title: Pre-Charged Local Bit-Line Sharing SRAM Architecture for Near-Threshold Operation

abstract: In this paper, a pre-charged local bit-line sharing (PCLBS) static random access memory (SRAM) for near-threshold operation is proposed. In previous local bit-line sharing SRAMs, such as average-8T and full-swing local bit-line (FSLB) SRAMs, multiple bit-cells share a local bit-line pair with a small capacitance for high read stability. However, the average-8T SRAM has a considerably large delay because the full development of the local bit-line cannot be achieved. On the other hand, the FSLB SRAM reduces the delay but requires a timing constraint of control signals to achieve sufficient read sensing margin. The proposed PCLBS SRAM achieves high read speed by fully developing local bit-line pair without a timing constraint. Furthermore, the proposed PCLBS SRAM enhances the read stability and the write ability by, respectively, applying a pre-charged local bit-line scheme and transmission gates in write paths. Based on a 22-nm FinFET technology, the FSLB and proposed PCLBS SRAM have the minimum operating voltages of 0.44 and 0.4 V, respectively, while achieving the $5\sigma$ target read stability and write ability yields. Compared with the FSLB SRAM, the proposed PCLBS SRAM consumes 21% less energy at each minimum operating voltage and has 57% smaller read delay at the operating voltage of 0.4 V.


title: Coloring Action Recognition in Still Images

abstract: In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color---shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification.


title: Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

abstract: Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest above ground biomass (AGB), many difficulties remain in data resources and prediction approaches. Each kind of sensor type and prediction method has its own merits and limitations. To select the proper estimation algorithm and remote-sensing data source, several forest AGB models were developed using different remote-sensing data sources (Geoscience Laser Altimeter System (GLAS) data and Thematic Mapper (TM) data) and 108 field measurements. Three modeling methods (stepwise regression (SR), support vector regression (SVR) and random forest (RF)) were used to estimate forest AGB over the Daxing’anling Mountains in northeastern China. The results of models using different datasets and three approaches were compared. The random forest AGB model using Landsat5/TM as input data was shown the acceptable modeling accuracy (R2 = 0.95 RMSE = 17.73 Mg/ha) and it was also shown to estimate AGB reliably by cross validation (R2 = 0.71 RMSE = 39.60 Mg/ha). The results also indicated that adding GLAS data significantly improved AGB predictions for the SVR and SR AGB models. In the case of the RF AGB models, including GLAS data no longer led to significant improvement. Finally, a forest biomass map with spatial resolution of 30 m over the Daxing'anling Mountains was generated using the obtained optimal model.


title: Deliberation for autonomous robots: A survey

abstract: Autonomous robots facing a diversity of open environments and performing a variety of tasks and interactions need explicit deliberation in order to fulfill their missions. Deliberation is meant to endow a robotic system with extended, more adaptable and robust functionalities, as well as reduce its deployment cost. The ambition of this survey is to present a global overview of deliberation functions in robotics and to discuss the state of the art in this area. The following five deliberation functions are identified and analyzed: planning, acting, monitoring, observing, and learning. The paper introduces a global perspective on these deliberation functions and discusses their main characteristics, design choices and constraints. The reviewed contributions are discussed with respect to this perspective. The survey focuses as much as possible on papers with a clear robotics content and with a concern on integrating several deliberation functions.


title: Context-aware Point-of-Interest Recommendation Using Tensor Factorization with Social Regularization

abstract: Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks in recent years. Compared with traditional tasks, it focuses more on personalized, context-aware recommendation results to provide better user experience. To address this new challenge, we propose a Collaborative Filtering method based on Non-negative Tensor Factorization, a generalization of the Matrix Factorization approach that exploits a high-order tensor instead of traditional User-Location matrix to model multi-dimensional contextual information. The factorization of this tensor leads to a compact model of the data which is specially suitable for context-aware POI recommendations. In addition, we fuse users' social relations as regularization terms of the factorization to improve the recommendation accuracy. Experimental results on real-world datasets demonstrate the effectiveness of our approach.


title: Position determination from modern radar systems-new theoretical results and their practical aspects

abstract: Several position estimation algorithms have been elaborated till now under assumptions that can be called classical ones. However, these algorithms may be generally ineffective when they are applied in modern radar systems. The problem of the object position estimation has been formulated under general assumptions corresponding to the real measurement conditions that exist (occur) in modern radar systems. The presence of unknown systematic measurement error (sensor misalignment errors, bias) and of navigation or geodetic errors (sensor position uncertainty) are taken into consideration. A general observation model is introduced. This model, called the extended observation model, takes into account the above-mentioned errors. An appropriate unbiased estimator of the object position has been obtained using the generalized least squares criterion. The estimator is presented in a compact form using a rectangular matrix pseudoinverse (Moore-Penrose generalized inverse). The observation model and estimator are general, and all known observation models and position estimators can be considered as particular cases of the observation model and estimator obtained.


title: Predicting student success based on prior performance

abstract: Colleges and universities are increasingly interested in tracking student progress as they monitor and work to improve their retention and graduation rates. Ideally, early indicators of student progress, or lack thereof, can be used to provide appropriate interventions that increase the likelihood of student success. In this paper we present a framework that uses machine learning, and in particular, a Bayesian Belief Network (BBN), to predict the performance of students early in their academic careers. The results obtained show that the proposed framework can predict student progress, specifically student grade point average (GPA) within the intended major, with minimal error after observing a single semester of performance. Furthermore, as additional performance is observed, the predicted GPA in subsequent semesters becomes increasingly accurate, providing the ability to advise students regarding likely success outcomes early in their academic careers.


title: Gamification of Cognitive Assessment and Cognitive Training: A Systematic Review of Applications and Efficacy

abstract: Background: Cognitive tasks are typically viewed as effortful, frustrating, and repetitive, which often leads to participant disengagement. This, in turn, may negatively impact data quality and/or reduce intervention effects. However, gamification may provide a possible solution. If game design features can be incorporated into cognitive tasks without undermining their scientific value, then data quality, intervention effects, and participant engagement may be improved. Objectives: This systematic review aims to explore and evaluate the ways in which gamification has already been used for cognitive training and assessment purposes. We hope to answer 3 questions: (1) Why have researchers opted to use gamification? (2) What domains has gamification been applied in? (3) How successful has gamification been in cognitive research thus far? Methods: We systematically searched several Web-based databases, searching the titles, abstracts, and keywords of database entries using the search strategy (gamif* OR game OR games) AND (cognit* OR engag* OR behavi* OR health* OR attention OR motiv*). Searches included papers published in English between January 2007 and October 2015. Results: Our review identified 33 relevant studies, covering 31 gamified cognitive tasks used across a range of disorders and cognitive domains. We identified 7 reasons for researchers opting to gamify their cognitive training and testing. We found that working memory and general executive functions were common targets for both gamified assessment and training. Gamified tests were typically validated successfully, although mixed-domain measurement was a problem. Gamified training appears to be highly engaging and does boost participant motivation, but mixed effects of gamification on task performance were reported. Conclusions: Heterogeneous study designs and typically small sample sizes highlight the need for further research in both gamified training and testing. Nevertheless, careful application of gamification can provide a way to develop engaging and yet scientifically valid cognitive assessments, and it is likely worthwhile to continue to develop gamified cognitive tasks in the future. [JMIR Serious Games 2016;4(2):e11]


Outlier Processing Via L1-Principal Subspaces.


title: 1|A Kalman filter-based algorithm for IMU-camera calibration

abstract: Vision-aided Inertial Navigation Systems (V-INS) can provide precise state estimates for the 3D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an IMU with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMU-camera calibration process causes biases that reduce the accuracy of the estimation process and can even lead to divergence. In this paper, we present a Kalman filter-based algorithm for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlations of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3D laser scanner) except a calibration target. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.


title: Enabling JSON Document Stores in Relational Systems

abstract: In recent years, “document store” NoSQL systems have exploded in popularity, largely driven by the adoption of the JSON data model in Web and mobile applications. The advantages of these NoSQL document store systems (like MongoDB and CouchDB) are tempered by a lack of traditional RDBMS features, notably a sophisticated declarative query language, rich native query processing constructs (e.g. joins), and transaction management providing ACID safety guarantees. With this paper, we hope to spark a discussion in the database community about how JSON data can be supported on top of a relational infrastructure, and how the best features of JSON document stores and relational databases can be combined. We present Argo, a proof-ofconcept mapping layer for storing and querying JSON data in a relational system with an easy-to-use SQL-like query language. We also present NoBench, a micro-benchmark suite for queries over JSON data in NoSQL and SQL systems. Our results point to directions of how one can marry the best of both worlds, combining the flexibility and interoperability of JSON with the rich query processing and transactional properties offered by a traditional RDBMS. Note: An extended version of this paper is available at [6]. The Argo source code is available at http://research.cs. wisc.edu/quickstep/argo/argo.tar.bz2


Unsupervised Domain Adaptation With Distribution Matching Machines


title: Application in Market Basket Research Based on FP-Growth Algorithm

abstract: Market basket analysis gives us insight into the merchandise by telling us which products tend to be purchased together and which are most enable to purchase. The market basket analysis is a powerful tool especially in retailing it is essential to discover large baskets, since it deals with thousands of items. FP-growth algorithm is an efficient algorithm for mining frequent patterns. It does not need to produce the candidate sets and that is quite time consuming. It scans database only twice and frequent item set is mining by using of FP tree. In this paper, Visual C++ is applied to design the program to mine the frequent item sets using FP-growth algorithm. According to the mining result, the merchandise in the supermarket is arranged together in the same place well-suited for customer.


the Implicit Association Test: IV What We Know (So Far) about the Method


Associative Compression Networks


title: Combining full text and bibliometric information in mapping scientific disciplines

abstract: In the present study results of an earlier pilot study by Glenisson, Glanzel and Persson are extended on the basis of larger sets of papers. Full text analysis and traditional bibliometric methods are serially combined to improve the efficiency of the two individual methods. The text mining methodology already introduced in the pilot study is applied to the complete publication year 2003 of the journal Scientometrics. Altogether 85 documents that can be considered research articles or notes have been selected for this exercise. The outcomes confirm the main results of the pilot study, namely, that such hybrid methodology can be applied to both research evaluation and information retrieval. Nevertheless, Scientometrics documents published in 2003 cover a much broader and more heterogeneous spectrum of bibliometrics and related research than those analysed in the pilot study. A modified subject classification based on the scheme used in an earlier study by Schoepflin and Glanzel has been applied for validation purposes.


title: Where to start reading a textual XML document?

abstract: In structured information retrieval, the aim is to exploit document structure to retrieve relevant components, allowing the user to go straight to the relevant material. This paper looks at the so-called best entry points (BEPs), which are intended to give the user the best starting point to access the relevant information in the document. We examine the relationship between BEPs and relevant components in the INEX 2006 ad hoc assessments. Our main findings are the following: First, although documents are short, assessors often choose the best entry point some distance from the start of the document. Second, many of the best entry points coincide with the first relevant character in relevant documents, showing a strong relation between the BEP and relevant text. Third, we find browsing BEPs in articles with a single relevant passages, and container BEPs or context BEPs in articles with more relevant passages.


title: JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music with LSTMs

abstract: We propose a novel approach for the generation of polyphonic music based on LSTMs. We generate music in two steps. First, a chord LSTM predicts a chord progression based on a chord embedding. A second LSTM then generates polyphonic music from the predicted chord progression. The generated music sounds pleasing and harmonic, with only few dissonant notes. It has clear long-term structure that is similar to what a musician would play during a jam session. We show that our approach is sensible from a music theory perspective by evaluating the learned chord embeddings. Surprisingly, our simple model managed to extract the circle of fifths, an important tool in music theory, from the dataset.


title: A spatio-temporal extension to Isomap nonlinear dimension reduction

abstract: We present an extension of Isomap nonlinear dimension reduction (Tenenbaum et al., 2000) for data with both spatial and temporal relationships. Our method, ST-Isomap, augments the existing Isomap framework to consider temporal relationships in local neighborhoods that can be propagated globally via a shortest-path mechanism. Two instantiations of ST-Isomap are presented for sequentially continuous and segmented data. Results from applying ST-Isomap to real-world data collected from human motion performance and humanoid robot teleoperation are also presented.


title: Face Recognition System Based on Principal Component Analysis (PCA) with Back

abstract: Face recognition has received substantial attention from researches in biometrics, pattern recognition field and computer vision communities. Face recognition can be applied in Security measure at Air ports, Passport verification, Criminals list verification in police department, Visa processing , Verification of Electoral identification and Card Security measure at ATM's. In this paper, a face recognition system for personal identification and verification using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) is proposed. This system consists on three basic steps which are automatically detect human face image using BPNN, the various facial features extraction, and face recognition are performed based on Principal Component Analysis (PCA) with BPNN. The dimensionality of face image is reduced by the PCA and the recognition is done by the BPNN for efficient and robust face recognition. In this paper also focuses on the face database with different sources of variations, especially Pose, Expression, Accessories, Lighting and backgrounds would be used to advance the state-of-the-art face recognition technologies aiming at practical applications


title: A survey of distributed denial of service attack

abstract: Information security deals with a large number of subjects like spoofed message detection, audio processing, video surveillance and cyber-attack detections. However the biggest threat for the homeland security is cyber-attacks. Distributed Denial of Service attack is one among them. Interconnected systems such as database server, web server, cloud computing servers etc., are now under threads from network attackers. Denial of service is common attack in the internet which causes problem for both the user and the service providers. Distributed attack sources can be used to enlarge the attack in case of Distributed Denial of Service so that the effect of the attack will be high. Distributed Denial of Service attacks aims at exhausting the communication and computational power of the network by flooding the packets through the network and making malicious traffic in the network. In order to be an effective service the DDoS attack must be detected and mitigated quickly before the legitimate user access the attacker's target. The group of systems that is used to perform the DoS attack is known as the botnets. This paper introduces the overview of the state of art in DDoS attack detection strategies.


title: Cascaded H-bridge multilevel converter topology and three-phase balance control for large scale photovoltaic systems

abstract: The increase in the power levels of photovoltaic (PV) energy conversion systems has resulted in new large-scale grid connected configurations that have reached the megawatt level. This substantial increment in the power levels imposes new challenges to the grid interfacing converter, and therefore results in new opportunities to be explored. This work introduces a new medium voltage multilevel scheme based on a three-phase cascaded H-bridge (CHB) converter and multiple PV strings. The proposed configuration enables a large increase of the total power capacity of the PV system, while the introduction of a multilevel converter helps to improve both power quality and efficiency and medium voltage operation at the grid side. The main challenge of the proposed configuration is to handle the inherent power imbalances that occur not only between the different cells of one phase of the converter but also between the three phases. Simulation results of a 7-level CHB PV system are presented to validate the proposed topology and control method.


title: Identity Management and Mental Health Discourse in Social Media

abstract: Social media is increasingly being adopted in health discourse. We examine the role played by identity in supporting discourse on socially stigmatized conditions. Specifically, we focus on mental health communities on reddit. We investigate the characteristics of mental health discourse manifested through reddit's characteristic 'throwaway' accounts, which are used as proxies of anonymity. For the purpose, we propose affective, cognitive, social, and linguistic style measures, drawing from literature in psychology. We observe that mental health discourse from throwaways is considerably disinhibiting and exhibits increased negativity, cognitive bias and self-attentional focus, and lowered self-esteem. Throwaways also seem to be six times more prevalent as an identity choice on mental health forums, compared to other reddit communities. We discuss the implications of our work in guiding mental health interventions, and in the design of online communities that can better cater to the needs of vulnerable populations. We conclude with thoughts on the role of identity manifestation on social media in behavioral therapy.


title: Physical unclonable functions for device authentication and secret key generation

abstract: Physical Unclonable Functions (PUFs) are innovative circuit primitives that extract secrets from physical characteristics of integrated circuits (ICs). We present PUF designs that exploit inherent delay characteristics of wires and transistors that differ from chip to chip, and describe how PUFs can enable low-cost authentication of individual ICs and generate volatile secret keys for cryptographic operations.


title: Predictive user click models based on click-through history

abstract: Web search engines consistently collect information about users interaction with the system: they record the query they issued, the URL of presented and selected documents along with their ranking. This information is very valuable: It is a poll over millions of users on the most various topics and it has been used in many ways to mine users interests and preferences. Query logs have the potential to partially alleviate the search engines from thousand of searches by providing a way to predict answers for a subset of queries and users without knowing the content of a document. Even if the predicted result is at rank one, this analysis might be of interest: If there is enough confidence on a user's click, we might redirect the user directly to the page whose link would be clicked. In this paper, we present three different models for predicting user clicks, ranging from most specific ones (using only past user history for the query) to very general ones (aggregating data over all users for a given query). The former model has a very high precision at low recall values, while the latter can achieve high recalls. We show that it is possible to combine the different models to predict with high accuracy (over 90%) a high subset of query sessions (24% of all the sessions).


title: Fuel cell power conditioning for electric power applications: a summary

abstract: Fuel cells are considered to be one of the most promising sources of distributed energy because of their high efficiency, low environmental impact and scalability. Unfortunately, multiple complications exist in fuel cell operation. Fuel cells cannot accept current in the reverse direction, do not perform well with ripple current, have a low output voltage that varies with age and current, respond sluggishly to step changes in load and are limited in overload capabilities. For these reasons, power converters are often necessary to boost and regulate the voltage as a means to provide a stiff applicable DC power source. Furthermore, the addition of an inverter allows for the conversion of DC power to AC for an utility interface or for the application of an AC motor. To help motivate the use of power conditioning for the fuel cell, a brief introduction of the different types, applications and typical electrical characteristics of fuel cells is presented. This is followed by an examination of the various topologies of DC-DC boost converters and inverters used for power conditioning of fuel cells. Several architectures to aggregate multiple fuel cells for high-voltage/high-power applications are also reviewed.


title: An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation

abstract: The multilevel capability of metal oxide resistive switching memory was explored for the potential use as a single-element electronic synapse device. TiN/HfOx/AlOx/ Pt resistive switching cells were fabricated. Multilevel resistance states were obtained by varying the programming voltage amplitudes during the pulse cycling. The cell conductance could be continuously increased or decreased from cycle to cycle, and about 105 endurance cycles were obtained. Nominal energy consumption per operation is in the subpicojoule range with a maximum measured value of 6 pJ. This low energy consumption is attractive for the large-scale hardware implementation of neuromorphic computing and brain simulation. The property of gradual resistance change by pulse amplitudes was exploited to demonstrate the spike-timing-dependent plasticity learning rule, suggesting that metal oxide memory can potentially be used as an electronic synapse device for the emerging neuromorphic computation system.


Hippocampal Cytogenesis Correlates to Escitalopram-Mediated Recovery in a Chronic Mild Stress Rat Model of Depression


title: Microservices Architecture Based Cloudware Deployment Platform for Service Computing

abstract: With the rising of Cloud computing, evolution have occurred not only in datacenter, but also in software development, deployment, maintain and usage. How to build cloud platform for traditional software, and how to deliver cloud service to users are central research fields which will have a huge impact. In recent years, the development of microservice and container technology make software paradigm evolve towards Cloudware in cloud environment. Cloudware, which is based on service and supported by cloud platform, is an important method to cloudalize traditional software. It is also a significant way for software development, deployment, maintenance and usage in future cloud environment. Furthermore, it creates a completely new thought for software in cloud platform. In this paper, we proposed a new Cloudware PaaS platform based on microservice architecture and light weighted container technology. We can directly deploy traditional software which provides services to users by browser in this platform without any modification. By utilizing the microservice architecture, this platform has the characteristics of scalability, auto-deployment, disaster recovery and elastic configuration.


title: Privacy-Enhancing of User's Behaviour Toward Privacy Settings in Social Networking Sites

abstract: Social Networking Sites (SNSs) are applications that allow users to create personal profiles to interact with friends or public and to share data such as photos and short videos. The amount of these personal disclosures has raised issues and concerns regarding SNSs' privacy. Users' attitudes toward privacy and their sharing behaviours are inconsistent because they are concerned about privacy, but continue sharing personal information. Also, the existing privacy settings are not flexible enough to prevent privacy risks. In this paper, we propose a novel model called Privacy Settings Model (PSM) that can lead users to understand, control, and update SNSs' privacy settings. We believe that this model will enhance their privacy behaviours toward SNSs' privacy settings and reduce privacy risks.


Polynomial texture mapping : a new tool for examining the surface of paintings


title: Asymptotic Theory of Supersonic Viscous Gas Flows

abstract: This is the first book in English devoted to the latest developments in fluid mechanics and aerodynamics. Written by the leading authors in the field, based at the renowned Central Aerohydrodynamic Institute in Moscow, it deals with viscous gas flow problems that arise from supersonic flows. These complex problems are central to the work of researchers and engineers delaing with new aircraft and turbomachinery development (jet engines, compressors and other turbine equipment). The book presents the latest asymptotical models, simplified Navier-Stokes equations and viscous-inviscid interaction theroies and will be of critical interest to researchers, engineers, academics and advanced graduate students in the areas of fluid mechanics, compressible flows, aerodynamics and aircraft design, applied mathematics and computational fluid dynamics.This is the first book in English to cover the latest methodology for incopressible flow analysis of high speed aerodynamics, an essential topic for those working on new generation aircraft and turbomachinery. Authors are internationally recognised as the leading figures in the field. It includes a chapter introducing asymptotical methods to enable advanced level students to use the book.


A Survey on Automatic Twitter Event Summarization.


Predictive Current Method for Single Open-phase Fault Detection in Permanent Magnet Synchronous Machine


title: Design and Analysis of a Low-Profile and Broadband Microstrip Monopolar Patch Antenna

abstract: A new microstrip monopolar patch antenna is proposed and analyzed. The antenna has a wide bandwidth and a monopole like radiation pattern. Such antenna is constructed on a circular patch antenna that is shorted concentrically with a set of conductive vias. The antenna is analyzed using a cavity model. The cavity model analysis not only distinguishes each resonating mode and gives a physical insight into each mode of the antenna, but also provides a guideline to design a broadband monopolar patch antenna that utilizes two modes (TM01 and TM02 modes). Both modes provide a monopole like radiation pattern. The proposed antenna has a simple structure with a low profile of 0.024 wavelengths, and yields a wide impedance bandwidth of 18% and a maximum gain of 6 dBi.


Mining as a Strategy for Qualitative Researchers


Knowledge Graph Visualization for Understanding Ideas


title: Radiation Constrained Scheduling of Wireless Charging Tasks

abstract: This paper studies the problem of Radiation cOnstrained scheduling of wireless Charging tasKs (ROCK), that is, given wireless charging tasks with required charging energy and charging deadline for rechargeable devices, scheduling the power of wireless chargers to maximize the overall effective charging energy for all rechargeable devices, and further to minimize the total charging time, while guaranteeing electromagnetic radiation (EMR) safety, i.e., no point on the considered 2D area has EMR intensity exceeding a given threshold. To address ROCK, we first present a centralized algorithm. We transform ROCK from nonlinear problem to linear problem by applying two approaches of area discretization and solution regularization, and then propose a linear programming based greedy test algorithm to solve it. We also propose a distributed algorithm by presenting an area partition scheme and two approaches called area-scaling and EMR-scaling, and prove that it achieves effective charging energy no less than (1 -- e) of that of the optimal solution, and charging time no more than that of the optimal solution. We conduct both simulation and field experiments to validate our theoretical findings. The results show that our algorithm achieves 94.9% of the optimal effective charging energy and requires 47.1% smaller charging time compared with the optimal one when e ≥ 0.2, and outperforms the other algorithms by at least 350.1% in terms of charging time with even more effective charging energy.


title: Bus Architectures For Safety-Critical Embedded Systems ?

abstract: Embedded systems for safety-critical applications often integrate multiple "functions" and must generally be fault-tolerant. These requirements lead to a need for mechanisms and services that provide protection against fault propagation and ease the construction of distributed fault-tolerant applications.A number of bus architectures have been developed to satisfy this need. This paper reviews the requirements on these architectures, the mechanisms employed, and the services provided. Four representative architectures (SAFEbusTM, SPIDER,TTA, and FlexRay) are briefly described.


title: Scalable detection of semantic clones

abstract: Several techniques have been developed for identifying similar code fragments in programs. These similar fragments, referred to as code clones, can be used to identify redundant code, locate bugs, or gain insight into program design. Existing scalable approaches to clone detection are limited to finding program fragments that are similar only in their contiguous syntax. Other, semantics-based approaches are more resilient to differences in syntax, such as reordered statements, related statements interleaved with other unrelated statements, or the use of semantically equivalent control structures. However, none of these techniques have scaled to real world code bases. These approaches capture semantic information from Program Dependence Graphs (PDGs), program representations that encode data and control dependencies between statements and predicates. Our definition of a code clone is also based on this representation: we consider program fragments with isomorphic PDGs to be clones. In this paper, we present the first scalable clone detection algorithm based on this definition of semantic clones. Our insight is the reduction of the difficult graph similarity problem to a simpler tree similarity problem by mapping carefully selected PDG subgraphs to their related structured syntax. We efficiently solve the tree similarity problem to create a scalable analysis. We have implemented this algorithm in a practical tool and performed evaluations on several million-line open source projects, including the Linux kernel. Compared with previous approaches, our tool locates significantly more clones, which are often more semantically interesting than simple copied and pasted code fragments.


title: Identification of motor neuron disease using wavelet domain features extracted from EMG signal

abstract: Amyotrophic lateral sclerosis (ALS) is a common fatal motor neuron disease that assails the nerve cells in the brain. As the nervous system controls the muscle activity, the electromyography (EMG) signals can be viewed and examined in order to detect the vital features of the ALS disease in individuals. In this paper, the discrete wavelet transform (DWT) based features, which are extracted from a frame of EMG data, are introduced to classify the normal person and the ALS patients. From each frame of EMG data, instead of using a large number of DWT coefficients, the DWT coefficients with higher values as well as their mean and maxima are proposed to be used, which drastically reduces the feature dimension. It is shown that the proposed feature vector offers a high within class compactness and between class separations. For the purpose of classification, the K-nearest neighborhood classifier is employed. In order to demonstrate the classification performance, an EMG database consisted of 5 normal subjects and 5 ALS patients is considered and it is found that the proposed method is capable of distinctly separating the ALS patients from the normal persons.


title: Word Sense Induction for Novel Sense Detection

abstract: We apply topic modelling to automatically induce word senses of a target word, and demonstrate that our word sense induction method can be used to automatically detect words with emergent novel senses, as well as token occurrences of those senses. We start by exploring the utility of standard topic models for word sense induction (WSI), with a pre-determined number of topics (=senses). We next demonstrate that a non-parametric formulation that learns an appropriate number of senses per word actually performs better at the WSI task. We go on to establish state-of-the-art results over two WSI datasets, and apply the proposed model to a novel sense detection task.


3D-CNN in Drug Resistance Detection and Tuberculosis Classification


title: Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods

abstract: Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of multivariate analysis (MVA). This article provides a uniform treatment of several methods: principal component analysis (PCA), partial least squares (PLS), canonical correlation analysis (CCA), and orthonormalized PLS (OPLS), as well as their nonlinear extensions derived by means of the theory of reproducing kernel Hilbert spaces (RKHSs). We also review their connections to other methods for classification and statistical dependence estimation and introduce some recent developments to deal with the extreme cases of large-scale and low-sized problems. To illustrate the wide applicability of these methods in both classification and regression problems, we analyze their performance in a benchmark of publicly available data sets and pay special attention to specific real applications involving audio processing for music genre prediction and hyperspectral satellite image processing for Earth and climate monitoring.


title: Taper Design of Vivaldi and Co-Planar Tapered Slot Antenna (TSA) by Chebyshev Transformer

abstract: A new method for the flare's opening rate for a single radiator tapered slot antenna (TSA) is presented. A stepped quarter-wave Chebyshev transformer is calculated to minimize the frequency domain reflection and maximize the power transfer gain. Then slot widths are computed to match the Chebychev design, and smooth curves are fit to the computed steps. Different smoothing functions are compared, including the conventional exponential function, as well as cubic spline and Gaussian. The validity of the method is verified through simulations and measurements of experimental designs for the 1 to 3 GHz frequency range. In this work the TSA is fabricated on one side of a single layer Duroid substrate with dielectric constant 6.15. Although the Chebyshev transformer is an old concept, the novelty of this method is applying the transformer to design the TSA slotline taper profile, leading to a closed design procedure rather than an optimization. Advantages of this novel method are the straightforward mathematics of the design and that a simple simulation program can be written in a short time.


title: Prediction of Prospective User Engagement with Intelligent Assistants

abstract: Intelligent assistants on mobile devices, such as Siri, have recently gained considerable attention as novel applications of dialogue technologies. A tremendous amount of real users of intelligent assistants provide us with an opportunity to explore a novel task of predicting whether users will continually use their intelligent assistants in the future. We developed prediction models of prospective user engagement by using large-scale user logs obtained from a commercial intelligent assistant. Experiments demonstrated that our models can predict prospective user engagement reasonably well, and outperforms a strong baseline that makes prediction based past utterance frequency.


title: Real time hand gesture recognition for computer interaction

abstract: Hand gesture recognition is a natural and intuitive way to interact with the computer, since interactions with the computer can be increased through multidimensional use of hand gestures as compare to other input methods. The purpose of this paper is to explore three different techniques for HGR (hand gesture recognition) using finger tips detection. A new approach called “Curvature of Perimeter” is presented with its application as a virtual mouse. The system presented, uses only a webcam and algorithms which are developed using computer vision, image and the video processing toolboxes of Matlab.


Subjective Well-Being and Social Media. A Semantically Annotated Twitter Corpus on Fertility and Parenthood


title: AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling

abstract: In vision-enabled autonomous systems such as robots and autonomous cars, video object detection plays a crucial role, and both its speed and accuracy are important factors to provide reliable operation. The key insight we show in this paper is that speed and accuracy are not necessarily a trade-off when it comes to image scaling. Our results show that re-scaling the image to a lower resolution will sometimes produce better accuracy. Based on this observation, we propose a novel approach, dubbed AdaScale, which adaptively selects the input image scale that improves both accuracy and speed for video object detection. To this end, our results on ImageNet VID and mini YouTube-BoundingBoxes datasets demonstrate 1.3 points and 2.7 points mAP improvement with 1.6x and 1.8x speedup, respectively. Additionally, we improve state-of-the-art video acceleration work by an extra 1.25x speedup with slightly better mAP on ImageNet VID dataset.


title: Cadmium-induced gene expression changes in the mouse embryo, and the influence of pretreatment with zinc.

abstract: Cadmium (Cd) administered to female C57BL/6 mice on gestation day 8 induces a high incidence of anterior neural tube defects (exencephaly). This adverse effect can be attenuated by maternal pretreatment with zinc (Zn). In this study we used replicated microarray analysis and real-time PCR to investigate gene expression changes induced in the embryo 5 and 10h after maternal Cd exposure in the absence or presence of Zn pretreatment. We report nine genes with a transcriptional response induced by Cd, none of which was influenced by Zn pretreatment, and two genes induced only by combined maternal Cd exposure and Zn pretreatment. We discuss the results in relation to the possibility that Cd is largely excluded from the embryo, that the teratogenic effects of Cd may be secondary to toxicity in extraembryonic tissues, and that the primary protective role of Zn may not be to reverse Cd-induced transcription in the embryo.


title: Finding lost wrenches: Using continuum robots for contact detection and estimation of contact location

abstract: This paper presents a novel modeling framework for contact detection and estimation of contact location on multi-backbone continuum robots. The paper presents modified kinematics for constrained continuum robots and introduces the concept of Fixed Centrode Deviation (FCD) for continuum robots. It is shown that the change in fixed centrode locus may be used for detection of contact and for contact location estimation. An alternative method for contact detection using Joint Force Deviation (JFD) is investigated and a lower bound for contact detectability is derived while considering uncertainties in joint forces. An estimation framework for the location of contact is presented based on FCD and the constrained kinematics Jacobian of the continuum robot. These methods are validated by simulation and experiments. This framework may be used for enhancing the safety of robotic surgical slaves and for exploration of unknown environments such as in scenarios of search and rescue.


title: APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery

abstract: This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting association rule learning to subgroup discovery. This was achieved by building a classification rule learner APRIORI-C, enhanced with a novel post-processing mechanism, a new quality measure for induced rules (weighted relative accuracy) and using probabilistic classification of instances. Results of APRIORI-SD are similar to the subgroup discovery algorithm CN2-SD while experimental comparisons with CN2, RIPPER and APRIORI-C demonstrate that the subgroup discovery algorithm APRIORI-SD produces substantially smaller rule sets, where individual rules have higher coverage and significance.


title: Making Big Data, Privacy, and Anonymization Work Together in the Enterprise: Experiences and Issues

abstract: Some scholars feel that Big Data techniques render anonymization (also known as de-identification) useless as a privacy protection technique. This paper discusses our experiences and issues encountered when we successfully combined anonymization, privacy protection, and Big Data techniques to analyze usage data while protecting the identities of users. Our Human Factors Engineering team wanted to use web page access logs and Big Data tools to improve usability of Intel's heavily used internal web portal. To protect Intel employees' privacy, they needed to remove personally identifying information (PII) from the portal's usage log repository but in a way that did not affect the use of Big Data tools to do analysis or the ability to re-identify a log entry in order to investigate unusual behavior. To meet these objectives, we created an open architecture for anonymization that allowed a variety of tools to be used for both de-identifying and re-identifying web log records. In the process of implementing our architecture, we found that enterprise data has properties different from the standard examples in anonymization literature. Our proof of concept showed that Big Data techniques could yield benefits in the enterprise environment even when working on anonymized data. We also found that despite masking obvious PII like usernames and IP addresses, the anonymized data was vulnerable to correlation attacks. We explored the tradeoffs of correcting these vulnerabilities and found that User Agent (Browser/OS) information strongly correlates to individual users. While browser fingerprinting has been known before, it has implications for tools and products currently used to de-identify enterprise data. We conclude that Big Data, anonymization, and privacy can be successfully combined but requires analysis of data sets to make sure that anonymization is not vulnerable to correlation attacks.


Mechanical Behaviour of TiAl Spherical Particles Including Friction Effect


title: Decision making under stress: A selective review

abstract: Many decisions must be made under stress, and many decision situations elicit stress responses themselves. Thus, stress and decision making are intricately connected, not only on the behavioral level, but also on the neural level, i.e., the brain regions that underlie intact decision making are regions that are sensitive to stress-induced changes. The purpose of this review is to summarize the findings from studies that investigated the impact of stress on decision making. The review includes those studies that examined decision making under stress in humans and were published between 1985 and October 2011. The reviewed studies were found using PubMed and PsycInfo searches. The review focuses on studies that have examined the influence of acutely induced laboratory stress on decision making and that measured both decision-making performance and stress responses. Additionally, some studies that investigated decision making under naturally occurring stress levels and decision-making abilities in patients who suffer from stress-related disorders are described. The results from the studies that were included in the review support the assumption that stress affects decision making. If stress confers an advantage or disadvantage in terms of outcome depends on the specific task or situation. The results also emphasize the role of mediating and moderating variables. The results are discussed with respect to underlying psychological and neural mechanisms, implications for everyday decision making and future research directions.


title: Survey on distance metric learning and dimensionality reduction in data mining

abstract: Distance metric learning is a fundamental problem in data mining and knowledge discovery. Many representative data mining algorithms, such as $$k$$ k -nearest neighbor classifier, hierarchical clustering and spectral clustering, heavily rely on the underlying distance metric for correctly measuring relations among input data. In recent years, many studies have demonstrated, either theoretically or empirically, that learning a good distance metric can greatly improve the performance of classification, clustering and retrieval tasks. In this survey, we overview existing distance metric learning approaches according to a common framework. Specifically, depending on the available supervision information during the distance metric learning process, we categorize each distance metric learning algorithm as supervised, unsupervised or semi-supervised. We compare those different types of metric learning methods, point out their strength and limitations. Finally, we summarize open challenges in distance metric learning and propose future directions for distance metric learning.


title: Information Systems Opportunities in Brain–Machine Interface Decoders

abstract: Brain-machine interface (BMI) systems convert neural signals from motor regions of the brain into control signals to guide prosthetic devices. The ultimate goal of BMIs is to improve the quality of life for people with paralysis by providing direct neural control of prosthetic arms or computer cursors. While considerable research over the past 15 years has led to compelling BMI demonstrations, there remain several challenges to achieving clinically viable BMI systems. In this review, we focus on the challenge of increasing BMI performance and robustness. We review and highlight key aspects of intracortical BMI decoder design, which is central to the conversion of neural signals into prosthetic control signals, and discuss emerging opportunities to improve intracortical BMI decoders. This is one of the primary research opportunities where information systems engineering can directly impact the future success of BMIs.


title: Low-Cost Epoch-Based Correlation Prefetching for Commercial Applications

abstract: The performance of many important commercial workloads, such as on-line transaction processing, is limited by the frequent stalls due to off-chip instruction and data accesses. These applica- tions are characterized by irregular control flow and complex data access patterns that render many low-cost prefetching schemes, such as stream-based and stride-based prefetching, ineffective. For such applications, correlation-based prefetching, which is ca- pable of capturing complex data access patterns, has been shown to be a more promising approach. However, the large instruction and data working sets of these applications require extremely large correlation tables, making these tables impractical to be im- plemented on-chip. This paper proposes the epoch-based correla- tion prefetcher, which cost-effectively stores its correlation table in main memory and exploits the concept of epochs to hide the long latency of its correlation table access, and which attempts to elim- inate entire epochs instead of individual instruction and data miss- es. Experimental results demonstrate that the epoch-based correlation prefetcher, which requires minimal on-chip real estate to implement, improves the performance of a suite of important commercial benchmarks by 13% to 31% and significantly outper- forms previously proposed correlation prefetchers.


title: Low power VLSI architecture for combined FMO/Manchester encoder for reusability and FMO/Manchester codecs

abstract: The VLSI architecture for the low power combined FM0 and Manchester encoder (SOLS) circuit using modified GDI has been proposed in this paper. Comparisons are made with existing architecture using general CMOS Logic. The power consumption and delay in this circuit are reduced. The working conditions for existing circuit for FMO/Manchester encoder and decoder using HCPM technique have also been modified in this paper. The architecture has been realized in CMOS 90 nm technology. Cadence Virtuoso Analog design environment has been used for implementing and synthesizing the test circuits.


title: Attentive Linear Transformation for Image Captioning

abstract: We propose a novel attention framework called attentive linear transformation (ALT) for automatic generation of image captions. Instead of learning the spatial or channel-wise attention in existing models, ALT learns to attend to the high-dimensional transformation matrix from the image feature space to the context vector space. Thus ALT can learn various relevant feature abstractions, including spatial attention, channel-wise attention, and visual dependence. Besides, we propose a soft threshold regression to predict the spatial attention probabilities. It preserves more relevant local regions than popular softmax regression. Extensive experiments on the MS COCO and the Flickr30k data sets all demonstrate the superiority of our model compared with other state-of-the-art models.


title: List-wise learning to rank with matrix factorization for collaborative filtering

abstract: A ranking approach, ListRank-MF, is proposed for collaborative filtering that combines a list-wise learning-to-rank algorithm with matrix factorization (MF). A ranked list of items is obtained by minimizing a loss function that represents the uncertainty between training lists and output lists produced by a MF ranking model. ListRank-MF enjoys the advantage of low complexity and is analytically shown to be linear with the number of observed ratings for a given user-item matrix. We also experimentally demonstrate the effectiveness of ListRank-MF by comparing its performance with that of item-based collaborative recommendation and a related state-of-the-art collaborative ranking approach (CoFiRank).


title: Piecing together the NFV provisioning puzzle: Efficient placement and chaining of virtual network functions

abstract: Network Function Virtualization (NFV) is a promising network architecture concept, in which virtualization technologies are employed to manage networking functions via software as opposed to having to rely on hardware to handle these functions. By shifting dedicated, hardware-based network function processing to software running on commoditized hardware, NFV has the potential to make the provisioning of network functions more flexible and cost-effective, to mention just a few anticipated benefits. Despite consistent initial efforts to make NFV a reality, little has been done towards efficiently placing virtual network functions and deploying service function chains (SFC). With respect to this particular research problem, it is important to make sure resource allocation is carefully performed and orchestrated, preventing over- or under-provisioning of resources and keeping end-to-end delays comparable to those observed in traditional middlebox-based networks. In this paper, we formalize the network function placement and chaining problem and propose an Integer Linear Programming (ILP) model to solve it. Additionally, in order to cope with large infrastructures, we propose a heuristic procedure for efficiently guiding the ILP solver towards feasible, near-optimal solutions. Results show that the proposed model leads to a reduction of up to 25% in end-to-end delays (in comparison to chainings observed in traditional infrastructures) and an acceptable resource over-provisioning limited to 4%. Further, we demonstrate that our heuristic approach is able to find solutions that are very close to optimality while delivering results in a timely manner.


title: An ultra low power ASK demodulator for passive UHF RFID tag

abstract: In this paper, an Amplitude Shift Keying (ASK) demodulator circuit for passive UHF Radio Frequency Identification (RFID) tag is designed with ultra low power. Full chip area of the tag is 0.38mm2@0.18µm CMOS process (without test pads) and consumes only 4.28µW. As a significant module of the tag, the proposed ASK demodulator is powered by on-chip power of only 0.95V and consumes ultra small average current of 0.23µA for typical 300mV RF input voltage. Meanwhile, the demodulator can also be powered from external pad, in which way the supply voltage can be as low as 0.5V with 0.21µA current consumption.


Enhancements to rss based indoor tracking systems using kalman filters


title: Collaborative spectrum sensing for opportunistic access in fading environments

abstract: Traditionally, frequency spectrum is licensed to users by government agencies in a fixed manner where licensee has exclusive right to access the allocated band. This policy has been de jure practice to protect systems from mutual interference for many years. However, with increasing demand for the spectrum and scarcity of vacant bands, a spectrum policy reform seems inevitable. Meanwhile, recent measurements suggest the possibility of sharing spectrum among different parties subject to interference-protection constraints. In this paper we study spectrum-sharing between a primary licensee and a group of secondary users. In order to enable access to unused licensed spectrum, a secondary user has to monitor licensed bands and opportunistically transmit whenever no primary signal is detected. However, detection is compromised when a user experiences shadowing or fading effects. In such cases, user cannot distinguish between an unused band and a deep fade. Collaborative spectrum sensing is proposed and studied in this paper as a means to combat such effects. Our analysis and simulation results suggest that collaboration may improve sensing performance significantly


title: Improving Wireless Physical Layer Security via Cooperating Relays

abstract: Physical (PHY) layer security approaches for wireless communications can prevent eavesdropping without upper layer data encryption. However, they are hampered by wireless channel conditions: absent feedback, they are typically feasible only when the source-destination channel is better than the source-eavesdropper channel. Node cooperation is a means to overcome this challenge and improve the performance of secure wireless communications. This paper addresses secure communications of one source-destination pair with the help of multiple cooperating relays in the presence of one or more eavesdroppers. Three cooperative schemes are considered: decode-and-forward (DF), amplify-and-forward (AF), and cooperative jamming (CJ). For these schemes, the relays transmit a weighted version of a reencoded noise-free message signal (for DF), a received noisy source signal (for AF), or a common jamming signal (for CJ). Novel system designs are proposed, consisting of the determination of relay weights and the allocation of transmit power, that maximize the achievable secrecy rate subject to a transmit power constraint, or, minimize the transmit power subject to a secrecy rate constraint. For DF in the presence of one eavesdropper, closed-form optimal solutions are derived for the relay weights. For other problems, since the optimal relay weights are difficult to obtain, several criteria are considered leading to suboptimal but simple solutions, i.e., the complete nulling of the message signals at all eavesdroppers (for DF and AF), or the complete nulling of jamming signal at the destination (for CJ). Based on the designed relay weights, for DF in the presence of multiple eavesdroppers, and for CJ in the presence of one eavesdropper, the optimal power allocation is obtained in closed-form; in all other cases the optimal power allocation is obtained via iterative algorithms. Numerical evaluation of the obtained secrecy rate and transmit power results show that the proposed design can significantly improve the performance of secure wireless communications.


title: 160-GHz Power Amplifier Design in Advanced SiGe HBT Technologies With ${P}_{\rm sat}$ in Excess of 10 dBm

abstract: Power amplifier (PA) design for 160-GHz applications in an advanced SiGe heterojunction bipolar transistor (HBT) technology with saturated output power (Psat) in excess of 10 dBm is presented. The architecture is based on a three-stage pseudodifferential configuration that was implemented in SiGe HBT evaluation technology with fmax of 400 GHz. At saturation, a PA breakout circuit delivers 10 dBm with 20-dB gain. From 150 to 170 GHz, the small-signal gain is within 20-32 dB and the output referred 1-dB compression point (P1 dB) is 8.5 dBm at 160 GHz. High output power was possible due to optimum device sizing, efficient layout, and accurate EM modeling. To our best knowledge, this is the highest output power reported for silicon PAs operating beyond 120 GHz.


title: Product concepts for land mobile satellite communication terminals in Ku-/Ka-band

abstract: The details of two product concepts and the hardware realization aspects for low profile Ku-/Ka-band land mobile terminals for high data rate satellite communications are described. Beside the competitive low cost approach, the non-obstructive design and lower weight are key issues for many applications to achieve a broad customer acceptance. The introduction of a proprietary planar antenna technology opens the possibility to meet these requirements.


title: Scalable Modified Kneser-Ney Language Model Estimation

abstract: We present an efficient algorithm to estimate large modified Kneser-Ney models including interpolation. Streaming and sorting enables the algorithm to scale to much larger models by using a fixed amount of RAM and variable amount of disk. Using one machine with 140 GB RAM for 2.8 days, we built an unpruned model on 126 billion tokens. Machine translation experiments with this model show improvement of 0.8 BLEU point over constrained systems for the 2013 Workshop on Machine Translation task in three language pairs. Our algorithm is also faster for small models: we estimated a model on 302 million tokens using 7.7% of the RAM and 14.0% of the wall time taken by SRILM. The code is open source as part of KenLM.


title: A New Design Method of an LCL Filter Applied in Active DC-Traction Substations

abstract: This paper concentrates on the LCL filter with damping resistance intended to connect the shunt active power filter of an active dc-traction substation to the point of common coupling with the transmission grid. In order to find design conditions and conceive a design algorithm, attention is directed to the transfer functions related to currents and the associated frequency response. The mathematical foundation of the design method is based on meeting the requirements related to the significant attenuation of the high-frequency switching current, concurrently with the unalterated flow of the current that needs to be compensated by active filtering. It is pointed out that there are practical limitations and a compromise must be made between the two requirements. To quantify the extent to which the harmonics to be compensated are influenced by imposing the magnitude response at both highest harmonic frequency to be compensated and switching frequency, a performance indicator is defined. As an additional design criterion, the damping power losses are taken into consideration. The validity and effectiveness of the proposed method are proved by simulation results and experimental tests on a laboratory test bench at small scale reproducing the specific conditions of a dc-traction substation with six-pulse diode rectifier.


SCIM III (Spinal Cord Independence Measure version III): reliability of assessment by interview and comparison with assessment by observation


title: Modeling Attention Flow on Graphs

abstract: Real-world scenarios demand reasoning about process, more than final outcome prediction, to discover latent causal chains and better understand complex systems. It requires the learning algorithms to offer both accurate predictions and clear interpretations. We design a set of trajectory reasoning tasks on graphs with only the source and the destination observed. We present the attention flow mechanism to explicitly model the reasoning process, leveraging the relational inductive biases by basing our models on graph networks. We study the way attention flow can effectively act on the underlying information flow implemented by message passing. Experiments demonstrate that the attention flow driven by and interacting with graph networks can provide higher accuracy in prediction and better interpretation for trajectories reasoning.


title: Political Preference Formation: Competition, Deliberation, and the (Ir)relevance of Framing Effects

abstract: A framing effect occurs when different, but logically equivalent, words or phrases – such as 95% employment or 5% unemployment – cause individuals to alter their preferences. Framing effects challenge the foundational assumptions of much of the social sciences (e.g., the existence of coherent preferences or stable attitudes), and raise serious normative questions about democratic responsiveness. Many scholars and pundits assume that framing effects are highly robust in political contexts. Using a new theory and an experiment with more than 550 participants, I show that this is not the case – framing effects do not occur in many political settings. Elite competition and citizens’ interpersonal conversations often vitiate and eliminate framing effects. However, I also find that when framing effects persist, they can be even more pernicious than often thought – not only do they suggest incoherent preferences but they also stimulate increased confidence in those preferences. My results have broad implications for preference formation, rational choice theory, political psychology, and experimental design.


title: Real-Time Performance of a Self-Powered Environmental IoT Sensor Network System

abstract: Wireless sensor networks (WSNs) play an increasingly important role in monitoring applications in many areas. With the emergence of the Internet-of-Things (IoT), many more lowpower sensors will need to be deployed in various environments to collect and monitor data about environmental factors in real time. Providing power supply to these sensor nodes becomes a critical challenge for realizations of IoT applications as sensor nodes are normally battery-powered and have a limited lifetime. This paper proposes a wireless sensor network that is powered by solar energy harvesting. The sensor network monitors the environmental data with low-power sensor electronics and forms a network using multiple XBee wireless modules. A detailed performance analysis of the network system under solar energy harvesting has been presented. The sensor network system and the proposed energy-harvesting techniques are configured to achieve a continuous energy source for the sensor network. The proposed energy-harvesting system has been successfully designed to enable an energy solution in order to keep sensor nodes active and reliable for a whole day. The paper also outlines some of our experiences in real-time implementation of a sensor network system with energy harvesting.


title: A Big Data Analysis Approach for Rail Failure Risk Assessment

abstract: Railway infrastructure monitoring is a vital task to ensure rail transportation safety. A rail failure could result in not only a considerable impact on train delays and maintenance costs, but also on safety of passengers. In this article, the aim is to assess the risk of a rail failure by analyzing a type of rail surface defect called squats that are detected automatically among the huge number of records from video cameras. We propose an image processing approach for automatic detection of squats, especially severe types that are prone to rail breaks. We measure the visual length of the squats and use them to model the failure risk. For the assessment of the rail failure risk, we estimate the probability of rail failure based on the growth of squats. Moreover, we perform severity and crack growth analyses to consider the impact of rail traffic loads on defects in three different growth scenarios. The failure risk estimations are provided for several samples of squats with different crack growth lengths on a busy rail track of the Dutch railway network. The results illustrate the practicality and efficiency of the proposed approach.


title: Acute myeloid leukaemia

abstract: Acute myeloid leukaemia (AML) is a disorder characterized by a clonal proliferation derived from primitive haematopoietic stem cells or progenitor cells. Abnormal differentiation of myeloid cells results in a high level of immature malignant cells and fewer differentiated red blood cells, platelets and white blood cells. The disease occurs at all ages, but predominantly occurs in older people (>60 years of age). AML typically presents with a rapid onset of symptoms that are attributable to bone marrow failure and may be fatal within weeks or months when left untreated. The genomic landscape of AML has been determined and genetic instability is infrequent with a relatively small number of driver mutations. Mutations in genes involved in epigenetic regulation are common and are early events in leukaemogenesis. The subclassification of AML has been dependent on the morphology and cytogenetics of blood and bone marrow cells, but specific mutational analysis is now being incorporated. Improvements in treatment in younger patients over the past 35 years has largely been due to dose escalation and better supportive care. Allogeneic haematopoietic stem cell transplantation may be used to consolidate remission in those patients who are deemed to be at high risk of relapse. A plethora of new agents - including those targeted at specific biochemical pathways and immunotherapeutic approaches - are now in trial based on improved understanding of disease pathophysiology. These advances provide good grounds for optimism, although mortality remains high especially in older patients.


title: The effect of chronic low back pain on size and contraction of the lumbar multifidus muscle.

abstract: Decreases in the size of the multifidus muscle have been consistently documented in people with low back pain. Recently, ultrasound imaging techniques have been used to measure contraction size of the multifidus muscle, via comparison of the thickness of the muscle at rest and on contraction. The aim of this study was to compare both the size (cross-sectional area, CSA) and the ability to voluntarily perform an isometric contraction of the multifidus muscle at four vertebral levels in 34 subjects with and without chronic low back pain (CLBP). Ultrasound imaging was used for assessments, conducted by independent examiners. Results showed a significantly smaller CSA of the multifidus muscle for the subjects in the CLBP group compared with subjects from the healthy group at the L5 vertebral level (F=29.1, p=0.001) and a significantly smaller percent thickness contraction for subjects of the CLBP group at the same vertebral level (F=6.6, p=0.02). This result was not present at other vertebral levels (p>0.05). The results of this study support previous findings that the pattern of multifidus muscle atrophy in CLBP patients is localized rather than generalized but also provided evidence of a corresponding reduced ability to voluntarily contract the atrophied muscle.


title: Perspectives on theory at the interface of physics and biology

abstract: Theoretical physics is the search for simple and universal mathematical descriptions of the natural world. In contrast, much of modern biology is an exploration of the complexity and diversity of life. For many, this contrast is prima facie evidence that theory, in the sense that physicists use the word, is impossible in a biological context. For others, this contrast serves to highlight a grand challenge. I'm an optimist, and believe (along with many colleagues) that the time is ripe for the emergence of a more unified theoretical physics of biological systems, building on successes in thinking about particular phenomena. In this essay I try to explain the reasons for my optimism, through a combination of historical and modern examples.


title: The Druze: A Population Genetic Refugium of the Near East

abstract: BACKGROUND ::: Phylogenetic mitochondrial DNA haplogroups are highly partitioned across global geographic regions. A unique exception is the X haplogroup, which has a widespread global distribution without major regions of distinct localization. ::: ::: ::: PRINCIPAL FINDINGS ::: We have examined mitochondrial DNA sequence variation together with Y-chromosome-based haplogroup structure among the Druze, a religious minority with a unique socio-demographic history residing in the Near East. We observed a striking overall pattern of heterogeneous parental origins, consistent with Druze oral tradition, together with both a high frequency and a high diversity of the mitochondrial DNA (mtDNA) X haplogroup within a confined regional subpopulation. Furthermore demographic modeling indicated low migration rates with nearby populations. ::: ::: ::: CONCLUSIONS ::: These findings were enabled through the use of a paternal kindred based sampling approach, and suggest that the Galilee Druze represent a population isolate, and that the combination of a high frequency and diversity of the mtDNA X haplogroup signifies a phylogenetic refugium, providing a sample snapshot of the genetic landscape of the Near East prior to the modern age.


Recent Advances in the Recombinant Biosynthesis of Polyphenols


title: Object Detection in Real-Time Systems: Going Beyond Precision

abstract: Applications like autonomous driving, industrial robotics, surveillance, and wearable assistive technology rely on object detectors as an integral part of the system. Thus, an increase in performance of object detectors directly affects the quality of such systems. In the recent years, convolutional neural networks (CNNs) and its variants emerged as the state of art in object detection, where performance is usually measured either in terms of mean average precision (mAP) or number of frames processed per second (fps). Many applications which use object detectors are resource constrained in practice. Even though it is clear from the published results, that a frame-level analysis of the system in terms of mAP or fps proves the superiority of one algorithm over the other, we observe that such metrics do not necessarily apply to real time applications with resource constraints. A slower algorithm even though highly accurate may need to drop frames to maintain the necessary frame rate and lose on the accuracy. We propose a closer look at the metrics used for performance in real-time applications, and suggest some new evaluation criterion. Our comparison of state of the art detectors on these metrics has also thrown some surprises in terms of conventional wisdom, which we present in this paper. Our framework is available at https://www.github.com/anupamsobti/object-detectionreal-time-systems.


title: Scheduling Spare Drones for Persistent Task Performance under Energy Constraints

abstract: This paper considers the problem of enabling persistent execution of a multi-drone task under energy limitations. The drones are given a set of locations and their task is to ensure that at least one drone will be present, for example for monitoring, over each location at any given time. Because of energy limitations, drones must be replaced from time to time, and fly back home where their batteries can be replaced. Our goals are to identify the minimum number of spare drones needed to accomplish the task while no drone battery drains, and to provide a drone replacement strategy. We present an efficient procedure for calculating whether one spare drone is enough for a given task and provide an optimal replacement strategy. If more than one drone is needed, we aim at finding the minimum number of spare drones required, and extend the replacement strategy to multiple spare drones by introducing a new Bin-Packing variant, named Bin Maximum Item Double Packing (BMIDP). Since the problem is presumably computationally hard, we provide a first fit greedy approximation algorithm for efficiently solving the BMIDP problem. For the offline version, in which all locations are known in advance, we prove an approximation factor upper bound of 1.5, and for the online version, in which locations are given one by one, we show via extensive simulations, that the approximation yields an average factor of 1.7.


title: Cranial magnetic resonance imaging and school performance in very low birth weight infants in adolescence

abstract: Aim—To determine whether neurological deficits are associated with structural anomalies of the brain in very low birthweight (VLBW) infants with subsequent learning disorders but without cerebral palsy, or whether other factors, such as poor early growth, are responsible. Methods—Eighty seven VLBW infants and eight term controls who had been examined at school between the ages of 12 and 13 years, had cranial magnetic resonance imaging (MRI) scans at 15‐17 years of age. Results—Thirty seven (42.5%) of the VLBW children had abnormalities reported on their scans (two porencephaly, 28 periventricular leucomalacia, 24 ventricular dilatation, and 15 thinning of the corpus callosum). No significant diVerences in intelligence quotient, motor clumsiness, or frequency of attention deficit / hyperactivity disorder were observed between those children with MRI lesions and those with normal scans. Quantitative measurements showed the VLBW infants had smaller brains, and a relatively smaller corpus callosum compared with controls. No association between brain measurements and school performance was observed among the VLBW infants. Conclusions—The diYculties experienced by VLBW children at school are unlikely to be the result of perinatal brain injury, but they might to be attributable to the eVects of poor postnatal growth. (Arch Dis Child Fetal Neonatal Ed 1999;81:F116‐F121)


title: Counterfactual Multi-Agent Policy Gradients

abstract: Cooperative multi-agent systems can be naturally used to model many real world problems, such as network packet routing and the coordination of autonomous vehicles. There is a great need for new reinforcement learning methods that can efficiently learn decentralised policies for such systems. To this end, we propose a new multi-agent actor-critic method called counterfactual multi-agent (COMA) policy gradients. COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies. In addition, to address the challenges of multi-agent credit assignment, it uses a counterfactual baseline that marginalises out a single agent's action, while keeping the other agents' actions fixed. COMA also uses a critic representation that allows the counterfactual baseline to be computed efficiently in a single forward pass. We evaluate COMA in the testbed of StarCraft unit micromanagement, using a decentralised variant with significant partial observability. COMA significantly improves average performance over other multi-agent actor-critic methods in this setting, and the best performing agents are competitive with state-of-the-art centralised controllers that get access to the full state.


title: Triple consistency loss for pairing distributions in GAN-based face synthesis

abstract: Generative Adversarial Networks have shown impressive results for the task of object translation, including face-to-face translation. A key component behind the success of recent approaches is the self-consistency loss, which encourages a network to recover the original input image when the output generated for a desired attribute is itself passed through the same network, but with the target attribute inverted. While the self-consistency loss yields photo-realistic results, it can be shown that the input and target domains, supposed to be close, differ substantially. This is empirically found by observing that a network recovers the input image even if attributes other than the inversion of the original goal are set as target. This stops one combining networks for different tasks, or using a network to do progressive forward passes. In this paper, we show empirical evidence of this effect, and propose a new loss to bridge the gap between the distributions of the input and target domains. This "triple consistency loss", aims to minimise the distance between the outputs generated by the network for different routes to the target, independent of any intermediate steps. To show this is effective, we incorporate the triple consistency loss into the training of a new landmark-guided face to face synthesis, where, contrary to previous works, the generated images can simultaneously undergo a large transformation in both expression and pose. To the best of our knowledge, we are the first to tackle the problem of mismatching distributions in self-domain synthesis, and to propose "in-the-wild" landmark-guided synthesis. Code will be available at this https URL


title: Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings

abstract: Machine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora. In this paper, we propose a new method for this task based on multilingual sentence embeddings. Our approach uses an encoder-decoder trained over an initial parallel corpus to build multilingual sentence representations, which are then incorporated into a new margin-based method to score, mine and filter parallel sentences. In contrast to previous approaches, which rely on nearest neighbor retrieval with a hard threshold over cosine similarity, our proposed method accounts for the scale inconsistencies of this measure, considering the margin between a given sentence pair and its closest candidates instead. Our experiments show large improvements over existing methods. We outperform the best published results on the BUCC shared task on parallel corpus mining by more than 10 F1 points. We also improve the precision from 48.9 to 83.3 on the reconstruction of 11.3M English-French sentence pairs of the UN corpus. Finally, filtering the English-German ParaCrawl corpus with our approach, we obtain 31.2 BLEU points on newstest2014, an improvement of more than one point over the best official filtered version.


title: New approaches for direct conversion of patient fibroblasts into neural cells

abstract: Recent landmark studies have demonstrated the production of disease-relevant human cell types by two different methods; differentiation of stem cells using external morphogens or lineage conversion using genetic factors. Directed differentiation changes embryonic stem cells (ESCs) or induced pluripotent stem cells (iPSCs) into a desired cell type by providing developmental cues in an in vitro environment. Direct reprogramming is achieved by the introduction of exogenous lineage specific transcription factors to convert any somatic cell type into another, thereby bypassing an intermediate pluripotent stage. A variety of somatic cell types such as blood, keratinocytes and fibroblasts can be used to derive iPSC cells. However, the process is time consuming,laborious, expensive and gives rise to cells with reported epigenetic heterogeneity even amongst different iPSC lines from same patient which could propagate phenotypic variability. A major concern with the use of pluripotent cells as starting material for cell replacement therapy is their incomplete differentiation and their propensity to form tumors following transplantation. In comparison, transcription factor mediated reprogramming offers a direct route to target cell types. This could allow for rapid comparison of large cohorts of patient and control samples at a given time for disease modeling. Additionally, transcription factors that drive maturation may yield more functionally mature cells than directed differentiation. Several studies have demonstrated the feasibility of generating of cell types such as cardiomyocytes, hepatocytes, and neurons from fibroblasts. Here, we will discuss recent advances and key challenges regarding direct reprogramming of somatic cell types into diverse neural cells. This article is part of a Special Issue entitled SI: Exploiting human neurons.


title: Monocular 3D shape reconstruction using deep neural networks

abstract: This paper presents a novel approach to reconstructing the 3D shape of an object from a single image. The approach combines deep neural networks with a silhouette-based 3D reconstruction process. The optimal 3D shape is sought efficiently inside an extremely low-dimensional latent shape space, and the viewpoint and the object shape are jointly optimized based on the result of image segmentation. Evaluation of this approach shows a nearly 20 percent performance gain in viewpoint estimation subsequent to the optimization.


Image Classification Using Naive Bayes Classifier With Pairwise Local Observations.


title: Overcoming implementation challenges in enterprise architecture management: a design theory for architecture-driven IT Management (ADRIMA)

abstract: Enterprise architecture management (EAM) is acknowledged as a discipline to drive organizational change, to improve IT landscapes' transparency, and to align business and IT. Despite its increasing popularity in practice, many EAM initiatives are confronted with substantial challenges, as demonstrated by the low usage level of enterprise architecture (EA) documentation and enterprise architects' lack of authority, and often fail. This motivates our research, which aims at developing a design theory that may guide organizations to successfully implement EAM. Based on three field studies, we first analyze the issues that arise when implementing EAM in practice. We find that EAM often suffers from being regarded as a separate and parallel initiative, although it needs to be embedded in established management processes and organization. We then suggest a design theory for architecture-driven IT management (ADRIMA) that synthesizes prescriptive knowledge related to embedding EAM practices, artifacts, and roles in the existing IT management processes and organization. By consolidating both IT management and EAM perspectives, our research goes beyond existing EA literature and EA frameworks which describe EAM as a stand-alone management concept focusing on EA models and the EA life cycle.


title: Bounds on Multiprocessing Timing Anomalies

abstract: An apparatus for generating sparks over a selected area to be used for theatrical effects. Metal wire having a diameter in the range of 0.020-0.125 inches is provided by two, independent supply sources. Each wire supply source is coupled to a wire guide which imposes synchronous, linear movement to each wire source at a selected rate. Each wire source is coupled to a tip assembly which places the terminus of each wire source adjacent one another. The positive and negative electrodes of a direct current power source are electrically connected to a respective terminus of each of the pair of wire sources, the output of the direct current power source is amplified to voltage sufficient to atomize the wire when the power source is short circuited. The atomization of the wire results in the production of heated, metallic particles simulating generated sparks. A source of compressed air is disposed adjacent the point of atomization. The atomized particles are disseminated across an area determined by the force imposed thereon by the compressed air.


A Note on the Expectation-Maximization (EM) Algorithm


title: Characterising the inventive appropriation of emoji as relationally meaningful in mediated close personal relationships

abstract: Emoji are two-dimensional pictographs that were originally designed to convey emotion between participants in text-based conversation. This paper draws on interview data to identify ways in which emoji have been appropriated in pursuit of relationally meaningful behaviours in contemporary messaging applications. We suggest that the presence of appropriable tools like emoji might influence the selection of a communication channel for particular types of mediated conversation.


title: Determination of polyfluoroalkyl phosphoric acid diesters, perfluoroalkyl phosphonic acids, perfluoroalkyl phosphinic acids, perfluoroalkyl carboxylic acids, and perfluoroalkane sulfonic acids in lake trout from the Great Lakes region

abstract: A comprehensive method to extract perfluoroalkyl carboxylic acids, perfluoroalkane sulfonic acids, perfluoroalkyl phosphonic acids, perfluoroalkyl phosphinic acids, and polyfluoroalkyl phosphoric acid diesters simultaneously from fish samples has been developed. The recoveries of target compounds ranged from 78 % to 121 %. The new method was used to analyze lake trout (Salvelinus namaycush) from the Great Lakes region. The results showed that the total perfluoroalkane sulfonate concentrations ranged from 0.1 to 145 ng/g (wet weight) with perfluorooctane sulfonate (PFOS) as the dominant contaminant. Concentrations in fish between lakes were in the order of Lakes Ontario ≈ Erie > Huron > Superior ≈ Nipigon. The total perfluoroalkyl carboxylic acid concentrations ranged from 0.2 to 18.2 ng/g wet weight. The aggregate mean perfluorooctanoic acid (PFOA) concentration in fish across all lakes was 0.045 ± 0.023 ng/g. Mean concentrations of PFOA were not significantly different (p > 0.1) among the five lakes. Perfluoroalkyl phosphinic acids were detected in lake trout from Lake Ontario, Lake Erie, and Lake Huron with concentration ranging from non-detect (ND) to 0.032 ng/g. Polyfluoroalkyl phosphoric acid diesters were detected only in lake trout from Lake Huron, at levels similar to perfluorooctanoic acid.


title: An Agent-based Market Platform for Smart Grids

abstract: The trend towards renewable, decentralized, and highly fluctuating energy suppliers (e.g. photovoltaic, wind power, CHP) introduces a tremendous burden on the stability of future power grids. By adding sophisticated ICT and intelligent devices, various Smart Grid initiatives work on concepts for intelligent power meters, peak load reductions, efficient balancing mechanisms, etc. As in the Smart Grid scenario data is inherently distributed over different, often non-cooperative parties, mechanisms for efficient coordination of the suppliers, consumers and intermediators is required in order to ensure global functioning of the power grid. In this paper, a highly flexible market platform is introduced for coordinating self-interested energy agents representing power suppliers, customers and prosumers. These energy agents implement a generic bidding strategy that can be governed by local policies. These policies declaratively represent user preferences or constraints of the devices controlled by the agent. Efficient coordination between the agents is realized through a market mechanism that incentivizes the agents to reveal their policies truthfully to the market. By knowing the agent's policies, an efficient solution for the overall system can be determined. As proof of concept implementation the market platform D'ACCORD is presented that supports various market structures ranging from a single local energy exchange to a hierarchical energy market structure (e.g. as proposed in [10]).


title: Human-friendly motion control of a wheeled inverted pendulum by reduced-order disturbance observer

abstract: This paper introduces a novel approach to control a wheeled inverted pendulum when a disturbance is applied by a human. The interaction between a human and a wheeled inverted pendulum involves a pulling or pushing force. This type of action is a severe disturbance for a wheeled inverted pendulum, as the wheeled inverted pendulum tends to maintain its initial position if there is no desired input. Thus, there are many possibilities for the wheeled inverted pendulum to be unstable as a result of interactions with humans. To solve this problem, the control algorithm of a wheeled inverted pendulum was designed to move in coordination with the external force of a human. This control algorithm is termed human-friendly motion control. It contains an optimal controller using a full-state feedback and a reduce-order disturbance observer. The disturbance torque from a human was estimated, and the estimated disturbance torque was used to generate a position reference for the human-friendly motion. This control algorithm keeps the wheeled inverted pendulum stable even when a severe disturbance is applied.


title: Calculating the similarity between words and sentences using a lexical database and corpus statistics

abstract: Calculating the semantic similarity between sentences is a long dealt problem in the area of natural language processing. The semantic analysis field has a crucial role to play in the research related to the text analytics. The semantic similarity differs as the domain of operation differs. In this paper, we present a methodology which deals with this issue by incorporating semantic similarity and corpus statistics. To calculate the semantic similarity between words and sentences, the proposed method follows an edge-based approach using a lexical database. The methodology can be applied in a variety of domains. The methodology has been tested on both benchmark standards and mean human similarity dataset. When tested on these two datasets, it gives highest correlation value for both word and sentence similarity outperforming other similar models. For word similarity, we obtained Pearson correlation coefficient of 0.8753 and for sentence similarity, the correlation obtained is 0.8794.


title: Gait recognition from corrupted silhouettes: a robust statistical approach

abstract: This paper introduces a method based on robust statistics to build reliable gait signatures from averaging silhouette descriptions, mainly when gait sequences are affected by severe and persistent defects. The term robust refers to the ability of reducing the impact of silhouette defects (outliers) on the average gait pattern, while taking advantage of clean silhouette regions. An extensive experimental framework was defined based on injecting three types of realistic defects (salt and pepper noise, static occlusion, and dynamic occlusion) to clean gait sequences, both separately in an easy setting and jointly in a hard setting. The robust approach was compared against two other operation modes: (1) simple mean (weak baseline) and (2) defect exclusion (strong benchmark). Three gait representation methods based on silhouette averaging were used: Gait Energy Image (GEI), Gradient Histogram Energy Image (GHEI), and the joint use of GEI and HOG descriptors. Quality of gait signatures was assessed by their discriminant power in a large number of gait recognition tasks. Nonparametric statistical tests were applied on recognition results, searching for significant differences between operation modes.


title: Exploring the credibility of online celebrities' Instagram profiles in influencing the purchase decisions of young female users

abstract: The growth of Instagram continues, with the majority of its users being young women. This study investigates the impact of Instagram upon source credibility, consumer buying intention and social identification with different types of celebrities. In-depth interviews were conducted with 18 female Instagram users aged 1830 to determine the extent to which Instagram influences their buying behaviour. The research findings show that celebrities on Instagram are influential in the purchase behaviour of young female users. However, non-traditional celebrities such as bloggers, YouTube personalities and Instafamous profiles are more powerful, as participants regard them as more credible and are able to relate to these, rather than more traditional, celebrities. Female users are perceptively aware and prefer to follow Instagram profiles that intentionally portray positive images and provide encouraging reviews. Online celebrity endorsements are perceived to be credible and trustworthy when communicating marketing messages.Personal experience of online celebrities enhances the credibility of their communication.Female online celebrities are most influential, as these categories are more credible and relevant to young women.No negative reviews regarding products are posted by users in order to maintain a positive online self-presentation.