural language (NL) expressions, rather than the keyword-based retrieval mechanisms used by the current search engines. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input and returns answers drawn from the available semantic markup. We say that AquaLog is portable, because the configuration time required to customize the system for a particular ontology is negligible. AquaLog combines several powerful techniques in a novel way to make sense of NL queries and to map them to semantic markup. Moreover it also includes a learning component, which ensures that the performance of the system improves over time, in response to the particular community jargon used by the end users. In this paper we describe the current version of the system, in particular discussing its portability, its reasoning capabilities, and its learning mechanism.


title: Traffic engineering in software defined networks

abstract: Software Defined Networking is a new networking paradigm that separates the network control plane from the packet forwarding plane and provides applications with an abstracted centralized view of the distributed network state. A logically centralized controller that has a global network view is responsible for all the control decisions and it communicates with the network-wide distributed forwarding elements via standardized interfaces. Google recently announced [5] that it is using a Software Defined Network (SDN) to interconnect its data centers due to the ease, efficiency and flexibility in performing traffic engineering functions. It expects the SDN architecture to result in better network capacity utilization and improved delay and loss performance. The contribution of this paper is on the effective use of SDNs for traffic engineering especially when SDNs are incrementally introduced into an existing network. In particular, we show how to leverage the centralized controller to get significant improvements in network utilization as well as to reduce packet losses and delays. We show that these improvements are possible even in cases where there is only a partial deployment of SDN capability in a network. We formulate the SDN controller's optimization problem for traffic engineering with partial deployment and develop fast Fully Polynomial Time Approximation Schemes (FPTAS) for solving these problems. We show, by both analysis and ns-2 simulations, the performance gains that are achievable using these algorithms even with an incrementally deployed SDN.


title: Catch Me if You Can: An Abnormality Detection Approach for Collaborative Spectrum Sensing in Cognitive Radio Networks

abstract: Collaborative spectrum sensing is subject to the attack of malicious secondary user(s), which may send false reports. Therefore, it is necessary to detect potential attacker(s) and then exclude the attacker's report for spectrum sensing. Many existing attacker-detection schemes are based on the knowledge of the attacker's strategy and thus apply the Bayesian attacker detection. However, in practical cognitive radio systems the data fusion center typically does not know the attacker's strategy. To alleviate the problem of the unknown strategy of attacker(s), an abnormality-detection approach, based on the abnormality detection in data mining, is proposed. The performance of the attacker detection in the single-attacker scenario is analyzed explicitly. For the case in which the attacker does not know the reports of honest secondary users (called independent attack), it is shown that the attacker can always be detected as the number of spectrum sensing rounds tends to infinity. For the case in which the attacker knows all the reports of other secondary users, based on which the attacker sends its report (called dependent attack), an approach for the attacker to perfectly avoid being detected is found, provided that the attacker has perfect information about the miss-detection and false-alarm probabilities. This motivates cognitive radio networks to protect the reports of secondary users. The performance of attacker detection in the general case of multiple attackers is demonstrated using numerical simulations.


title: Implicit Dimension Identification in User-Generated Text with LSTM Networks

abstract: Abstract In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative. It is hard to manually detect these implicit beliefs, intentions and moral foundations of the writers. We study and investigate two different tasks, each of which reflect the difficulty of detecting an implicit user’s knowledge, intent or belief that may be based on writer’s moral foundation: (1) political perspective detection in news articles (2) identification of informational vs. conversational questions in community question answering (CQA) archives. In both tasks we first describe new interesting annotated datasets and make the datasets publicly available. Second, we compare various classification algorithms, and show the differences in their performance on both tasks. Third, in political perspective detection task we utilize a narrative representation language of local press to identify perspective differences between presumably neutral American and British press.


title: Computation Error Analysis of Block Floating Point Arithmetic Oriented Convolution Neural Network Accelerator Design

abstract: The heavy burdens of computation and off-chip traffic impede deploying the large scale convolution neural network on embedded platforms. As CNN is attributed to the strong endurance to computation errors, employing block floating point (BFP) arithmetics in CNN accelerators could save the hardware cost and data traffics efficiently, while maintaining the classification accuracy. In this paper, we verify the effects of word width definitions in BFP to the CNN performance without retraining. Several typical CNN models, including VGG16, ResNet-18, ResNet-50 and GoogLeNet, were tested in this paper. Experiments revealed that 8-bit mantissa, including sign bit, in BFP representation merely induced less than 0.3% accuracy loss. In addition, we investigate the computational errors in theory and develop the noise-to-signal ratio (NSR) upper bound, which provides the promising guidance for BFP based CNN engine design.


title: The generalized-Euler-constant function γ(z) and a generalization of Somos’s quadratic recurrence constant

abstract: Abstract We define the generalized-Euler-constant function γ ( z ) = ∑ n = 1 ∞ z n − 1 ( 1 n − log n + 1 n ) when | z | ⩽ 1 . Its values include both Euler's constant γ = γ ( 1 ) and the “alternating Euler constant” log 4 π = γ ( − 1 ) . We extend Euler's two zeta-function series for γ to polylogarithm series for γ ( z ) . Integrals for γ ( z ) provide its analytic continuation to C − [ 1 , ∞ ) . We prove several other formulas for γ ( z ) , including two functional equations; one is an inversion relation between γ ( z ) and γ ( 1 / z ) . We generalize Somos's quadratic recurrence constant and sequence to cubic and other degrees, give asymptotic estimates, and show relations to γ ( z ) and to an infinite nested radical due to Ramanujan. We calculate γ ( z ) and γ ′ ( z ) at roots of unity; in particular, γ ′ ( − 1 ) involves the Glaisher–Kinkelin constant A. Several related series, infinite products, and double integrals are evaluated. The methods used involve the Kinkelin–Bendersky hyperfactorial K function, the Weierstrass products for the gamma and Barnes G functions, and Jonquiere's relation for the polylogarithm.


title: Text summarization using a trainable summarizer and latent semantic analysis q

abstract: This paper proposes two approaches to address text summarization: modified corpus-based approach (MCBA) and LSA-based T.R.M. approach (LSA + T.R.M.). The first is a trainable summarizer, which takes into account several features, including position, positive keyword, negative keyword, centrality, and the resemblance to the title, to generate summaries. Two new ideas are exploited: (1) sentence positions are ranked to emphasize the significances of different sentence positions, and (2) the score function is trained by the genetic algorithm (GA) to obtain a suitable combination of feature weights. The second uses latent semantic analysis (LSA) to derive the semantic matrix of a document or a corpus and uses semantic sentence representation to construct a semantic text relationship map. We evaluate LSA + T.R.M. both with single documents and at the corpus level to investigate the competence of LSA in text summarization. The two novel approaches were measured at several compression rates on a data corpus composed of 100 political articles. When the compression rate was 30%, an average f-measure of 49% for MCBA, 52% for MCBA + GA, 44% and 40% for LSA + T.R.M. in single-document and corpus level were achieved respectively.


title: Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution

abstract: Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.


title: Phrase Localization and Visual Relationship Detection with Comprehensive Image-Language Cues

abstract: This paper presents a framework for localization or grounding of phrases in images using a large collection of linguistic and visual cues. We model the appearance, size, and position of entity bounding boxes, adjectives that contain attribute information, and spatial relationships between pairs of entities connected by verbs or prepositions. Special attention is given to relationships between people and clothing or body part mentions, as they are useful for distinguishing individuals. We automatically learn weights for combining these cues and at test time, perform joint inference over all phrases in a caption. The resulting system produces state of the art performance on phrase localization on the Flickr30k Entities dataset and visual relationship detection on the Stanford VRD dataset.


title: [Leaching characteristics of heavy metals in MSW fly ash under different condition].

abstract: The leaching characteristic of heavy metals in MSW fly ash was studied under acidic and alkaline condition. The result showed the leaching characteristic of Pb, Zn, Ni, Cr and Hg had similar law, which was these heavy metals could leach out under acidic condition and alkaline condition (pH > 12), and the leaching amount increased with the leachate density or L/S increasing. For example, under acidic and alkaline condition, the leaching amount of Pb increased from 0.457 mg/kg, 51.142 mg/kg to 104.576 mg/kg, 59.692 mg/kg respectively when L/S increased from 20 to 40. Cd and Cu could leaching out only under acidic condition. Under acidic condition the leaching rate of Cd, Pb and Zn were higher then the others.


title: No Evidence from Genome-Wide Data of a Khazar Origin for the Ashkenazi Jews

abstract: Abstract The origin and history of the Ashkenazi Jewish population have long been of great interest, and advances in high-throughput genetic analysis have recently provided a new approach for investigating these topics. We and others have argued on the basis of genome-wide data that the Ashkenazi Jewish population derives its ancestry from a combination of sources tracing to both Europe and the Middle East. It has been claimed, however, through a reanalysis of some of our data, that a large part of the ancestry of the Ashkenazi population originates with the Khazars, a Turkic-speaking group that lived to the north of the Caucasus region ∼1,000 years ago. Because the Khazar population has left no obvious modern descendants that could enable a clear test for a contribution to Ashkenazi Jewish ancestry, the Khazar hypothesis has been difficult to examine using genetics. Furthermore, because only limited genetic data have been available from the Caucasus region, and because these data have been concentrated i...


title: MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features

abstract: In this work, we tackle the problem of instance segmentation, the task of simultaneously solving object detection and semantic segmentation. Towards this goal, we present a model, called MaskLab, which produces three outputs: box detection, semantic segmentation, and direction prediction. Building on top of the Faster-RCNN object detector, the predicted boxes provide accurate localization of object instances. Within each region of interest, MaskLab performs foreground/background segmentation by combining semantic and direction prediction. Semantic segmentation assists the model in distinguishing between objects of different semantic classes including background, while the direction prediction, estimating each pixel's direction towards its corresponding center, allows separating instances of the same semantic class. Moreover, we explore the effect of incorporating recent successful methods from both segmentation and detection (i.e. atrous convolution and hypercolumn). Our proposed model is evaluated on the COCO instance segmentation benchmark and shows comparable performance with other state-of-art models.


title: A Survey of Dictionary Learning Algorithms for Face Recognition

abstract: During the past several years, as one of the most successful applications of sparse coding and dictionary learning, dictionary-based face recognition has received significant attention. Although some surveys of sparse coding and dictionary learning have been reported, there is no specialized survey concerning dictionary learning algorithms for face recognition. This paper provides a survey of dictionary learning algorithms for face recognition. To provide a comprehensive overview, we not only categorize existing dictionary learning algorithms for face recognition but also present details of each category. Since the number of atoms has an important impact on classification performance, we also review the algorithms for selecting the number of atoms. Specifically, we select six typical dictionary learning algorithms with different numbers of atoms to perform experiments on face databases. In summary, this paper provides a broad view of dictionary learning algorithms for face recognition and advances study in this field. It is very useful for readers to understand the profiles of this subject and to grasp the theoretical rationales and potentials as well as their applicability to different cases of face recognition.


title: CaveUDK: a VR game engine middleware

abstract: Previous attempts at developing immersive versions of game engines have faced difficulties in achieving both overall high performance and preserving reusability of software developments. In this paper, we present a high-level VR middleware based on one of the most successful commercial game engines: the Unreal® Engine 3.0 (UE3). We describe a VR framework implemented as an extension to the Unreal® Development Kit (UDK) supporting CAVE"-like installations. Our approach relies on a distributed architecture reinforced by specific replication patterns to synchronize the user's point of view and interactions within a multi-screen installation. Our performance benchmarks indicated that our immersive port does not affect the game engine performance, even with complex real-time applications, such as fast-paced multiplayer First Person Shooter (FPS) games or high-resolution graphical environments with 2M+ polygons. A user study also demonstrated the capacity of our VR middleware to elicit high spatial presence while maintaining low cybersickness effects. With free distribution, we believe such a platform can support future Entertainment and VR research.


title: A cognitive approach to identifying measurable milestones for programming skill acquisition

abstract: Traditional approaches to programming education, as exemplified by the typical CS1/CS2 course sequence, have not taken advantage of the long record of psychological and experimental studies on the development of programming skills. These studies indicate a need for a new curricular strategy for developing programming skills and indicate that a cognitive approach would be a promising starting point. This paper first reviews the literature on studies of programming skills, cognition and learning, then within that context reports on a new formal structure, called an anchor graph, that supports curricular design and facilitates the setting of measurable milestones.


title: Adaptive Web Sites: an AI Challenge

abstract: The creation of a complex web site is a thorny problem in user interface design. First, different visitors have distinct goals. Second, even a single visitor may have different needs at different times. Much of the information at the site may also be dynamic or time-dependent. Third, as the site grows and evolves, its original design may no longer be appropriate. Finally, a site may be designed for a particular purpose but used in unexpected ways. ::: ::: Web servers record data about user interactions and accumulate this data over time. We believe that AI techniques can be used to examine user access logs in order to automatically improve the site. We challenge the AI community to create adaptive web sites: sites that automatically improve their organization and presentation based on user access data. ::: ::: Several unrelated research projects in plan recognition, machine learning, knowledge representation, and user modeling have begun to explore aspects of this problem. We hope that posing this challenge explicitly will bring these projects together and stimulate fundamental AI research. Success would have a broad and highly visible impact on the web and the AI community.


title: Ontology-driven geographic information systems

abstract: Information integration is the combination of different types of information in a framework so that it can be queried, retrieved, and manipulated. Integration of geographic data has gained in importance because of the new possibilities arising from the interconnected world and the increasing availability of geographic information. Many times the need for information is so pressing that it does not matter if some details are lost, as long as integration is achieved. To integrate information across computerized information systems it is necessary first to have explicit formalizations of the mental concepts that people have about the real world. Furthermore, these concepts need to be grouped by communities in order to capture the basic agreements that exist within different communities. The explicit formalization of the mental models within a community is an ontology. ::: This thesis introduces a framework for the integration of geographic information. We use ontologies as the foundation of this framework. By integrating ontologies that are linked to sources of geographic information we allow for the integration of geographic information based primarily on its meaning. Since the integration may occurs across different levels, we also create the basic mechanisms for enabling integration across different levels of detail. The use of an ontology, translated into an active, information-system component, leads Ontology-Driven Geographic Information Systems. ::: The results of this thesis show that a model that incorporates hierarchies and roles has the potential to integrate more information than models that do not incorporate these concepts. We developed a methodology to evaluate the influence of the use of roles and of hierarchical structures for representing ontologies on the potential for information integration. The use of a hierarchical structure increases the potential for information integration. The use of roles also improves the potential for information integration, although to a much lesser extent than did the use of hierarchies. The combined effect of roles and hierarchies had a more positive effect in the potential for information integration than the use of roles alone or hierarchies alone. These three combinations (hierarchies, roles, roles and hiearchies) gave better results than the results using neither roles nor hierarchies.


title: Predicting trend in the next-day market by Hierarchical Hidden Markov Model

abstract: The prediction of stock market movements represents a key component in developing winning trading strategies. Forecasting can have different time horizon. In this paper we focus our attention to very short term, and we develop a model able to predict market trends with a horizon of few days ahead. Based on Hierarchical Hidden Markov Model, our approach has been tested against the Indian stock market index (Nifty), and experimental results are presented and discussed.


title: Momentum-based Balance Control for Torque-controlled Humanoids

abstract: Recently several hierarchical inverse dynamics controllers based on cascades of quadratic programs have been proposed for application on torque controlled robots. They have important theoretical benefits but have never been implemented on a torque controlled robot where model inaccuracies and real-time computation requirements can be problematic. In this contribution we present an experimental evaluation of these algorithms in the context of balance control for a humanoid robot. The presented experiments demonstrate the applicability of the approach under real robot conditions (i.e. model uncertainty, estimation errors, etc). We propose a simplification of the optimization problem that allows us to decrease computation time enough to implement it in a fast torque control loop. We implement a momentum-based balance controller which shows robust performance in face of unknown disturbances, even when the robot is standing on only one foot. In a second experiment, a tracking task is evaluated to demonstrate the performance of the controller with more complicated hierarchies. Our results show that hierarchical inverse dynamics controllers can be used for feedback control of humanoid robots and that momentum-based balance control can be efficiently implemented on a real robot.


title: Position-based rigid-body dynamics

abstract: We propose a position-based approach for large-scale simulations of rigid bodies at interactive frame rates. Our method solves positional constraints between rigid bodies and can therefore be seamlessly integrated into other position-based methods. Interaction of particles and rigid bodies through common constraints enables two-way coupling with deformables. The method exhibits exceptional performance and stability while being user controllable and easy to implement. Various results demonstrate the practicability of our method for the resolution of collisions, contacts, stacking and joint constraints. Copyright © 2014 John Wiley & Sons, Ltd.


title: A MOS Image Sensor With a Digital-Microlens

abstract: We have developed a MOS image sensor with digital-microlenses (DMLs), each of which has an effective refractive index realized by variation of the subwavelength separations between the concentric SiO2 ring walls. The effective refractive index profiles are optimized for the location of each pixel. The light-collection efficiency of the image sensor is twice as high as that of a conventional image sensor because of the enhanced light acceptance in the periphery. A 2.2-mum pitch 3-megapixel MOS image sensor based on the DML technology exhibited excellent uniformity of the light-collection efficiency across the image area, even for light with a very large incident angle, i.e., over 45deg. The DML promises new levels of performance of image sensors.


title: False rumors detection on Sina Weibo by propagation structures

abstract: This paper studies the problem of automatic detection of false rumors on Sina Weibo, the popular Chinese microblogging social network. Traditional feature-based approaches extract features from the false rumor message, its author, as well as the statistics of its responses to form a flat feature vector. This ignores the propagation structure of the messages and has not achieved very good results. We propose a graph-kernel based hybrid SVM classifier which captures the high-order propagation patterns in addition to semantic features such as topics and sentiments. The new model achieves a classification accuracy of 91.3% on randomly selected Weibo dataset, significantly higher than state-of-the-art approaches. Moreover, our approach can be applied at the early stage of rumor propagation and is 88% confident in detecting an average false rumor just 24 hours after the initial broadcast.


A Survey of Sybil Attacks in Networks 1


title: Lethal Injection for Execution: Chemical Asphyxiation?

abstract: Background Lethal injection for execution was conceived as a comparatively humane alternative to electrocution or cyanide gas. The current protocols are based on one improvised by a medical examiner and an anesthesiologist in Oklahoma and are practiced on an ad hoc basis at the discretion of prison personnel. Each drug used, the ultrashort-acting barbiturate thiopental, the neuromuscular blocker pancuronium bromide, and the electrolyte potassium chloride, was expected to be lethal alone, while the combination was intended to produce anesthesia then death due to respiratory and cardiac arrest. We sought to determine whether the current drug regimen results in death in the manner intended.


title: Anonymity, Intimacy and Self-Disclosure in Social Media

abstract: Self-disclosure is rewarding and provides significant benefits for individuals, but it also involves risks, especially in social media settings. We conducted an online experiment to study the relationship between content intimacy and willingness to self-disclose in social media, and how identification (real name vs. anonymous) and audience type (social ties vs. people nearby) moderate that relationship. Content intimacy is known to regulate self-disclosure in face-to-face communication: people self-disclose less as content intimacy increases. We show that such regulation persists in online social media settings. Further, although anonymity and an audience of social ties are both known to increase self-disclosure, it is unclear whether they (1) increase self-disclosure baseline for content of all intimacy levels, or (2) weaken intimacy's regulation effect, making people more willing to disclose intimate content. We show that intimacy always regulates self-disclosure, regardless of settings. We also show that anonymity mainly increases self-disclosure baseline and (sometimes) weakens the regulation. On the other hand, an audience of social ties increases the baseline but strengthens the regulation. Finally, we demonstrate that anonymity has a more salient effect on content of negative valence.The results are critical to understanding the dynamics and opportunities of self-disclosure in social media services that vary levels of identification and types of audience.


title: Different Arrangements for Dual-Rotor Dual-Output Radial-Flux Motors

abstract: This paper describes different arrangements for a dual-rotor, radial-flux, and permanent-magnet brushless dc motor for application to variable-speed air conditioners. In conventional air conditioners, two motors of appropriate ratings are usually used to drive the condenser and evaporator separately. Alternatively, a motor with two output shafts may be employed, and this is studied here. The motor has inner and outer rotors with a stator in between which is toroidally wound or axially wound with inner and outer slotted stator surfaces. The power sharing on the two rotors is designed to meet the requirement of the condenser and evaporator. Finite element analysis (FEA) is employed to verify the designs. A prototype is made and tested to evaluate the performance. Alternative windings are investigated to assess the possibilities of decoupling the rotors so that they run independently. In the final section, a new and novel arrangement is proposed, where one three-phase winding set and one two-phase winding set (both toroidal) are wound on the same stator to control two rotors of different pole numbers. The two winding sets can be bifilar or share the same set of phase windings. This design simplifies the winding (because it is toroidal) and reduces the copper loss or amount of copper required. The design is tested using FEA solutions, and the initial results indicate that this machine could operate successfully.


title: Two controlled experiments concerning the comparison of pair . . .

abstract: This paper reports on two controlled experiments comparing pair programming with single developers who are assisted by an additional anonymous peer code review phase. The experiments were conducted in the summer semester 2002 and 2003 at the University of Karlsruhe with 38 computer science students. Instead of comparing pair programming to solo programming this study aims at finding a technique by which a single developer produces similar program quality as programmer pairs do but with moderate cost. The study has one major finding concerning the cost of the two development methods. Single developers are as costly as programmer pairs, if both programmer pairs and single developers with an additional review phase are forced to produce programs of similar level of correctness. In conclusion, programmer pairs and single developers become interchangeable in terms of development cost. As this paper reports on the results of small development tasks the comparison could not take into account long time benefits of either technique.


title: An Approach to Data Analysis in 5G Networks

abstract: 5G networks expect to provide significant advances in network management compared to traditional mobile infrastructures by leveraging intelligence capabilities such as data analysis, prediction, pattern recognition and artificial intelligence. The key idea behind these actions is to facilitate the decision-making process in order to solve or mitigate common network problems in a dynamic and proactive way. In this context, this paper presents the design of Self-Organized Network Management in Virtualized and Software Defined Networks (SELFNET) Analyzer Module, which main objective is to identify suspicious or unexpected situations based on metrics provided by different network components and sensors. The SELFNET Analyzer Module provides a modular architecture driven by use cases where analytic functions can be easily extended. This paper also proposes the data specification to define the data inputs to be taking into account in diagnosis process. This data specification has been implemented with different use cases within SELFNET Project, proving its effectiveness.


title: Age differences in dual information-processing modes

abstract: Age differences in affective/experiential and deliberative processes have important theoretical implications for cancer decision making, as cancer is often a disease of older adulthood. The authors examined evidence for adult age differences in affective and deliberative information processes, reviewed the sparse evidence about age differences in decision making, and introduced how dual process theories and their findings might be applied to cancer decision making. Age-related declines in the efficiency of deliberative processes predict poorer-quality decisions as we age, particularly when decisions are unfamiliar and the information is numeric. However, age-related adaptive processes, including an increased focus on emotional goals and greater experience, can influence decision making and potentially offset age-related declines. A better understanding of the mechanisms that underlie cancer decision processes in our aging population should ultimately allow us to help older adults to better help themselves.


title: Automata Guided Hierarchical Reinforcement Learning for Zero-shot Skill Composition

abstract: An obstacle that prevents the wide adoption of (deep) reinforcement learning (RL) in control systems is its need for a large number of interactions with the environment in order to master a skill. The learned skill usually generalizes poorly across domains and re-training is often necessary when presented with a new task. We present a framework that combines techniques in \textit{formal methods} with \textit{hierarchical reinforcement learning} (HRL). The set of techniques we provide allows for the convenient specification of tasks with logical expressions, learns hierarchical policies (meta-controller and low-level controllers) with well-defined intrinsic rewards using any RL methods and is able to construct new skills from existing ones without additional learning. We evaluate the proposed methods in a simple grid world simulation as well as simulation on a Baxter robot.


title: How to Share a Secret

abstract: In this paper we show how to divide data D into n pieces in such a way that D is easily reconstructable from any k pieces, but even complete knowledge of k - 1 pieces reveals absolutely no information about D . This technique enables the construction of robust key management schemes for cryptographic systems that can function securely and reliably even when misfortunes destroy half the pieces and security breaches expose all but one of the remaining pieces.


title: Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks

abstract: Accurate delineation of the left ventricle (LV) is an important step in evaluation of cardiac function. In this paper, we present an automatic method for segmentation of the LV in cardiac CT angiography (CCTA) scans. Segmentation is performed in two stages. First, a bounding box around the LV is detected using a combination of three convolutional neural networks (CNNs). Subsequently, to obtain the segmentation of the LV, voxel classification is performed within the defined bounding box using a CNN. The study included CCTA scans of sixty patients, fifty scans were used to train the CNNs for the LV localization, five scans were used to train LV segmentation and the remaining five scans were used for testing the method. Automatic segmentation resulted in the average Dice coefficient of 0.85 and mean absolute surface distance of 1.1 mm. The results demonstrate that automatic segmentation of the LV in CCTA scans using voxel classification with convolutional neural networks is feasible.


title: Working Paper Number 129 Tax evasion, tax avoidance and development finance

abstract: Domestic revenue mobilisation is key to sustainable development finance ‐ only self-sufficiency will allow the development of fully-functioning states with flourishing systems of political representation and economies reflecting societies’ expressed preferences in regard to, for example, inequality. Tax evasion and tax avoidance are important insofar as they affect both the volume and nature of government finances. This paper estimates the total cost to developing countries of these leakages as US$385 billion annually, dwarfing any potential increase in aid. An additional result suggests that doubling aid to low income countries may have little positive revenue effect but damage the strength of political representation, if full trade liberalisation is simultaneously required.


title: Convolutional neural networks for biomedical text classification: application in indexing biomedical articles

abstract: Building high accuracy text classifiers is an important task in biomedicine given the wealth of information hidden in unstructured narratives such as research articles and clinical documents. Due to large feature spaces, traditionally, discriminative approaches such as logistic regression and support vector machines with n-gram and semantic features (e.g., named entities) have been used for text classification where additional performance gains are typically made through feature selection and ensemble approaches. In this paper, we demonstrate that a more direct approach using convolutional neural networks (CNNs) outperforms several traditional approaches in biomedical text classification with the specific use-case of assigning medical subject headings (or MeSH terms) to biomedical articles. Trained annotators at the national library of medicine (NLM) assign on an average 13 codes to each biomedical article, thus semantically indexing scientific literature to support NLM's PubMed search system. Recent evidence suggests that effective automated efforts for MeSH term assignment start with binary classifiers for each term. In this paper, we use CNNs to build binary text classifiers and achieve an absolute improvement of over 3% in macro F-score over a set of selected hard-to-classify MeSH terms when compared with the best prior results on a public dataset. Additional experiments on 50 high frequency terms in the dataset also show improvements with CNNs. Our results indicate the strong potential of CNNs in biomedical text classification tasks.


title: Boosted Dictionary Learning for Image Compression

abstract: Sparse representations over redundant dictionaries have shown to produce high-quality results in various signal and image processing tasks. Recent advancements in learning-based dictionary design have made image compression using data-adaptive learned dictionaries a promising field. In this paper, we present a boosted dictionary learning framework to construct an ensemble of complementary specialized dictionaries for sparse image representation. Boosted dictionaries along with a competitive sparse coding form our ensemble model which can provide us with more efficient sparse representations. The constituent dictionaries of the ensemble are obtained using a coherence regularized dictionary learning model for which two novel dictionary optimization algorithms are proposed. These algorithms improve the generalization properties of the trained dictionary compared with several incoherent dictionary learning methods. Based on the proposed ensemble model, we then develop a new image compression algorithm using boosted multi-scale dictionaries learned in the wavelet domain. Our algorithm is evaluated for the compression of natural images. Experimental results demonstrate that the proposed algorithm has better rate-distortion performance as compared with several competing compression methods, including analytic and learned dictionary schemes.


title: Efficient AES implementations for ARM based platforms

abstract: The Advanced Encryption Standard (AES) contest, started by the U.S. National Institute of Standards and Technology (NIST), saw the Rijndael [13] algorithm as its winner [11]. Although the AES is fully defined in terms of functionality, it requires best exploitation of architectural parameters in order to reach the optimum performance on specific architectures. Our work concentrates on ARM cores [1] widely used in the embedded industry. Most promising implementation choices for the common ARM Instruction Set Architecture (ISA) are identified, and a new implementation for the linear mixing layer is proposed. The performance improvement over current implementations is demonstrated by a case study on the Intel StrongARM SA-1110 Microprocessor [2]. Further improvements based on exploitation of memory hierarchies are also described, and the corresponding performance figures are presented.


title: Opinion mining in Dutch Hansards

abstract: The question is addressed if opinion mining techniques can be successfully used to automatically retrieve political viewpoints in Dutch parliamentary publications. Two specific tasks are identified: automatically determining subjectivity in the publications and automatically determining the semantic orientation of the subjective parts. A collection of recent parliamentary publications has been collected and a golden standard annotation is created on both subjectivity and orientation. Following this, models based on subjectivity lexicons and machine learning algorithms are evaluated on automatic classification. Overall results tend to be dominated by machine learning algorithms, but methods based on subjectivity lexicons provide promising results. Based on the results it is concluded that opinion mining techniques can indeed be successfully used to automatically retrieve political viewpoints in Dutch parliamentary publications.


title: Deep Speech: Scaling up end-to-end speech recognition

abstract: We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a "phoneme." Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems.


title: CONCEPTS AND APPLICATIONS OF MANAGEMENT INFORMATION SYSTEMS

abstract: Management information system (MIS) is an integrated system for providing information to support the planning, organization, control function That covers the unction’s of middle management from management especial reports. Management information systems(MIS)are generally automated information systems that are used within organizations andincludes allinformationand communicationchannelsthatareorganized. Aninformation systemisAll elements in thecollectionand dissemination ofdata andinformationinvolved, and usuallyinvolvechanges inhardware, software, people, data, andinformationandcommunicationchannels.Operationof the systemincludingdata collection, data processing and conversion ofraw dataintovaluable information, storage and retrieval ofdataandoutputssuch asmanagement reports.


title: The Second National Assessment in Mathematics: Area and Volume.

abstract: Measurement concepts are important in school mathematics not only for providing applications of arithmetic content during the middle years but also for offering a common source of illustrations in algebra and geometry. Even more important, measurement concepts form the basis for all judgment with respect to the size of objects that we encounter daily. One of the topics included in the NAEP second mathematics assessment deals with the ability to use measurement concepts. The results of the items relating to area and volume suggest several common miscon ceptions about measurement among mid dle school and high school students.


title: Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree

abstract: We seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets; they are also easy to implement, and can be applied within various deep neural network architectures. These benefits come with only a light increase in computational overhead during training and a very modest increase in the number of model parameters.


title: Agreement-based learning

abstract: The learning of probabilistic models with many hidden variables and non-decomposable dependencies is an important and challenging problem. In contrast to traditional approaches based on approximate inference in a single intractable model, our approach is to train a set of tractable submodels by encouraging them to agree on the hidden variables. This allows us to capture non-decomposable aspects of the data while still maintaining tractability. We propose an objective function for our approach, derive EM-style algorithms for parameter estimation, and demonstrate their effectiveness on three challenging real-world learning tasks.


title: Privacy Preserving BIRCH Algorithm under Differential Privacy

abstract: Current hierarchical clustering algorithms face the risk of privacy leakage during the clustering process for big dataset. While differential privacy is a relatively recent development in the field of privacy-preserving data mining, offering more robust privacy guarantees. In the paper, BIRCH algorithm under differential privacy is studied and analyzed. Firstly, Diff-BIRCH algorithm which directly add Laplace noise to the dataset before clustering is proposed. Though Diff-BIRCH algorithm achieves privacy protection, clustering result turns to be less available. Since cluster information such as cluster diameter or cluster distance may disclose during clustering process in BIRCH, three improved BIRCH algorithms under differential privacy are then designed aiming at avoiding leakage of the cluster information. Finally, experiment results validate the effectiveness and applicability of the proposed algorithms under the premise of meeting privacy budget.


title: Software-Defined Cloud Computing: Architectural elements and open challenges

abstract: The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.


title: Open Vocabulary Scene Parsing

abstract: Recognizing arbitrary objects in the wild has been a challenging problem due to the limitations of existing classification models and datasets. In this paper, we propose a new task that aims at parsing scenes with a large and open vocabulary, and several evaluation metrics are explored for this problem. Our approach is a joint image pixel and word concept embeddings framework, where word concepts are connected by semantic relations. We validate the open vocabulary prediction ability of our framework on ADE20K dataset which covers a wide variety of scenes and objects. We further explore the trained joint embedding space to show its interpretability.


title: A review of induction motors signature analysis as a medium for faults detection

abstract: This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without "state-of-the-art" terminology for the benefit of practising engineers in facilities today who may not be familiar with signal processing.


title: An Evaluation of Random Testing

abstract: Random testing of programs has usually (but not always) been viewed as a worst case of program testing. Testing strategies that take into account the program structure are generally preferred. Path testing is an often proposed ideal for structural testing. Path testing is treated here as an instance of partition testing, where by partition testing is meant any testing scheme which forces execution of at least one test case from each subset of a partition of the input domain. Simulation results are presented which suggest that random testing may often be more cost effective than partition testing schemes. Also, results of actual random testing experiments are presented which confirm the viability of random testing as a useful validation tool.


title: Carbon nanotube computer

abstract: A computer built entirely using transistors based on carbon nanotubes, which is capable of multitasking and emulating instructions from the MIPS instruction set, is enabled by methods that overcome inherent challenges with this new technology. Carbon nanotubes have long been touted as promising building blocks for computers based on carbon rather than silicon. A main motivation towards this goal is the potential for circuits using carbon nanotube transistors to achieve high energy efficiency. Various carbon nanotube electronic circuit blocks have been demonstrated previously, but Max Shulaker et al. now reach a true milestone in the fields of carbon electronics and nanoelectronics by building a simple but functional computer made entirely from carbon nanotube transistors. Composed of 178 transistors, each containing between 10 and 200 carbon nanotubes, it runs a simple operating system and is capable of multitasking: it performs four tasks (summarized as instruction fetch, data fetch, arithmetic operation and write-back) and can run two different programs concurrently. The miniaturization of electronic devices has been the principal driving force behind the semiconductor industry, and has brought about major improvements in computational power and energy efficiency. Although advances with silicon-based electronics continue to be made, alternative technologies are being explored. Digital circuits based on transistors fabricated from carbon nanotubes (CNTs) have the potential to outperform silicon by improving the energy–delay product, a metric of energy efficiency, by more than an order of magnitude. Hence, CNTs are an exciting complement to existing semiconductor technologies1,2. Owing to substantial fundamental imperfections inherent in CNTs, however, only very basic circuit blocks have been demonstrated. Here we show how these imperfections can be overcome, and demonstrate the first computer built entirely using CNT-based transistors. The CNT computer runs an operating system that is capable of multitasking: as a demonstration, we perform counting and integer-sorting simultaneously. In addition, we implement 20 different instructions from the commercial MIPS instruction set to demonstrate the generality of our CNT computer. This experimental demonstration is the most complex carbon-based electronic system yet realized. It is a considerable advance because CNTs are prominent among a variety of emerging technologies that are being considered for the next generation of highly energy-efficient electronic systems3,4.


title: Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface

abstract: In order to characterize the non-Gaussian information contained within the EEG signals, a new feature extraction method based on bispectrum is proposed and applied to the classification of right and left motor imagery for developing EEG-based brain-computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the BCI 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate.


title: A deep auto-encoder based approach for intrusion detection system

abstract: One of the most challenging problems facing network operators today is network attacks identification due to extensive number of vulnerabilities in computer systems and creativity of attackers. To address this problem, we present a deep learning approach for intrusion detection systems. Our approach uses Deep Auto-Encoder (DAE) as one of the most well-known deep learning models. The proposed DAE model is trained in a greedy layer-wise fashion in order to avoid overfitting and local optima. The experimental results on the KDD-CUP'99 dataset show that our approach provides substantial improvement over other deep learning-based approaches in terms of accuracy, detection rate and false alarm rate.