Do citations and readership identify seminal publications?

This work presents a new approach for analysing the ability of existing research metrics to identify research which has strongly influenced future developments. More specifically, we focus on the ability of citation counts and Mendeley reader counts to distinguish between publications regarded as seminal and publications regarded as literature reviews by field experts. The main motivation behind our research is to gain a better understanding of whether and how well the existing research metrics relate to research quality. For this experiment we have created a new dataset which we call TrueImpactDataset and which contains two types of publications, seminal papers and literature reviews. Using the dataset, we conduct a set of experiments to study how citation and reader counts perform in distinguishing these publication types, following the intuition that causing a change in a field signifies research quality. Our research shows that citation counts work better than a random baseline (by a margin of 10%) in distinguishing important seminal research papers from literature reviews while Mendeley reader counts do not work better than the baseline.


Successful failure: what Foucault can teach us about privacy self-management in a world of Facebook and big data

The “privacy paradox” refers to the discrepancy between the concern individuals express for their privacy and the apparently low value they actually assign to it when they readily trade personal information for low-value goods online. In this paper, I argue that the privacy paradox masks a more important paradox: the self-management model of privacy embedded in notice-and-consent pages on websites and other, analogous practices can be readily shown to underprotect privacy, even in the economic terms favored by its advocates. The real question, then, is why privacy self-management occupies such a prominent position in privacy law and regulation. Borrowing from Foucault’s late writings, I argue that this failure to protect privacy is also a success in ethical subject formation, as it actively pushes privacy norms and practices in a neoliberal direction. In other words, privacy self-management isn’t about protecting people’s privacy; it’s about inculcating the idea that privacy is an individual, commodified good that can be traded for other market goods. Along the way, the self-management regime forces privacy into the market, obstructs the functioning of other, more social, understandings of privacy, and occludes the various ways that individuals attempt to resist adopting the market-based view of themselves and their privacy. Throughout, I use the analytics practices of Facebook and social networking sites as a sustained case study of the point.


Always-ON visual node with a hardware-software event-based binarized neural network inference engine

This work introduces an ultra-low-power visual sensor node coupling event-based binary acquisition with Binarized Neural Networks (BNNs) to deal with the stringent power requirements of always-on vision systems for IoT applications. By exploiting in-sensor mixed-signal processing, an ultra-low-power imager generates a sparse visual signal of binary spatial-gradient features. The sensor output, packed as a stream of events corresponding to the asserted gradient binary values, is transferred to a 4-core processor when the amount of data detected after frame difference surpasses a given threshold. Then, a BNN trained with binary gradients as input runs on the parallel processor if a meaningful activity is detected in a pre-processing stage. During the BNN computation, the proposed Event-based Binarized Neural Network model achieves a system energy saving of 17.8% with respect to a baseline system including a low-power RGB imager and a Binarized Neural Network, while paying a classification performance drop of only 3% for a real-life 3-classes classification scenario. The energy reduction increases up to 8x when considering a long-term always-on monitoring scenario, thanks to the event-driven behavior of the processing sub-system.


Improving privacy and security in multi-authority attribute-based encryption

Attribute based encryption (ABE) [13] determines decryption ability based on a user's attributes. In a multi-authority ABE scheme, multiple attribute-authorities monitor different sets of attributes and issue corresponding decryption keys to users, and encryptors can require that a user obtain keys for appropriate attributes from each authority before decrypting a message. Chase [5] gave a multi-authority ABE scheme using the concepts of a trusted central authority (CA) and global identifiers (GID). However, the CA in that construction has the power to decrypt every ciphertext, which seems somehow contradictory to the original goal of distributing control over many potentially untrusted authorities. Moreover, in that construction, the use of a consistent GID allowed the authorities to combine their information to build a full profile with all of a user's attributes, which unnecessarily compromises the privacy of the user. In this paper, we propose a solution which removes the trusted central authority, and protects the users' privacy by preventing the authorities from pooling their information on particular users, thus making ABE more usable in practice.


Multigate-Cell Stacked FET Design for Millimeter-Wave CMOS Power Amplifiers

To increase the voltage handling capability of scaled CMOS-based circuits, series connection (stacking) of transistors has been demonstrated in recently reported mm-wave power amplifiers. This paper discusses the implementation of stacked CMOS circuits employing a compact, multigate layout technique, rather than the conventional series connection of individual transistors. A unit multigate FET is composed of a single transistor with one source and drain and multiple (four) gate connections. Capacitances are implemented in a distributed manner allowing close proximity to the individual gate fingers using metal layers available within the CMOS back-end-of-line (BEOL) stack. The multigate structure is demonstrated to decrease parasitic resistances and capacitances, and has better layout for heat-sinking. The unit cell is replicated through tiling to implement larger effective gate widths. Millimeter-wave power amplifiers using the multigate-cell are presented which operate over the 25-35 GHz band and achieve 300 mW of saturated output power and peak power-added efficiency (PAE) of 30% in 45 nm CMOS SOI technology. To the authors' knowledge, the output power is the best reported for a single stage CMOS power amplifier that does not use power-combining for this frequency range.


Application of Requirement-oriented Data Quality Evaluation Method

The data quality directly determines the data further analysis and other applications. With the important of data quality, data is a major asset in software applications and information systems. Getting and ensuring high quality data is critical to the software and related business operations. At present, there are many methods and techniques of data quality assessment or evaluation. Based on the evaluation criteria of data quality, the paper mainly starts from the needs of application software and uses the requirements as a guide in the evaluation process. Define evaluation criteria based on user-defined requirements. In this paper, the method proposed in a practical case has been applied, and achieved good practical results.


Real-time neuroevolution in the NERO video game

In most modern video games, character behavior is scripted; no matter how many times the player exploits a weakness, that weakness is never repaired. Yet, if game characters could learn through interacting with the player, behavior could improve as the game is played, keeping it interesting. This paper introduces the real-time Neuroevolution of Augmenting Topologies (rtNEAT) method for evolving increasingly complex artificial neural networks in real time, as a game is being played. The rtNEAT method allows agents to change and improve during the game. In fact, rtNEAT makes possible an entirely new genre of video games in which the player trains a team of agents through a series of customized exercises. To demonstrate this concept, the Neuroevolving Robotic Operatives (NERO) game was built based on rtNEAT. In NERO, the player trains a team of virtual robots for combat against other players' teams. This paper describes results from this novel application of machine learning, and demonstrates that rtNEAT makes possible video games like NERO where agents evolve and adapt in real time. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games.


Concept Mining via Embedding

In this work, we study the problem of concept mining, which serves as the first step in transforming unstructured text into structured information, and supports downstream analytical tasks such as information extraction, organization, recommendation and search. Previous work mainly relies on statistical signals, existing knowledge bases, or predefined linguistic patterns. In this work, we propose a novel approach that mines concepts based on their occurrence contexts, by learning embedding vector representations that summarize the context information for each possible candidates, and use these embeddings to evaluate the concept's global quality and their fitness to each local context. Experiments over several real-world corpora demonstrate the superior performance of our method. A publicly available implementation is provided at https://github.com/kleeeeea/ECON.


WhatsApp Usage Patterns and Prediction Models

This paper presents an extensive study of the usage of the WhatsApp social network, an Internet messaging application that is quickly replacing SMS messaging. It is based on the analysis of over 4 million messages from nearly 100 users that we collected in order to understand people’s use of the network. We believe that this is the first in-depth study of the properties of WhatsApp messages with an emphasis of noting differences across different age and gender demographic groups. It is also the first to use statistical and data analytic tools in this analysis. We found that different genders and age demographics had significantly different usage habits in almost all message and group attributes. These differences facilitate the development of user prediction models based on data mining tools. We illustrate this by developing several prediction models such as for a person’s gender or approximate age. We also noted differences in users’ group behavior. We created group behaviorial models including the likelihood a given group would have more file attachments, if a group would contain a larger number of participants, a higher frequency of activity, quicker response times and shorter messages. We present a detailed discussion about the specific attributes that were contained in all predictive models and suggest possible applications based on these results.


A data mining framework for detecting subscription fraud in telecommunication

Service providing companies including telecommunication companies often receive substantial damage from customers’ fraudulent behaviors. One of the common types of fraud is subscription fraud in which usage type is in contradiction with subscription type. This study aimed at identifying customers’ subscription fraud by employing data mining techniques and adopting knowledge discovery process. To this end, a hybrid approach consisting of preprocessing, clustering, and classification phases was applied, and appropriate tools were employed commensurate to each phase. Specifically, in the clustering phase SOM and K-means were combined, and in the classification phase decision tree (C4.5), neural networks, and support vector machines as single classifiers and bagging, boosting, stacking, majority and consensus voting as ensembles were examined. In addition to using clustering to identify outlier cases, it was also possible – by defining new features – to maintain the results of clustering phase for the classification phase. This, in turn, contributed to better classification results. A real dataset provided by Telecommunication Company of Tehran was applied to demonstrate the effectiveness of the proposed method. The efficient use of synergy among these techniques significantly increased prediction accuracy. The performance of all single and ensemble classifiers is evaluated based on various metrics and compared by statistical tests. The results showed that support vector machines among single classifiers and boosted trees among all classifiers have the best performance in terms of various metrics. The research findings show that the proposed model has a high accuracy, and the resulting outcomes are significant both theoretically and practically. & 2010 Elsevier Ltd. All rights reserved.


FastSpMM: An Efficient Library for Sparse Matrix Matrix Product on GPUs

Sparse matrix matrix (SpMM) multiplication is involved in a wide range of scientific and technical applications. The computational requirements for this kind of operation are enormous, especially for large matrices. This paper analyzes and evaluates a method to efficiently compute the SpMM product in a computing environment that includes graphics processing units (GPUs). Some libraries to compute this matricial operation can be found in the literature. However, our strategy (FastSpMM) outperforms the existing approaches because it combines the use of the ELLPACK-R storage format with the exploitation of the high ratio computation/memory access of the SpMM operation and the overlapping of CPU–GPU communications/computations by Compute Unified Device Architecture streaming computation. In this work, FastSpMM is described and its performance evaluated with regard to the CUSPARSE library (supplied by NVIDIA), which also includes routines to compute SpMM on GPUs. Experimental evaluations based on a representative set of test matrices show that, in terms of performance, FastSpMM outperforms the CUSPARSE routine as well as the implementation of the SpMM as a set of sparse matrix vector products.


Knowledge Management: An Introduction and Perspective

In the mid-1980s, individuals and organizations began to appreciate the increasingly important role of knowledge in the emerging competitive environment. International competition was changing to increasingly emphasize product and service quality, responsiveness, diversity and customization. Some organizations, such as USbased Chaparral Steel, had been pursuing a knowledge focus for some years, but during this period it started to become a more wide-spread business concern. These notions appeared in many places throughout the world – almost simultaneously in the way bubbles appear in a kettle of superheated water! Over a brief period from 1986 to 1989, numerous reports appeared in the public domain concerning how to manage knowledge explicitly. There were studies, results of corporate efforts, and conferences on the topic.


Boosting as a Regularized Path to a Maximum Margin Classifier

In this paper we study boosting methods from a new perspective. We build on recent work by Efron et al. to show that boosting approximately (and in some cases exactly) minimizes its loss criterion with an l1 constraint on the coefficient vector. This helps understand the success of boosting with early stopping as regularized fitting of the loss criterion. For the two most commonly used criteria (exponential and binomial log-likelihood), we further show that as the constraint is relaxed—or equivalently as the boosting iterations proceed—the solution converges (in the separable case) to an “l1-optimal” separating hyper-plane. We prove that this l1-optimal separating hyper-plane has the property of maximizing the minimal l1-margin of the training data, as defined in the boosting literature. An interesting fundamental similarity between boosting and kernel support vector machines emerges, as both can be described as methods for regularized optimization in high-dimensional predictor space, using a computational trick to make the calculation practical, and converging to margin-maximizing solutions. While this statement describes SVMs exactly, it applies to boosting only approximately.


Fault Detection, Isolation, and Service Restoration in Distribution Systems: State-of-the-Art and Future Trends

This paper surveys the conceptual aspects, as well as recent developments in fault detection, isolation, and service restoration (FDIR) following an outage in an electric distribution system. This paper starts with a discussion of the rationale for FDIR, and then investigates different areas of the FDIR problem. Recently reported approaches are compared and related to discussions on current practices. This paper then addresses some of the often-cited associated technical, environmental, and economic challenges of implementing self-healing for the distribution grid. The review concludes by pointing toward the need and directions for future research.


Exploiting potential citation papers in scholarly paper recommendation

To help generate relevant suggestions for researchers, recommendation systems have started to leverage the latent interests in the publication profiles of the researchers themselves. While using such a publication citation network has been shown to enhance performance, the network is often sparse, making recommendation difficult. To alleviate this sparsity, we identify "potential citation papers" through the use of collaborative filtering. Also, as different logical sections of a paper have different significance, as a secondary contribution, we investigate which sections of papers can be leveraged to represent papers effectively.
 On a scholarly paper recommendation dataset, we show that recommendation accuracy significantly outperforms state-of-the-art recommendation baselines as measured by nDCG and MRR, when we discover potential citation papers using imputed similarities via collaborative filtering and represent candidate papers using both the full text and assigning more weight to the conclusion sections.


A COMPARISON OF ENTERPRISE ARCHITECTURE FRAMEWORKS

An Enterprise Architecture Framework (EAF) maps all of the software development processes within the enterprise and how they relate and interact to fulfill the enterprise’s mission. It provides organizations with the ability to understand and analyze weaknesses or inconsistencies to be identified and addressed. There are a number of already established EAF in use today; some of these frameworks were developed for very specific areas, whereas others have broader functionality. This study provides a comparison of several frameworks that can then be used for guidance in the selection of an EAF that meets the needed criteria.


Glove: Global Vectors for Word Representation

Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.


Linguistic Regularities in Continuous Space Word Representations

• Neural network language model and distributed representation for words (Vector representation) • Capture syntactic and remantic regularities in language • Outperform state-of-the-art


Universal Adversarial Perturbations

Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for computing universal perturbations, and show that state-of-the-art deep neural networks are highly vulnerable to such perturbations, albeit being quasi-imperceptible to the human eye. We further empirically analyze these universal perturbations and show, in particular, that they generalize very well across neural networks. The surprising existence of universal perturbations reveals important geometric correlations among the high-dimensional decision boundary of classifiers. It further outlines potential security breaches with the existence of single directions in the input space that adversaries can possibly exploit to break a classifier on most natural images.


The Earth Mover's Distance as a Metric for Image Retrieval

We investigate the properties of a metric between two distributions, the Earth Mover's Distance (EMD), for content-based image retrieval. The EMD is based on the minimal cost that must be paid to transform one distribution into the other, in a precise sense, and was first proposed for certain vision problems by Peleg, Werman, and Rom. For image retrieval, we combine this idea with a representation scheme for distributions that is based on vector quantization. This combination leads to an image comparison framework that often accounts for perceptual similarity better than other previously proposed methods. The EMD is based on a solution to the transportation problem from linear optimization, for which efficient algorithms are available, and also allows naturally for partial matching. It is more robust than histogram matching techniques, in that it can operate on variable-length representations of the distributions that avoid quantization and other binning problems typical of histograms. When used to compare distributions with the same overall mass, the EMD is a true metric. In this paper we focus on applications to color and texture, and we compare the retrieval performance of the EMD with that of other distances.


Software Framework for Topic Modelling with Large Corpora

Large corpora are ubiquitous in today’s world and memory quickly becomes the limiting factor in practical applications of the Vector Space Model (VSM). In this paper, we identify a gap in existing implementations of many of the popular algorithms, which is their scalability and ease of use. We describe a Natural Language Processing software framework which is based on the idea of document streaming, i.e. processing corpora document after document, in a memory independent fashion. Within this framework, we implement several popular algorithms for topical inference, including Latent Semantic Analysis and Latent Dirichlet Allocation, in a way that makes them completely independent of the training corpus size. Particular emphasis is placed on straightforward and intuitive framework design, so that modifications and extensions of the methods and/or their application by interested practitioners are effortless. We demonstrate the usefulness of our approach on a real-world scenario of computing document similarities within an existing digital library DML-CZ.


Dual-Resonance NFC Antenna System Based on NFC Chip Antenna

In this letter, to enhance the performance of the near-field communication (NFC) antenna, an antenna system that has dual resonance is proposed. The antenna is based on NFC chip antenna, and the dual resonance comes from the chip antenna itself, the eddy current on printed circuit board, and a nearby loop that has strong coupling with the chip antenna. The performance of the proposed antenna system is confirmed by measurement.


Explaining the “ Identifiable Victim Effect ”

It is widely believed that people are willing to expend greater resources to save the lives of identified victims than to save equal numbers of unidentified or statistical victims. There are many possible causes of this disparity which have not been enumerated previously or tested empirically. We discuss four possible causes of the “identifiable victim effect” and present the results of two studies which indicate that the most important cause of the disparity in treatment of identifiable and statistical lives is that, for identifiable victims, a high proportion of those at risk can be saved.


Efficiency optimization of half-bridge series resonant inverter with asymmetrical duty cycle control for domestic induction heating

In this paper, a method to improve efficiency in half-bridge series resonant inverter applied to domestic induction heating is presented. Low and medium output powers required for this application implies the use of higher switching frequencies, which leads to an efficiency decrease. Asymmetrical duty cycle (ADC) modulation scheme is proposed to improve efficiency due to its switching frequency reduction and absence of additional hardware requirements. Study methodology comprises, in a first step, a theoretical analysis of power balance as a function of control parameters: duty cycle and switching frequency. In addition, restrictions due to snubber and dead time, and variability of the induction loads have been considered. Afterwards, an efficiency analysis has been carried out to determine the optimum operation point. Switching and conduction losses have been calculated to examine global importance of each one for different switching devices. ADC modulation efficiency improvement is achieved by means of a switching frequency reduction, mainly at low-medium power range and low quality factor (Q) loads. The analytical results obtained with this study have been validated through an induction heating test-bench. A discrete 3-kW RL load has been designed to emulate a typical induction heating load. Then, a commercial induction heating inverter is used to evaluate ADC modulation scheme.


Vehicle Detection and Compass Applications using AMR Magnetic Sensors

The earliest magnetic field detectors allowed navigation over trackless oceans by sensing the earth’s magnetic poles. Magnetic field sensing has vastly expanded as industry has adapted a variety of magnetic sensors to detect the presence, strength, or direction of magnetic fields not only from the earth, but also from permanent magnets, magnetized soft magnets, vehicle disturbances, brain wave activity, and fields generated from electric currents. Magnetic sensors can measure these properties without physical contact and have become the eyes of many industrial and navigation control systems. This paper will describe the current state of magnetic sensing within the earth’s field range and how these sensors are applied. Several applications will be presented for magnetic sensing in systems with emphasis on vehicle detection and navigation based on magnetic fields.


Flying Eyes and Hidden Controllers: A Qualitative Study of People's Privacy Perceptions of Civilian Drones in The US

Drones are unmanned aircraft controlled remotely or operated autonomously. While the extant literature suggests that drones can in principle invade people’s privacy, little is known about how people actually think about drones. Drawing from a series of in-depth interviews conducted in the United States, we provide a novel and rich account of people’s pri vacy perceptions of drones for civilian uses both in general and under specific usage scenarios. Our informants raised both physical and information privacy issues against government, organization and individual use of drones. Informants’ reason ing about the acceptance of drone use was in part based on whether the drone is operating in a public or private space. However, our informants differed significantly in their defini tions of public and private spaces. While our informants’ pri vacy concerns such as surveillance, data collection and shar ing have been raised for other tracking technologies such as camera phones and closed-circuit television (CCTV), our in terviews highlight two heightened issues of drones: (1) pow erful yet inconspicuous data collection, (2) hidden and inac cessible drone controllers. These two aspects of drones render some of people’s existing privacy practices futile (e.g., notice recording and ask controllers to stop or delete the recording). Some informants demanded notifications of drones near them and expected drone controllers asking for their explicit per missions before recording. We discuss implications for future privacy-enhancing drone designs.


Reduced graphene oxide–silver nanoparticle nanocomposite: a potential anticancer nanotherapy

BACKGROUND
Graphene and graphene-based nanocomposites are used in various research areas including sensing, energy storage, and catalysis. The mechanical, thermal, electrical, and biological properties render graphene-based nanocomposites of metallic nanoparticles useful for several biomedical applications. Epithelial ovarian carcinoma is the fifth most deadly cancer in women; most tumors initially respond to chemotherapy, but eventually acquire chemoresistance. Consequently, the development of novel molecules for cancer therapy is essential. This study was designed to develop a simple, non-toxic, environmentally friendly method for the synthesis of reduced graphene oxide-silver (rGO-Ag) nanoparticle nanocomposites using Tilia amurensis plant extracts as reducing and stabilizing agents. The anticancer properties of rGO-Ag were evaluated in ovarian cancer cells.

METHODS
The synthesized rGO-Ag nanocomposite was characterized using various analytical techniques. The anticancer properties of the rGO-Ag nanocomposite were evaluated using a series of assays such as cell viability, lactate dehydrogenase leakage, reactive oxygen species generation, cellular levels of malonaldehyde and glutathione, caspase-3 activity, and DNA fragmentation in ovarian cancer cells (A2780).

RESULTS
AgNPs with an average size of 20 nm were uniformly dispersed on graphene sheets. The data obtained from the biochemical assays indicate that the rGO-Ag nanocomposite significantly inhibited cell viability in A2780 ovarian cancer cells and increased lactate dehydrogenase leakage, reactive oxygen species generation, caspase-3 activity, and DNA fragmentation compared with other tested nanomaterials such as graphene oxide, rGO, and AgNPs.

CONCLUSION
T. amurensis plant extract-mediated rGO-Ag nanocomposites could facilitate the large-scale production of graphene-based nanocomposites; rGO-Ag showed a significant inhibiting effect on cell viability compared to graphene oxide, rGO, and silver nanoparticles. The nanocomposites could be effective non-toxic therapeutic agents for the treatment of both cancer and cancer stem cells.


Transfer Learning for Music Classification and Regression Tasks

In this paper, we present a transfer learning approach for music classification and regression tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network. We show how this convnet feature can serve as general-purpose music representation. In the experiments, a convnet is trained for music tagging and then transferred to other music-related classification and regression tasks. The convnet feature outperforms the baseline MFCC feature in all the considered tasks and several previous approaches that are aggregating MFCCs as well as lowand high-level music features.


Detecting Deceptive Behavior via Integration of Discriminative Features From Multiple Modalities

Deception detection has received an increasing amount of attention in recent years, due to the significant growth of digital media, as well as increased ethical and security concerns. Earlier approaches to deception detection were mainly focused on law enforcement applications and relied on polygraph tests, which had proved to falsely accuse the innocent and free the guilty in multiple cases. In this paper, we explore a multimodal deception detection approach that relies on a novel data set of 149 multimodal recordings, and integrates multiple physiological, linguistic, and thermal features. We test the system on different domains, to measure its effectiveness and determine its limitations. We also perform feature analysis using a decision tree model, to gain insights into the features that are most effective in detecting deceit. Our experimental results indicate that our multimodal approach is a promising step toward creating a feasible, non-invasive, and fully automated deception detection system.


Inferring Undiscovered Public Knowledge by Using Text Mining-driven Graph Model

Due to the recent development of Information Technology, the number of publications is increasing exponentially. In response to the increasing number of publications, there has been a sharp surge in the demand for replacing the existing manual text data processing by an automatic text data processing. Swanson proposed ABC model [1] on the top of text mining as a part of literature-based knowledge discovery for finding new possible biomedical hypotheses about three decades ago. The following clinical scholars proved the effectiveness of the possible hypotheses found by ABC model [2]. Such effectiveness let scholars try various literature-based knowledge discovery approaches [3, 4, 5]. However, their trials are not fully automated but hybrids of automatic and manual processes. The manual process requires the intervention of experts. In addition, their trials consider a single perspective. Even trials involving network theory have difficulties in mal-understanding the entire network structure of the relationships among concepts and the systematic interpretation on the structure [6, 7]. Thus, this study proposes a novel approach to discover various relationships by extending the intermediate concept B to a multi-leveled concept. By applying a graph-based path finding method based on co-occurrence and the relational entities among concepts, we attempt to systematically analyze and investigate the relationships between two concepts of a source node and a target node in the total paths. For the analysis of our study, we set our baseline as the result of Swanson [8]'s work. This work suggested the intermediate concept or terms between Raynaud's disease and fish oils as blood viscosity, platelet aggregability, and vasconstriction. We compared our results of intermediate concepts with these intermediate concepts of Swanson's. This study provides distinct perspectives for literature-based discovery by not only discovering the meaningful relationship among concepts in biomedical literature through graph-based path interference but also being able to generate feasible new hypotheses.


Failure detection and consensus in the crash-recovery model

Summary. We study the problems of failure detection and consensus in asynchronous systems in which processes may crash and recover, and links may lose messages. We first propose new failure detectors that are particularly suitable to the crash-recovery model. We next determine under what conditions stable storage is necessary to solve consensus in this model. Using the new failure detectors, we give two consensus algorithms that match these conditions: one requires stable storage and the other does not. Both algorithms tolerate link failures and are particularly efficient in the runs that are most likely in practice – those with no failures or failure detector mistakes. In such runs, consensus is achieved within $3 \delta$ time and with 4 n messages, where $\delta$ is the maximum message delay and n is the number of processes in the system.


Preimages for Step-Reduced SHA-2

In this paper, we present preimage attacks on up to 43step SHA-256 (around 67% of the total 64 steps) and 46-step SHA-512 (around 57.5% of the total 80 steps), which significantly increases the number of attacked steps compared to the best previously published preimage attack working for 24 steps. The time complexities are 2, 2 for finding pseudo-preimages and 2, 2 compression function operations for full preimages. The memory requirements are modest, around 2 words for 43-step SHA-256 and 46-step SHA-512. The pseudo-preimage attack also applies to 43-step SHA-224 and SHA-384. Our attack is a meet-in-the-middle attack that uses a range of novel techniques to split the function into two independent parts that can be computed separately and then matched in a birthday-style phase.


Critical review on video game evaluation heuristics: social games perspective

This paper presents the first step in creating design and evaluation heuristics for social games which emerge from the domain of social media. Initial high level heuristics for social games are offered by reviewing four existing video game heuristic models and analyzing two social games design frameworks.


Through-Wall Opportunistic Sensing System Utilizing a Low-Cost Flat-Panel Array

A UWB through-wall imaging system is proposed based on a planar low profile aperture array operating from 0.9 GHz to 2.3 GHz. The goal is to provide a lightweight, fixed array to serve as an alternative to synthetic aperture radars (SAR) that require continuous array movement while collecting data. The proposed system consists of 12 dual-linear printed elements arranged within a triangular lattice, each forming a “flower” shape and backed by a ground plane. The array delivers half-space radiation with wideband performance, necessary for imaging applications. UWB capability is realized by suppressing grating lobes via the introduction of virtual phase centers interwoven within the actual array feeds. The proposed system is demonstrated for through-wall imaging via a non-coherent process. Distinctively, several coherent images are forged from various fixed aperture locations (referred to as “snapshot” locations) and appropriately combined to create a composite scene image. In addition to providing a unique wideband imaging capability (as an alternative to SAR), the system is portable and inexpensive for collecting/storing scattering data. The array design and data collection system is described, and several through-wall images are presented to demonstrate functionality.


Maximum likelihood linear transformations for HMM-based speech recognition

This paper examines the application of linear transformations for speaker and environmental adaptation in an HMM-based speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Other than in the form of a simple bias, strict linear feature-space transformations are inappropriate in this case. Hence, only model-based linear transforms are considered. The paper compares the two possible forms of model-based transforms: (i) unconstrained, where any combination of mean and variance transform may be used, and (ii) constrained, which requires the variance transform to have the same form as the mean transform (sometimes referred to as feature-space transforms). Re-estimation formulae for all appropriate cases of transform are given. This includes a new and e cient \full" variance transform and the extension of the constrained model-space transform from the simple diagonal case to the full or block-diagonal case. The constrained and unconstrained transforms are evaluated in terms of computational cost, recognition time e ciency, and use for speaker adaptive training. The recognition performance of the two model-space transforms on a large vocabulary speech recognition task using incremental adaptation is investigated. In addition, initial experiments using the constrained model-space transform for speaker adaptive training are detailed.


Semantic-Based Location Recommendation With Multimodal Venue Semantics

In recent years, we have witnessed a flourishing of location -based social networks. A well-formed representation of location knowledge is desired to cater to the need of location sensing, browsing, navigation and querying. In this paper, we aim to study the semantics of point-of-interest (POI) by exploiting the abundant heterogeneous user generated content (UGC) from different social networks. Our idea is to explore the text descriptions, photos, user check-in patterns, and venue context for location semantic similarity measurement. We argue that the venue semantics play an important role in user check-in behavior. Based on this argument, a unified POI recommendation algorithm is proposed by incorporating venue semantics as a regularizer. In addition to deriving user preference based on user-venue check-in information, we place special emphasis on location semantic similarity. Finally, we conduct a comprehensive performance evaluation of location semantic similarity and location recommendation over a real world dataset collected from Foursquare and Instagram. Experimental results show that the UGC information can well characterize the venue semantics, which help to improve the recommendation performance.


SyncGC: A Synchronized Garbage Collection Technique for Reducing Tail Latency in Cassandra

Data-center applications running on distributed databases often suffer from unexpectedly high response time fluctuation which is caused by long tail latency. In this paper, we find that long tail latency of user writes is mainly created by the interference with garbage collection (GC) tasks running in various system layers. In order to address the tail latency problem, we propose a synchronized garbage collection technique, called SyncGC. By scheduling multiple GC instances to execute in sync with each other in an overlapped manner, SyncGC prevents user requests from being interfered with GC instances, thereby minimizing their negative impacts on tail latency. Our experimental results with Cassandra show that SyncGC reduces the 99.99th-percentile tail latency and the maximum latency by 35% and 37%, on average, respectively.


Experimental validation of dual PPG local pulse wave velocity probe

A novel dual photoplethysmograph (PPG) probe and measurement system for local pulse wave velocity (PWV) is proposed and demonstrated. The developed probe design employs reflectance PPG transducers for non-invasive detection of blood pulse propagation waveforms from two adjacent measurement points (28 mm apart). Transit time delay between the continuously acquired dual pulse waveform was used for beat-to-beat local PWV measurement. An in-vivo experimental validation study was conducted on 10 healthy volunteers (8 male and 2 female, 21 to 33 years of age) to validate the PPG probe design and developed local PWV measurement system. The proposed system was able to measure carotid local PWV from multiple subjects. Beat-to-beat variation of baseline carotid PWV was less than 7.5% for 7 out of 10 subjects, a maximum beat-to-beat variation of 16% was observed during the study. Variation in beat-to-beat carotid local PWV and brachial blood pressure (BP) values during post-exercise recovery period was also examined. A statistically significant correlation between intra-subject local PWV variation and brachial BP parameters was observed (r > 0.85, p < 0.001). The results demonstrated the feasibility of proposed PPG probe for continuous beat-to-beat local PWV measurement from the carotid artery. Such a non-invasive local PWV measurement unit can be potentially used for continuous ambulatory BP measurements.


Millimeter-Wave TE20-Mode SIW Dual-Slot-Fed Patch Antenna Array With a Compact Differential Feeding Network

A millimeter-wave series–parallel patch antenna array is presented, in which the dual-slot feeding structure is handily implemented using the intrinsic field distribution of TE20 mode in substrate-integrated waveguide (SIW). One 28 GHz patch antenna element fed by the TE20-mode SIW is first designed, achieving a 10 dB impedance bandwidth of 10.2% and a simulated peak gain of 6.48 dBi. Based on the antenna element, a <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> array with a compact series–parallel differential feeding network is developed accordingly. Due to the novel compact SIW-based series–parallel feeding network, the antenna array can achieve superior radiation performances, which is the highlight of this communication. The simulation and measurement results of the proposed antenna array are in good agreement, demonstrating a performance of 8.5% impedance bandwidth, 19.1 dBi peak gain, symmetrical radiation patterns, and low cross-polarization levels (−30 dB in E-plane and −25 dB in H-plane) in the operating frequency band of 26.65–29.14 GHz.


Perceived parental social support and academic achievement: an attachment theory perspective.

The study tested the extent to which parental social support predicted college grade point average among undergraduate students. A sample of 418 undergraduates completed the Social Provisions Scale--Parent Form (C.E. Cutrona, 1989) and measures of family conflict and achievement orientation. American College Testing Assessment Program college entrance exam scores (ACT; American College Testing Program, 1986) and grade point average were obtained from the university registrar. Parental social support, especially reassurance of worth, predicted college grade point average when controlling for academic aptitude (ACT scores), family achievement orientation, and family conflict. Support from parents, but not from friends or romantic partners, significantly predicted grade point average. Results are interpreted in the context of adult attachment theory.


Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony

The quantification of phase synchrony between neuronal signals is of crucial importance for the study of large-scale interactions in the brain. Two methods have been used to date in neuroscience, based on two distinct approaches which permit a direct estimation of the instantaneous phase of a signal [Phys. Rev. Lett. 81 (1998) 3291; Human Brain Mapping 8 (1999) 194]. The phase is either estimated by using the analytic concept of Hilbert transform or, alternatively, by convolution with a complex wavelet. In both methods the stability of the instantaneous phase over a window of time requires quantification by means of various statistical dependence parameters (standard deviation, Shannon entropy or mutual information). The purpose of this paper is to conduct a direct comparison between these two methods on three signal sets: (1) neural models; (2) intracranial signals from epileptic patients; and (3) scalp EEG recordings. Levels of synchrony that can be considered as reliable are estimated by using the technique of surrogate data. Our results demonstrate that the differences between the methods are minor, and we conclude that they are fundamentally equivalent for the study of neuroelectrical signals. This offers a common language and framework that can be used for future research in the area of synchronization.


Author ' s personal copy Romancing leadership : Past , present , and future ☆

Available online 2 October 2011 This paper presents a review of the romance of leadership and the social construction of leadership theory 25 years after it was originally introduced. We trace the development of this theoretical approach from the original formulation of the romance of leadership (RoL) theory as attributional bias through its emergence as a radical, unconventional approach that views leadership as a sensemaking activity that is primarily ‘in the eye of the beholder.’ We subsequently review research published in management and organizational psychology journals, book chapters and special issues of journals from 1985 to 2010. Three overall themes emerged from this review: 1) biases in (mis)attributions of leadership, including attributions for organizational success and failure; 2) follower-centered approaches, including the role of follower characteristics, perceptions, and motivations in interpreting leadership ratings; and 3) the social construction of leadership, including interfollower and social contagion processes, the role of crisis and uncertainty, and constructions and deconstructions of leadership and CEO celebrity in the media. Within each of these themes, we examine developments and summarize key findings. Our review concludes with recommendations for future theoretical and empirical


A General Agnostic Active Learning Algorithm

We present a simple, agnostic active learning algorithm that works for any hypothesis class of bounded VC dimension, and any data distribution. Our algorithm extends a scheme of Cohn, Atlas, and Ladner [6] to the agnostic setting, by (1) reformulating it using a reduction to supervised learning and (2) showing how to apply generalization bounds even for the non-i.i.d. samples that result from selective sampling. We provide a general characterization of the label complexity of our algorithm. This quantity is never more than the usual PAC sample complexity of supervised learning, and is exponentially smaller for some hypothesis classes and distributions. We also demonstrate improvements experimentally.


Multiresolution Models for Object Detection

Most current approaches to recognition aim to be scaleinvariant. However, the cues available for recognizing a 300 pixel tall object are qualitatively different from those for recognizing a 3 pixel tall object. We argue that for sensors with finite resolution, one should instead use scale-variant, or multiresolution representations that adapt in complexity to the size of a putative detection window. We describe a multiresolution model that acts as a deformable part-based model when scoring large instances and a rigid template with scoring small instances. We also examine the interplay of resolution and context, and demonstrate that context is most helpful for detecting low-resolution instances when local models are limited in discriminative power. We demonstrate impressive results on the Caltech Pedestrian benchmark, which contains object instances at a wide range of scales. Whereas recent state-of-theart methods demonstrate missed detection rates of 86%-37% at 1 falsepositive-per-image, our multiresolution model reduces the rate to 29%.


Gesture control of drone using a motion controller

In this study, we present our implementation of using a motion controller to control the motion of a drone via simple human gestures. We have used the Leap as the motion controller and the Parrot AR DRONE 2.0 for this implementation. The Parrot AR DRONE is an off the shelf quad rotor having an on board Wi-Fi system. The AR DRONE is connected to the ground station via Wi-Fi and the Leap is connected to the ground station via USB port. The LEAP Motion Controller recognizes the hand gestures and relays it on to the ground station. The ground station runs ROS (Robot Operating System) in Linux which is used as the platform for this implementation. Python is the programming language used for interaction with the AR DRONE in order to convey the simple hand gestures. In our implementation, we have written python codes to interpret the hand gestures captured by the LEAP, and transmit them in order to control the motion of the AR DRONE via these gestures.


Improvement of Marching Cubes Algorithm Based on Sign Determination

Traditional Marching Cubes algorithm has the problem of repeating calculation, so that an improved Marching Cubes algorithm is put forward. Boundary voxel is utilized to find adjacent boundary voxel. According to the relationship of edge and edge sign between boundary voxel and adjacent boundary voxel, we transmit intersection on the common face of the boundary voxel to adjacent boundary voxel. If the edge sign of adjacent boundary voxel is not existed, we change the edge sign of the adjacent boundary voxel simultaneously. In that way, we can avoid double counting of the intersection, which is in two adjacent boundary voxels. The two adjacent boundary voxels have common surface on where the edge of the isosurface lies. During the computation of intersection of edge and isosurface, we only compute the edges whose edge signs are null. At the same time, we make use of edge sign to avoid repeating assignment. It speeds up isosurface extraction.


Parallel Tracking and Mapping for Small AR Workspaces

This paper presents a method of estimating camera pose in an unknown scene. While this has previously been attempted by adapting SLAM algorithms developed for robotic exploration, we propose a system specifically designed to track a hand-held camera in a small AR workspace. We propose to split tracking and mapping into two separate tasks, processed in parallel threads on a dual-core computer: one thread deals with the task of robustly tracking erratic hand-held motion, while the other produces a 3D map of point features from previously observed video frames. This allows the use of computationally expensive batch optimisation techniques not usually associated with real-time operation: The result is a system that produces detailed maps with thousands of landmarks which can be tracked at frame-rate, with an accuracy and robustness rivalling that of state-of-the-art model-based systems.


Fast Keypoint Recognition in Ten Lines of Code

While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a Naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well to handle large number of classes. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image datasets containing very significant perspective changes.


Face to Face Collaborative AR on Mobile Phones

Mobile phones are an ideal platform for augmented reality. In this paper we describe how they can also be used to support face to face collaborative AR gaming. We have created a custom port of the ARToolKit library to the Symbian mobile phone operating system and then developed a sample collaborative AR game based on this. We describe the game in detail and user feedback from people who have played the game. We also provide general design guidelines that could be useful for others who are developing mobile phone collaborative AR applications.


USING CAMERA-EQUIPPED MOBILE PHONES FOR INTERACTING WITH REAL-WORLD OBJECTS

The idea described in this paper is to use the built-in cameras of consumer mobile phones as sensors for 2-dimensional visual codes. Such codes can be attached to physical objects in order to retrieve object-related information and functionality. They are also suitable for display on electronic screens. The proposed visual code system allows the simultaneous detection of multiple codes, introduces a position-independent coordinate system, and provides the phone’s orientation as a parameter. The ability to detect objects in the user’s vicinity offers a natural way of interaction and strengthens the role of mobile phones in a large number of application scenarios. We describe the hardware requirements, the design of a suitable visual code, a lightweight recognition algorithm, and present some example applications.


Robust Visual Tracking for Non-Instrumented Augmented Reality

This paper presents a robust and flexible framework for augmented reality which does not require instrumenting either the environment or the workpiece. A model-based visual tracking system is combined with with rate gyroscopes to produce a system which can track the rapid camera rotations generated by a head-mounted camera, even if images are substantially degraded by motion blur. This tracking yields estimates of head position at video field rate (50Hz) which are used to align computer-generated graphics on an optical see-through display. Nonlinear optimisation is used for the calibration of display parameters which include a model of optical distortion. Rendered visuals are pre-distorted to correct the optical distortion of the display.


Engagement in Multimedia Training Systems

Two studies examined user engagement in two type multimedia training systems -a more passive medium, videotape, and a less passive medium, interactive softw Each study compared engagement in three formats: the Text format contained text and still images, the Audio format contained audio and still images, and the Video format contained audio and video images. In both studies, engagement was lower in the Text condition than in the Video condition. However, there were no differences in engagement between Text and Audio in the videotapebased training, and no differences between Audio and Video in the computer-based training.


Action recognition based on a bag of 3D points

This paper presents a method to recognize human actions from sequences of depth maps. Specifically, we employ an action graph to model explicitly the dynamics of the actions and a bag of 3D points to characterize a set of salient postures that correspond to the nodes in the action graph. In addition, we propose a simple, but effective projection based sampling scheme to sample the bag of 3D points from the depth maps. Experimental results have shown that over 90% recognition accuracy were achieved by sampling only about 1% 3D points from the depth maps. Compared to the 2D silhouette based recognition, the recognition errors were halved. In addition, we demonstrate the potential of the bag of points posture model to deal with occlusions through simulation.


Birdbrains could teach basal ganglia research a new song

Recent advances in anatomical, physiological and histochemical characterization of avian basal ganglia neurons and circuitry have revealed remarkable similarities to mammalian basal ganglia. A modern revision of the avian anatomical nomenclature has now provided a common language for studying the function of the cortical-basal-ganglia-cortical loop, enabling neuroscientists to take advantage of the specialization of basal ganglia areas in various avian species. For instance, songbirds, which learn their vocal motor behavior using sensory feedback, have specialized a portion of their cortical-basal ganglia circuitry for song learning and production. This discrete circuit dedicated to a specific sensorimotor task could be especially tractable for elucidating the interwoven sensory, motor and reward signals carried by basal ganglia, and the function of these signals in task learning and execution.


Robotic Arm Based 3D Reconstruction Test Automation

The 3-D reconstruction involves the construction of a 3-D model from a set of images. The 3-D reconstruction has varied uses that include 3-D printing, the generation of 3-D models that can be shared through social media, and more. The 3-D reconstruction involves complex computations in mobile phones that must determine the pose estimation. The pose estimation involves the process of transforming a 2-D object into 3-D space. Once the pose estimation is done, then the mesh generation is performed using the graphics processing unit. This helps render the 3-D object. The competitive advantages of using hardware processors are to accelerate the intensive computation using graphics processors and digital signal processors. The stated problem that this technical paper addresses is the need for a reliable automated test for the 3-D reconstruction feature. The solution to this problem involved the design and development of an automated test system using a programmable robotic arm and rotor for precisely testing the quality of 3-D reconstruction features. The 3-D reconstruction testing involves using a robotic arm lab to accurately test the algorithmic integrity and end-to-end validation of the generated 3-D models. The robotic arm can move the hardware at different panning speeds, specific angles, fixed distances from the object, and more. The ability to reproduce the scanning at a fixed distance and the same panning speed helps to generate test results that can be benchmarked by different software builds. The 3-D reconstruction also requires a depth sensor to be mounted onto the device under examination. We use this robotic arm lab for functional, high performance, and stable validation of the 3-D reconstruction feature. This paper addresses the computer vision use case testing for 3-D reconstruction features and how we have used the robotic arm lab for automating these use cases.


A Ka-Band CMOS Wilkinson Power Divider Using Synthetic Quasi-TEM Transmission Lines

This work presents a Ka-band two-way 3 dB Wilkinson power divider using synthetic quasi-transverse electromagnetic (TEM) transmission lines (TLs). The synthetic quasi-TEM TL, also called complementary-conducting-strip TL (CCS TL), is theoretically analyzed. The equivalent TL model, whose production is based on the extracted results, is applied to the power divider design. The prototype is fabricated by the standard 0.18 mum 1P6M CMOS technology, showing the circuit size of 210.0 mumtimes390.0 mum without contact pads. The measurement results, which match the 50 Omega system, reveal perfect agreements with those of the simulations. The comparison reveals the following characteristics. The divider exhibits an equal power-split with the insertion losses (S21 and S31) of 3.65 dB. The return losses (S11, S22 and S33) of the prototype are higher than 10.0 dB from 30.0 to 40.0 GHz.


Using Patterns to Capture Architectural Decisions

Throughout the software design process, developers must make decisions and reify them in code. The decisions made during software architecting are particularly significant in that they have system-wide implications, especially on the quality attributes. However, architects often fail to adequately document their decisions because they don't appreciate the benefits, don't know how to document decisions, or don't recognize that they're making decisions. This lack of thorough documentation. This paper provides information about a decision's rationale and consequences, architecture patterns can help architects better understand and more easily record their decisions.


A model for concentric tube continuum robots under applied wrenches

Continuum robots made from telescoping precurved elastic tubes enable base-mounted actuators to specify the curved shapes of robots as thin as standard surgical needles. While free space beam mechanics-based models of the shape of these ‘active cannulas’ exist, current models cannot account for external forces and torques applied to the cannula by the environment. In this paper we apply geometrically exact beam theory to solve the statics problem for concentric-tube continuum robots. This yields the equivalent of forward kinematics for an active cannula with general tube precurvature functions and arbitrarily many tubes, under loading from a general wrench distribution. The model achieves average experimental tip errors of less than 3 mm over the workspace of a prototype active cannula subject to various tip forces.


The feasible workspace analysis of a set point control for a cable-suspended robot with input constraints and disturbances

This paper deals with the characterization of reachable domain of a set-point controller for a cable-suspended robot under disturbances and input constraints. The main contribution of the paper is to calculate the feasible domain analytically through the choice of a control law, starting from a given initial condition. This analytical computation is then recursively used to find a second feasible domain starting from a boundary point of the first feasible domain. Hence, this procedure allows to expand the region of feasible reference signals by connecting adjacent domains through common points. Finally, the effectiveness of the proposed method is illustrated by numerical simulations on a kinematically determined cable robot with six cables.


Graph Embedding Problem Settings Graph Embedding Input Graph Embedding Output Homogeneous Graph Heterogeneous Graph Graph with Auxiliary Information Graph Constructed from Non-relational Data Node Embedding Edge Embedding Hybrid Embedding Whole-Graph Embedding Graph Embedding Techniques Matrix Facto

Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.


Local sensor system for badminton smash analysis

This paper presents a development of a sensory system for analysis of badminton smashes. During a badminton game, the ability to execute a powerful smash is fundamental for a player to be competitive. In most games, the winning factor for the game is often attributed to a high shuttle speed during the execution of a smash. It was envisioned that the shuttle speed can be correlated from the speed of the rackets head. To help analyze the key relationship between the racket speed and the shuttle ball speed, a series of sensors were integrated into a conventional racket. The aim of this investigation is to develop a sensor system that will facilitate the quantifiable analysis of the badminton smash. It was determined in a previous investigation that a single accelerometer is insufficient to determine the two or three dimensional trajectory of the racket. Therefore a low mass compact, 2-axes piezoelectric accelerometers was applied to the head of the racket. An acoustic shock sensor was also mounted on the racket head to identify the instant of the contact event. It was hypothesized that the magnitude of the acoustic signal, associated with the hitting event, and the final speed of the racket when fused could provide a good correlation with the shuttle speed. The fuzzy inference system and ANFIS (Adaptive Neuro-Fuzzy Inferior System) sensor fusion techniques was investigated. It has been demonstrated that it is possible to analyze the performance of the smashing stroke based on the system developed in this investigation which in turn may, with further develop by a useful aid to badminton coaches and the methods developed may also be applicable to other applications.


Vital, Sophia, and Co. - The Quest for the Legal Personhood of Robots

The paper examines today’s debate on the legal status of AI robots, and how often scholars and policy makers confuse the legal agenthood of these artificial agents with the status of legal personhood. By taking into account current trends in the field, the paper suggests a twofold stance. First, policy makers shall seriously mull over the possibility of establishing novel forms of accountability and liability for the activities of AI robots in contracts and business law, e.g., new forms of legal agenthood in cases of complex distributed responsibility. Second, any hypothesis of granting AI robots full legal personhood has to be discarded in the foreseeable future. However, how should we deal with Sophia, which became the first AI application to receive citizenship of any country, namely, Saudi Arabia, in October 2017? Admittedly, granting someone, or something, legal personhood is—as always has been—a highly sensitive political issue that does not simply hinge on rational choices and empirical evidence. Discretion, arbitrariness, and even bizarre decisions play a role in this context. However, the normative reasons why legal systems grant human and artificial entities, such as corporations, their status, help us taking sides in today’s quest for the legal personhood of AI robots. Is citizen Sophia really conscious, or capable of suffering the slings and arrows of outrageous scholars?


Working memory as an emergent property of the mind and brain

Cognitive neuroscience research on working memory has been largely motivated by a standard model that arose from the melding of psychological theory with neuroscience data. Among the tenets of this standard model are that working memory functions arise from the operation of specialized systems that act as buffers for the storage and manipulation of information, and that frontal cortex (particularly prefrontal cortex) is a critical neural substrate for these specialized systems. However, the standard model has been a victim of its own success, and can no longer accommodate many of the empirical findings of studies that it has motivated. An alternative is proposed: Working memory functions arise through the coordinated recruitment, via attention, of brain systems that have evolved to accomplish sensory-, representation-, and action-related functions. Evidence from behavioral, neuropsychological, electrophysiological, and neuroimaging studies, from monkeys and humans, is considered, as is the question of how to interpret delay-period activity in the prefrontal cortex.


Innovating with Concept Mapping

Student-generated concept maps (Cmaps) have been the preferred choice to design assessment tasks, which are time-consuming in real classroom settings. We have proposed the use of Cmap with errors (elaborated by teachers) to develop assessment tasks, as a way to address the logistical practicality obstacles usually found in classrooms. This paper compared two different tasks, finding the errors and judging the selected propositions in Cmap with errors, exploring two topics with different levels of difficulty. Our results confirmed Cmap with errors as a straightforward approach to include concept mapping into the classroom routine to foster the pedagogic resonance (the bridge between teacher knowledge and student learning), which is critical for motivating students to learn meaningfully. Moreover, Cmaps with errors are also amenable for on-line learning platforms. Future works of our research group will develop an automated process for evaluation and feedback using the task formats presented in this paper.


Natural Language Processing for Question Answering ( NLP 4

The recent developments in Question Answering have kept with open-domain questions and collections, sometimes argued as being more di fficult than narrow domain-focused questions and corpora. The biomedical field is indeed a specialized domain; however, its scope is fairly broad, so that considering a biomedical QA task is not necessarily such a simplification over open-domain QA as represented in the recent TREC evaluations. We shall try to characterize salient aspects of biomedical QA as well as to give a short review of useful resources to address this task.


Guest editorial: qualitative studies in information systems: a critical review and some guiding principles

Please refer to attached Guest Editorial


Examining the critical success factors in the adoption of enterprise resource planning

This paper presents a literature review of the critical success factors (CSFs) in the implementation of enterprise resource planning (ERP) across 10 different countries/regions. The review covers journals, conference proceedings, doctoral dissertation, and textbooks from these 10 different countries/regions. Through a review of the literature, 18 CSFs were identified, with more than 80 sub-factors, for the successful implementation of ERP. The findings of our study reveal that ‘appropriate business and IT legacy systems’, ‘business plan/vision/goals/justification’, ‘business process reengineering’, ‘change management culture and programme’, ‘communication’, ‘ERP teamwork and composition’, ‘monitoring and evaluation of performance’, ‘project champion’, ‘project management’, ‘software/system development, testing and troubleshooting’, ‘top management support’, ‘data management’, ‘ERP strategy and implementation methodology’, ‘ERP vendor’, ‘organizational characteristics’, ‘fit between ERP and business/process’, ‘national culture’ and ‘country-related functional requirement’ were the commonly extracted factors across these 10 countries/regions. In these 18 CSFs, ‘top management support’ and ‘training and education’ were the most frequently cited as the critical factors to the successful implementation of ERP systems. # 2008 Elsevier B.V. All rights reserved.


Using Quality Models in Software Package Selection

0 7 4 0 7 4 5 9 / 0 3 / $ 1 9 . 0 0 © 2 0 0 3 I E E E All the methodologies that have been proposed recently for choosing software packages compare user requirements with the packages’ capabilities.3–5 There are different types of requirements, such as managerial, political, and, of course, quality requirements. Quality requirements are often difficult to check. This is partly due to their nature, but there is another reason that can be mitigated, namely the lack of structured and widespread descriptions of package domains (that is, categories of software packages such as ERP systems, graphical or data structure libraries, and so on). This absence hampers the accurate description of software packages and the precise statement of quality requirements, and consequently overall package selection and confidence in the result of the process. Our methodology for building structured quality models helps solve this drawback. (See the “Related Work” sidebar for information about other approaches.) A structured quality model for a given package domain provides a taxonomy of software quality features, and metrics for computing their value. Our approach relies on the International Organization for Standardization and International Electrotechnical Commission 9126-1 quality standard,6 which we selected for the following reasons:


An AI Approach to Automatic Natural Music Transcription

Automatic music transcription (AMT) remains a fundamental and difficult problem in music information research, and current music transcription systems are still unable to match human performance. AMT aims to automatically generate a score representation given a polyphonic acoustical signal. In our project, we approach the AMT problem on two fronts: acoustic modeling to identify pitches from a frame of audio, and establishing a score generation model to convert exact piano roll representations of audio into more ‘natural’ sheet music. We build an end to end pipeline that aims to convert .wav classical piano audio files into a ‘natural’ score representation.


COMPARISON OF LOSSLESS DATA COMPRESSION ALGORITHMS FOR TEXT DATA

Data compression is a common requirement for most of the computerized applications. There are number of data compression algorithms, which are dedicated to compress different data formats. Even for a single data type there are number of different compression algorithms, which use different approaches. This paper examines lossless data compression algorithms and compares their performance. A set of selected algorithms are examined and implemented to evaluate the performance in compressing text data. An experimental comparison of a number of different lossless data compression algorithms is presented in this paper. The article is concluded by stating which algorithm performs well for text data.


A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling

We introduce a simple and accurate neural model for dependency-based semantic role labeling. Our model predicts predicate-argument dependencies relying on states of a bidirectional LSTM encoder. The semantic role labeler achieves competitive performance on English, even without any kind of syntactic information and only using local inference. However, when automatically predicted partof-speech tags are provided as input, it substantially outperforms all previous local models and approaches the best reported results on the English CoNLL2009 dataset. We also consider Chinese, Czech and Spanish where our approach also achieves competitive results. Syntactic parsers are unreliable on out-of-domain data, so standard (i.e., syntactically-informed) SRL models are hindered when tested in this setting. Our syntax-agnostic model appears more robust, resulting in the best reported results on standard out-of-domain test sets.


Walking on foot to explore a virtual environment with uneven terrain

In this work we examine people's ability to maintain spatial awareness of an HMD--based immersive virtual environment when the terrain of the virtual world does not match the real physical world in which they are locomoting. More specifically, we examine what happens when a subject explores a hilly virtual environment while physically walking in a flat room. In our virtual environments, subjects maintain their height (or distance to the ground plane) at all times. In the experiment described in this work, we directly compare spatial orientation in a flat virtual environment to two variations of a hilly virtual environment. Interestingly, we find that subjects' spatial awareness of the environment is improved with the addition of uneven terrain.


The emergence of lncRNAs in cancer biology.

The discovery of numerous noncoding RNA (ncRNA) transcripts in species from yeast to mammals has dramatically altered our understanding of cell biology, especially the biology of diseases such as cancer. In humans, the identification of abundant long ncRNA (lncRNA) >200 bp has catalyzed their characterization as critical components of cancer biology. Recently, roles for lncRNAs as drivers of tumor suppressive and oncogenic functions have appeared in prevalent cancer types, such as breast and prostate cancer. In this review, we highlight the emerging impact of ncRNAs in cancer research, with a particular focus on the mechanisms and functions of lncRNAs.


Action Reaction Learning : Analysis and Synthesis of Human Behaviour

We propose Action-Reaction Learning as an approach for analyzing and synthesizing human behaviour. This paradigm uncovers causal mappings between past and future events or between an action and its reaction by observing time sequences. We apply this method to analyze human interaction and to subsequently synthesize human behaviour. Using a time series of perceptual measurements, a system automatically uncovers a mapping between gestures from one human participant (an action) and a subsequent gesture (a reaction) from another participant. A probabilistic model is trained from data of the human interaction using a novel estimation technique, Conditional Expectation Maximization (CEM). The system drives a graphical interactive character which probabilistically predicts the most likely response to the user's behaviour and performs it interactively. Thus, after analyzing human interaction in a pair of participants, the system is able to replace one of them and interact with a single remaining user.


Vehicle licence plate recognition using super-resolution technique

Due to the development of economy and technology, the people's demands on cars are growing and so are the problems, such as finding stolen car, banning violation and parking lot management. It will be time-consuming and low-efficiency if we do those jobs only by human because of the limitation of human being's concentration. Therefore, it has been a popular topic to develop intelligent monitoring system under new video technology within a decade. There are still many rooms for future development and application in the car license detection and recognition fields. For finding stolen car, we can integrate car license detection system and road monitoring system to analyze the videos and trace the objects, so we can gain high-efficiency and low-cost results. For parking lot management system, we can have the result of access management and automatic charge to reduce the human resource through car license recognition system. Automated toll of highway can be done through car license recognition system as well. Because the car license recognition system is mostly applied to security monitoring field and business purposes, the demand of the accuracy is quite strict. There are causes which make the inaccuracy of the license recognition, such as the lack of video resolution, too small license due to the distance and the light and shadow. We will discuss the license recognition system and how to use apply the super-resolution to overcome those above problems.


PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, launched in 2004 as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH). For the past 11 years, PubChem has grown to a sizable system, serving as a chemical information resource for the scientific research community. PubChem consists of three inter-linked databases, Substance, Compound and BioAssay. The Substance database contains chemical information deposited by individual data contributors to PubChem, and the Compound database stores unique chemical structures extracted from the Substance database. Biological activity data of chemical substances tested in assay experiments are contained in the BioAssay database. This paper provides an overview of the PubChem Substance and Compound databases, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access. It also gives a brief description of PubChem3D, a resource derived from theoretical three-dimensional structures of compounds in PubChem, as well as PubChemRDF, Resource Description Framework (RDF)-formatted PubChem data for data sharing, analysis and integration with information contained in other databases.


Adam: A Method for Stochastic Optimization

We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.


Probabilistic Principal Component Analysis

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Stochastic Variational Inference

We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets.


PILCO: A Model-Based and Data-Efficient Approach to Policy Search

In this paper, we introduce pilco, a practical, data-efficient model-based policy search method. Pilco reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way. By learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning, pilco can cope with very little data and facilitates learning from scratch in only a few trials. Policy evaluation is performed in closed form using state-ofthe-art approximate inference. Furthermore, policy gradients are computed analytically for policy improvement. We report unprecedented learning efficiency on challenging and high-dimensional control tasks.


Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

We present a new family of subgradient methods that dynamica lly incorporate knowledge of the geometry of the data observed in earlier iterations to perfo rm more informative gradient-based learning. Metaphorically, the adaptation allows us to find n eedles in haystacks in the form of very predictive but rarely seen features. Our paradigm stems fro m recent advances in stochastic optimization and online learning which employ proximal funct ions to control the gradient steps of the algorithm. We describe and analyze an apparatus for adap tively modifying the proximal function, which significantly simplifies setting a learning rate nd results in regret guarantees that are provably as good as the best proximal function that can be cho sen in hindsight. We give several efficient algorithms for empirical risk minimization probl ems with common and important regularization functions and domain constraints. We experimen tally study our theoretical analysis and show that adaptive subgradient methods outperform state-o f-the-art, yet non-adaptive, subgradient algorithms.


A 14-mW 6.25-Gb/s Transceiver in 90-nm CMOS

This paper describes a 6.25-Gb/s 14-mW transceiver in 90-nm CMOS for chip-to-chip applications. The transceiver employs a number of features for reducing power consumption, including a shared LC-PLL clock multiplier, an inductor-loaded resonant clock distribution network, a low- and programmable-swing voltage-mode transmitter, software-controlled clock and data recovery (CDR) and adaptive equalization within the receiver, and a novel PLL-based phase rotator for the CDR. The design can operate with channel attenuation of -15 dB or greater at a bit-error rate of 10-15 or less, while consuming less than 2.25 mW/Gb/s per transceiver.


Limbic-cortical dysregulation: a proposed model of depression.

A working model of depression implicating failure of the coordinated interactions of a distributed network of limbic-cortical pathways is proposed. Resting state patterns of regional glucose metabolism in idiopathic depressed patients, changes in metabolism with antidepressant treatment, and blood flow changes with induced sadness in healthy subjects were used to test and refine this hypothesis. Dorsal neocortical decreases and ventral paralimbic increases characterize both healthy sadness and depressive illness; concurrent inhibition of overactive paralimbic regions and normalization of hypofunctioning dorsal cortical sites characterize disease remission. Normal functioning of the rostral anterior cingulate, with its direct connections to these dorsal and ventral areas, is postulated to be additionally required for the observed reciprocal compensatory changes, since pretreatment metabolism in this region uniquely predicts antidepressant treatment response. This model is offered as an adaptable framework to facilitate continued integration of clinical imaging findings with complementary neuroanatomical, neurochemical, and electrophysiological studies in the investigation of the pathogenesis of affective disorders.


Image Steganography Using LSB and Edge – Detection Technique

217  Abstract-Steganography is the technique of hiding the fact that communication is taking place, by hiding data in other data. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exist a large variety of steganographic techniques some are more complex than others and all of them have respective strong and weak points. Steganalysis, the detection of this hidden information, is an inherently difficult problem and requires a thorough investigation so we are using " Edge detection Filter ". In this paper search how the edges of the images can be used to hiding text message in Steganography .It give the depth view of image steganography and Edge detection Filter techniques. Method: In this paper search how the edges of the images can be used to hiding text message in Steganography. For that gray image has been presented. In this method tried to find binary value of each character of text message and then in the next stage, tried to find dark places of gray image (black) by converting the original image to binary image for labeling each object of image by considering on 8 pixel connectivity. Then these images have been converted to RGB image in order to find dark places. Because in this way each sequence of gray color turns into RGB color and dark level of grey image is found by this way if the Gary image is very light the histogram must be changed manually to find just dark places. In the final stage each 8 pixels of dark places has been considered as a byte and binary value of each character has been put in low bit of each byte that was created manually by dark places pixels for increasing security of the main way of least Significant bit steganography. Steganalysis then used to evaluate the hiding process to ensure the data can be hidden in best possible way.


Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents.

We describe here a multiplexed protein quantitation strategy that provides relative and absolute measurements of proteins in complex mixtures. At the core of this methodology is a multiplexed set of isobaric reagents that yield amine-derivatized peptides. The derivatized peptides are indistinguishable in MS, but exhibit intense low-mass MS/MS signature ions that support quantitation. In this study, we have examined the global protein expression of a wild-type yeast strain and the isogenic upf1Delta and xrn1Delta mutant strains that are defective in the nonsense-mediated mRNA decay and the general 5' to 3' decay pathways, respectively. We also demonstrate the use of 4-fold multiplexing to enable relative protein measurements simultaneously with determination of absolute levels of a target protein using synthetic isobaric peptide standards. We find that inactivation of Upf1p and Xrn1p causes common as well as unique effects on protein expression.


An Efficient Certificateless Encryption for Secure Data Sharing in Public Clouds

We propose a mediated certificateless encryption scheme without pairing operations for securely sharing sensitive information in public clouds. Mediated certificateless public key encryption (mCL-PKE) solves the key escrow problem in identity based encryption and certificate revocation problem in public key cryptography. However, existing mCL-PKE schemes are either inefficient because of the use of expensive pairing operations or vulnerable against partial decryption attacks. In order to address the performance and security issues, in this paper, we first propose a mCL-PKE scheme without using pairing operations. We apply our mCL-PKE scheme to construct a practical solution to the problem of sharing sensitive information in public clouds. The cloud is employed as a secure storage as well as a key generation center. In our system, the data owner encrypts the sensitive data using the cloud generated users' public keys based on its access control policies and uploads the encrypted data to the cloud. Upon successful authorization, the cloud partially decrypts the encrypted data for the users. The users subsequently fully decrypt the partially decrypted data using their private keys. The confidentiality of the content and the keys is preserved with respect to the cloud, because the cloud cannot fully decrypt the information. We also propose an extension to the above approach to improve the efficiency of encryption at the data owner. We implement our mCL-PKE scheme and the overall cloud based system, and evaluate its security and performance. Our results show that our schemes are efficient and practical.


Clustal W and Clustal X version 2.0

SUMMARY
The Clustal W and Clustal X multiple sequence alignment programs have been completely rewritten in C++. This will facilitate the further development of the alignment algorithms in the future and has allowed proper porting of the programs to the latest versions of Linux, Macintosh and Windows operating systems.

AVAILABILITY
The programs can be run on-line from the EBI web server: http://www.ebi.ac.uk/tools/clustalw2. The source code and executables for Windows, Linux and Macintosh computers are available from the EBI ftp site ftp://ftp.ebi.ac.uk/pub/software/clustalw2/


Evaluating three treatments for borderline personality disorder: a multiwave study.

OBJECTIVE
The authors examined three yearlong outpatient treatments for borderline personality disorder: dialectical behavior therapy, transference-focused psychotherapy, and a dynamic supportive treatment.

METHOD
Ninety patients who were diagnosed with borderline personality disorder were randomly assigned to transference-focused psychotherapy, dialectical behavior therapy, or supportive treatment and received medication when indicated. Prior to treatment and at 4-month intervals during a 1-year period, blind raters assessed the domains of suicidal behavior, aggression, impulsivity, anxiety, depression, and social adjustment in a multiwave study design.

RESULTS
Individual growth curve analysis revealed that patients in all three treatment groups showed significant positive change in depression, anxiety, global functioning, and social adjustment across 1 year of treatment. Both transference-focused psychotherapy and dialectical behavior therapy were significantly associated with improvement in suicidality. Only transference-focused psychotherapy and supportive treatment were associated with improvement in anger. Transference-focused psychotherapy and supportive treatment were each associated with improvement in facets of impulsivity. Only transference-focused psychotherapy was significantly predictive of change in irritability and verbal and direct assault.

CONCLUSIONS
Patients with borderline personality disorder respond to structured treatments in an outpatient setting with change in multiple domains of outcome. A structured dynamic treatment, transference-focused psychotherapy was associated with change in multiple constructs across six domains; dialectical behavior therapy and supportive treatment were associated with fewer changes. Future research is needed to examine the specific mechanisms of change in these treatments beyond common structures.


A Corpus and Model Integrating Multiword Expressions and Supersenses

This paper introduces a task of identifying and semantically classifying lexical expressions in running text. We investigate the online reviews genre, adding semantic supersense annotations to a 55,000 word English corpus that was previously annotated for multiword expressions. The noun and verb supersenses apply to full lexical expressions, whether singleor multiword. We then present a sequence tagging model that jointly infers lexical expressions and their supersenses. Results show that even with our relatively small training corpus in a noisy domain, the joint task can be performed to attain 70% class labeling F1.


The neural basis of rationalization: cognitive dissonance reduction during decision-making.

People rationalize the choices they make when confronted with difficult decisions by claiming they never wanted the option they did not choose. Behavioral studies on cognitive dissonance provide evidence for decision-induced attitude change, but these studies cannot fully uncover the mechanisms driving the attitude change because only pre- and post-decision attitudes are measured, rather than the process of change itself. In the first fMRI study to examine the decision phase in a decision-based cognitive dissonance paradigm, we observed that increased activity in right-inferior frontal gyrus, medial fronto-parietal regions and ventral striatum, and decreased activity in anterior insula were associated with subsequent decision-related attitude change. These findings suggest the characteristic rationalization processes that are associated with decision-making may be engaged very quickly at the moment of the decision, without extended deliberation and may involve reappraisal-like emotion regulation processes.