title: How Does the Gender of Parents Matter

abstract: Claims that children need both a mother and father presume that women and men parent differently in ways crucial to development but generally rely on studies that conflate gender with other family structure variables. We analyze findings from studies with designs that mitigate these problems by comparing 2-parent families with same or different sex coparents and single-mother with single-father families. Strengths typically associated with married mother-father families appear to the same extent in families with 2 mothers and potentially in those with 2 fathers. Average differences favor women over men, but parenting skills are not dichotomous or exclusive. The gender of parents correlates in novel ways with parent-child relationships but has minor significance for children's psychological adjustment and social success.


title: Fast Aircraft Detection Using End-to-End Fully Convolutional Network

abstract: Aircraft detection from remote sensing images of complex background is a challenging task. Existing aircraft detection methods usually consist of two separated stages: proposal generation and window classification, which may be suboptimal for the aircraft detection task. To overcome this shortcoming, we propose a unified aircraft detection framework to simultaneously predict aircraft bounding boxes and class probabilities directly from an arbitrary-sized remote sensing image. Specifically, an end-to-end fully convolutional network (FCN) replaces the fully connected layers in traditional convolutional neural network (CNN). This can greatly reduce the model size while obtaining the comparable detection accuracy. To directly detect aircrafts under multiple scales and different aspect ratios, multiple referenced boxes are introduced. The whole framework can be optimized end-to-end by minimizing a multi-task loss. Extensive experiments on a common dataset demonstrate that the proposed method yields much lower false alarm rates at different recall rates than the state-of-the-art methods, and its speed is more than 35 times faster than the compared methods.


title: Software Design for Automated Assembly of Truss Structures

abstract: Concern over limited extravehicular and intravehicular activitiy time has increased the interest in performing in-space assembly and construction operations with automated robotic systems. A technique being considered at Langley Research Center is a supervised-autonomy approach, which can be monitored by an Earth-based supervisor that intervenes only when the automated system encounters a problem. A test-bed to support evaluation of the hardware and software requirements for supervised-autonomy assembly methods has been developed. This report describes the design of the software system necessary to support the assembly process. The system is implemented and successfully assembles and disassembles a planar tetrahedral truss structure. The software is hierarchical and supports both automated assembly operations and supervisor error-recovery procedures, including the capability to pause and reverse any operation. The software design serves as a model for the development of software for more sophisticated automated systems and as a test-bed for evaluation of new concepts and hardware components.


title: Newly graduated nurses' orientation experiences: A systematic review of qualitative studies.

abstract: OBJECTIVE ::: This systematic review describes the experiences of the orientation into nursing of newly graduated nurses. ::: ::: ::: DESIGN ::: Systematic review. ::: ::: ::: METHODS ::: Data was collected from five databases: Medic, EBSCO Cinahl, Scopus, PsycARTICLES and ERIC (ProQuest). Qualitative, peer reviewed, original studies published in English, Swedish or Finnish before February 2016 and exploring newly graduated nurses' experiences of the nursing orientation process were included. The studies were selected by screening titles, abstracts and full texts and the quality of the studies was assessed by two researchers independently. Data was analysed using content analysis. ::: ::: ::: RESULTS ::: Thirteen studies were chosen for the review. Newly graduated nurses' orientation experiences were divided into four main categories: experiences related to orientation arrangements; experiences related to the preceptor; experiencing role transition during the orientation and suggestions for changes based on orientation experiences. The findings establish that the orientation and the preceptor have a great impact on how newly graduated nurses experience the start of their career. Often, newly graduated nurses wish to continue their relationship with their preceptor after the orientation period. ::: ::: ::: CONCLUSIONS ::: Results bring together both the positive and negative aspects that newly graduated nurses relate as regards their current orientation programmes. Attention needs to be paid to the variation and imbalance in the quality of orientation programmes and preceptors. A more formal form of mentorship needs to be developed further.


title: Antidepressant effects of ketamine in depressed patients

abstract: Abstract Background: A growing body of preclinical research suggests that brain glutamate systems may be involved in the pathophysiology of major depression and the mechanism of action of antidepressants. This is the first placebo-controlled, double-blinded trial to assess the treatment effects of a single dose of an N -methyl-d-aspartate (NMDA) receptor antagonist in patients with depression. Methods: Seven subjects with major depression completed 2 test days that involved intravenous treatment with ketamine hydrochloride (.5 mg/kg) or saline solutions under randomized, double-blind conditions. Results: Subjects with depression evidenced significant improvement in depressive symptoms within 72 hours after ketamine but not placebo infusion (i.e., mean 25-item Hamilton Depression Rating Scale scores decreased by 14 ± SD 10 points vs. 0 ± 12 points, respectively during active and sham treatment). Conclusions: These results suggest a potential role for NMDA receptor-modulating drugs in the treatment of depression.


title: Nonce Generation For The Digital Signature Standard

abstract: Digital Signature Algorithm (DSA) is an underlying algorithm to form a signature in the Digital Signature Standard (DSS). DSA uses a new random number (or nonce) each time a signature is generated for a message. In this paper, we present a Linear Congruential Generator (LCG) based approach to generate nonce for DSS. LCG has been shown to be insecure for nonce generation. If two message-signature pairs are known along with the parameters of the LCG used to generate the nonce then the private key in the signature scheme can be found, with high probability, by solving three congruences over different moduli. We use a comparison of the output of two LCGs to generate the nonces and show that our approach is secure. We also show that coupled multiple recursive generators which are similar to LCGs are also safe for nonce generation. Congruences can no longer be set up to solve for the private key. The advantage of LCG based schemes for pseudo-random number generation is their efficiency.


title: Grayscale Image Matting And Colorization

abstract: This paper presents a novel approach to grayscale image matting and colorization. The first part of this approach is an efficient grayscale image matting algorithm in Bayesian framework. The foreground and background color distributions, and the alpha’s distribution are modelled with spatially varying sets of Gaussians. The major novelties of this matting algorithm are the introduction of alpha’s distribution and gradient into the Bayesian framework and an efficient optimization scheme. This grayscale image matting algorithm can effectively handle objects with intricate and vision sensitive boundaries, such as hair strands or facial organs. In the second part, by combining the grayscale image matting algorithm with color transferring techniques, an efficient colorization scheme is proposed, which provides great improvement over existing techniques for some difficult cases, such as human faces or images with confusing luminance distribution.


title: Fast Single Image Rain Removal via a Deep Decomposition-Composition Network

abstract: Abstract Rain effect in images typically is annoying for many multimedia and computer vision tasks. For removing rain effect from a single image, deep leaning techniques have been attracting considerable attentions. This paper designs a novel multi-task leaning architecture in an end-to-end manner to reduce the mapping range from input to output and boost the performance. Concretely, a decomposition net is built to split rain images into clean background and rain layers. Different from previous architectures, our model consists of, besides a component representing the desired clean image, an extra component for the rain layer. During the training phase, we further employ a composition structure to reproduce the input by the separated clean image and rain information for improving the quality of decomposition. Experimental results on both synthetic and real images are conducted to reveal the high-quality recovery by our design, and show its superiority over other state-of-the-art methods. Furthermore, our design is also applicable to other layer decomposition tasks like dust removal. More importantly, our method only requires about 50ms to process a testing image in VGA resolution on a GTX 1080 GPU with promising rain removal quality, making it attractive for practical use. The synthesized dataset and code are publicly available at https://sites.google.com/view/xjguo/rain .


title: A single-phase PLL algorithm with disturbances immunity

abstract: This paper proposes a new closed-loop synchronization algorithm, PLL (Phase-Locked Loop), for applications in power conditioner systems for single-phase networks. The structure presented is based on the correlation of the input signal with a complex signal generated from the use of an adaptive filter in a PLL algorithm in order to minimize the computational effort. Moreover, the adapted PLL presents a higher level of rejection for two particular disturbances: interharmonic and subharmonic, when compared to the original algorithm. Simulation and experimental results will be presented in order to prove the efficacy of the proposed adaptive algorithm.


title: 3D Shape Context and Distance Transform for action recognition

abstract: We propose the use of 3D (2D+time) shape context to recognize the spatial and temporal details inherent in human actions. We represent an action in a video sequence by a 3D point cloud extracted by sampling 2D silhouettes over time. A non-uniform sampling method is introduced that gives preference to fast moving body parts using a Euclidean 3D distance transform. Actions are then classified by matching the extracted point clouds. Our proposed approach is based on a global matching and does not require specific training to learn the model. We test the approach thoroughly on two publicly available datasets and compare to several state-of-the-art methods. The achieved classification accuracy is on par with or superior to the best results reported to date.


title: What-if Simulation Modeling in Business Intelligence

abstract: Optimizing decisions has become a vital factor for companies. In order to be able to evaluate beforehand the impact of a decision, managers need reliable provisional systems. Though data warehouses enable analysis of past data, they are not capable of giving anticipations of future trends. What-if analysis fills this gap by enabling users to simulate and inspect the behavior of a complex system under some given hypotheses. A crucial issue in the design of what-if applications is to find an adequate formalism to conceptually express the underlying simulation model. In this paper the authors report on how, within the framework of a comprehensive design methodology, this can be accomplished by extending UML 2 with a set of stereotypes. Their proposal is centered on the use of activity diagrams enriched with object flows, aimed at expressing functional, dynamic, and static aspects in an integrated fashion. The paper is completed by examples taken from a real case study in the commercial area.


title: Performance Analysis of Classification Algorithms on Medical Diagnoses-a Survey

abstract: The aim of this research paper is to study and discuss the various classification algorithms applied on different kinds of medical datasets and compares its performance. The classification algorithms with maximum accuracies on various kinds of medical datasets are taken for performance analysis. The result of the performance analysis shows the most frequently used algorithms on particular medical dataset and best classification algorithm to analyse the specific disease. This study gives the details of different classification algorithms and feature selection methodologies. The study also discusses about the data constraints such as volume and dimensionality problems. This research paper also discusses the new features of C5.0 classification algorithm over C4.5 and performance of classification algorithm on high dimensional datasets. This research paper summarizes various reviews and technical articles which focus on the current research on Medical diagnosis.


title: Desalination by ammonia-carbon dioxide forward osmosis : Influence of draw and feed solution concentrations on process performance

abstract: Abstract Forward (direct) osmosis (FO) using semi-permeable polymeric membranes may be a viable alternative to reverse osmosis as a lower cost and more environmentally friendly desalination technology. The driving force in the described FO process is provided by a draw solution comprising highly soluble gases—ammonia and carbon dioxide. Using a commercially available FO membrane, experiments conducted in a crossflow, flat-sheet membrane filtration cell yielded water fluxes ranging from 1 to 10 μm/s (2.1 to 21.2 gal ft −2 d −1 or 3.6 to 36.0 l m −2 h −1 ) for a wide range of draw and feed solution concentrations. It was found, however, that the experimental water fluxes were far lower than those anticipated based on available bulk osmotic pressure difference and membrane pure water permeability data. Internal concentration polarization was determined to be the major cause for the lower than expected water flux by analysis of the available water flux data and SEM images of the membrane displaying a porous support layer. Draw solution concentration was found to play a key role in this phenomenon. Sodium chloride rejection was determined to be 95–99% for most tests, with higher rejections occurring under higher water flux conditions. Desalination of very high sodium chloride feed solutions (simulating 75% recovery of seawater) was also deemed possible, leading to the possibility of brine discharge minimization.


Early osseointegration driven by the surface chemistry and wettability of dental implants


title: Gesture Recognition for Indonesian Sign Language Systems (ISLS) Using Multimodal Sensor Leap Motion and Myo Armband Controllers Based-on Naïve Bayes Classifier

abstract: Indonesian Sign Language System (ISLS) has been used widely by Indonesian for translating the sign language of disabled people to many applications, including education or entertainment. ISLS consists of static and dynamic gestures in representing words or sentences. However, individual variations in performing sign language have been a big challenge especially for developing automatic translation. The accuracy of recognizing the signs will decrease linearly with the increase of variations of gestures. This research is targeted to solve these issues by implementing the multimodal methods: leap motion and Myo armband controllers (EMG electrodes). By combining these two data and implementing Naive Bayes classifier, we hypothesized that the accuracy of gesture recognition system for ISLS then can be increased significantly. The data streams captured from hand-poses were based on time-domain series method which will warrant the generated data synchronized accurately. The selected features for leap motion data would be based on fingers positions, angles, and elevations, while for the Myo armband would be based on electrical signal generated by eight channels of EMG electrodes relevant to the activities of linked finger’s and forearm muscles. This study will investigate the accuracy of gesture recognition by using either single modal or multimodal for translating Indonesian sign language. For multimodal strategy, both features datasets were merged into a single dataset which was then used for generating a model for each hand gesture. The result showed that there was a significant improvement on its accuracy, from 91% for single modal using leap motion to 98% for multi-modal (combined with Myo armband). The confusion matrix of multimodal method also showed better performance than the single-modality. Finally, we concluded that the implementation of multi-modal controllers for ISLS’s gesture recognition showed better accuracy and performance compared of single modality of using only leap motion controller.


Symptomatic Adults Colorectal Neoplasms in Asymptomatic and Prevalence of Nonpolypoid ( Flat and Depressed )


title: The PREP pipeline: standardized preprocessing for large-scale EEG analysis

abstract: The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode/.


title: Dielectric properties estimation of normal and malignant skin tissues at millimeter-wave frequencies using effective medium theory

abstract: Millimeter-wave reflectometry can be used as a non-invasive method for early detection of skin cancers. Less penetration and higher resolution are only some reasons to use mm-waves for skin cancer diagnosis. The dielectric parameters data of human skin at mm-wave frequencies are not widely addressed in the literature due to the expensive instruments and the complicated structure of human skin. Available dielectric properties of cancerous tissues in the literature are for other tissues rather than skin and are restricted to the frequencies below 20 GHz. In this paper, dielectric properties of healthy and malignant skin tissues were estimated in the range of 20–100 GHz by applying the effective medium theory. A stratified skin model and some skin tumors were presented. The possibility of mm-wave reflectometry for detection of skin cancers has also been studied. The results of this paper can be used as a theoretical basis for taking further steps in skin cancer research.


title: Road boundary detection for run-off road prevention based on the fusion of video and radar

abstract: An approach for detecting the road boundary on different types of roads without any preliminary knowledge is presented. We fuse information obtained from an algorithm which detects road markings and road edges in images acquired by a video camera as well as data from a radar sensor. Each road marking, each road edge and each road barrier is tracked individually. Hence we can even capture exits or laybys. We use an edge image for road marking detection and texture information for road edge detection. Additional data provided by a radar sensor is used to measure targets referring to static barriers along the road side such as guardrails. The output of each processing unit is fused into a Kalman filter framework, where the confidence of each subsystem influences the innovation of the overall system. The underlying geometric road model comprises parameters for multiple lanes, the flanking road edge as well as the vehicle's relative pose. The work is part of the project Interactive.


title: What Do Stroke Patients Look for in Game-Based Rehabilitation: A Survey Study

abstract: Stroke is one of the most common causes of physical disability, and early, intensive, and repetitive rehabilitation exercises are crucial to the recovery of stroke survivors. Unfortunately, research shows that only one third of stroke patients actually perform recommended exercises at home, because of the repetitive and mundane nature of conventional rehabilitation exercises. Thus, to motivate stroke survivors to engage in monotonous rehabilitation is a significant issue in the therapy process. Game-based rehabilitation systems have the potential to encourage patients continuing rehabilitation exercises at home. However, these systems are still rarely adopted at patients' places. Discovering and eliminating the obstacles in promoting game-based rehabilitation at home is therefore essential. For this purpose, we conducted a study to collect and analyze the opinions and expectations of stroke patients and clinical therapists. The study is composed of 2 parts: Rehab-preference survey - interviews to both patients and therapists to understand the current practices, challenges, and expectations on game-based rehabilitation systems; and Rehab-compatibility survey - a gaming experiment with therapists to elaborate what commercial games are compatible with rehabilitation. The study is conducted with 30 outpatients with stroke and 19 occupational therapists from 2 rehabilitation centers in Taiwan. Our surveys show that game-based rehabilitation systems can turn the rehabilitation exercises more appealing and provide personalized motivation for various stroke patients. Patients prefer to perform rehabilitation exercises with more diverse and fun games, and need cost-effective rehabilitation systems, which are often built on commodity hardware. Our study also sheds light on incorporating the existing design-for-fun games into rehabilitation system. We envision the results are helpful in developing a platform which enables rehab-compatible (i.e., existing, appropriately selected) games to be operated on commodity hardware and brings cost-effective rehabilitation systems to more and more patients' home for long-term recovery.


Sequence-to-Dependency Neural Machine Translation


Bilateral, Misalignment-Compensating, Full-DOF Hip Exoskeleton: Design and Kinematic Validation


title: An electronic differential system using fuzzy logic speed controlled in-wheel brushless DC motors

abstract: Environmental issues and running out of fossil fuels has increased the interest in electric vehicles (EV). In this paper, an EV with independent rear two-wheel drive systems is presented. Two brushless DC motors (BLDCMs) with fuzzy logic speed controller (FLSC) were used in rear wheels. Ackermann-Jeantaud steering model was employed for electronic differential (ED) system. The proposed method was simulated using Matlab/Simulink. Simulations were carried out on straight and curved roads. Simulation results show that proposed ED system operates satisfactorily.


title: Trust Enhanced Cryptographic Role-Based Access Control for Secure Cloud Data Storage

abstract: Cloud data storage has provided significant benefits by allowing users to store massive amount of data on demand in a cost-effective manner. To protect the privacy of data stored in the cloud, cryptographic role-based access control (RBAC) schemes have been developed to ensure that the data can only be accessed by those who are allowed by access policies. However, these cryptographic approaches do not address the issues of trust. In this paper, we propose trust models to reason about and to improve the security for stored data in cloud storage systems that use cryptographic RBAC schemes. The trust models provide an approach for the owners and roles to determine the trustworthiness of individual roles and users, respectively, in the RBAC system. The proposed trust models consider role inheritance and hierarchy in the evaluation of trustworthiness of roles. We present a design of a trust-based cloud storage system, which shows how the trust models can be integrated into a system that uses cryptographic RBAC schemes. We have also considered practical application scenarios and illustrated how the trust evaluations can be used to reduce the risks and to enhance the quality of decision making by data owners and roles of cloud storage service.


title: AirSeal system insufflator to maintain a stable pneumorectum during TAMIS

abstract: Transanal minimally invasive surgery (TAMIS) is typically used for treating intraluminal rectal tumors. Other applications have recently been described. We here present the use of TAMIS as a tool to treat a chronic anastomotic fistula after restorative rectal resection. A new insufflation device expected to solve the problem of maintaining a stable pneumorectum is described.


title: Drowsy Driver Detection Through Facial Movement Analysis

abstract: The advance of computing technology has provided the means for building intelligent vehicle systems. Drowsy driver detection system is one of the potential applications of intelligent vehicle systems. Previous approaches to drowsiness detection primarily make preassumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers for 30 facial actions from the Facial Action Coding system were developed using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. In addition, head motion was collected through automatic eye tracking and an accelerometer. These measures were passed to learning-based classifiers such as Adaboost and multinomial ridge regression. The system was able to predict sleep and crash episodes during a driving computer game with 96% accuracy within subjects and above 90% accuracy across subjects. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis revealed new information about human behavior during drowsy driving.


title: Left Ventricular Hypertrophy : Pathogenesis , Detection , and Prognosis

abstract: When the heart faces a hemodynamic burden, it can do the following to compensate: (1) use the Frank-Starling mechanism to increase crossbridge formation; (2) augment muscle mass to bear the extra load; and (3) recruit neurohormonal mechanisms to increase contractility. The first mechanism is limited in its scope, and the third is deleterious as a chronic adjustment. Thus, increasing mass assumes a key role in the compensation for hemodynamic overload. This increase in mass is due to the hypertrophy of existing myocytes rather than hyperplasia, because cardiomyocytes become terminally differentiated soon after birth. In response to pressure overload in conditions such as aortic stenosis or hypertension, the parallel addition of sarcomeres causes an increase in myocyte width, which in turn increases wall thickness. This remodeling results in concentric hypertrophy (increase in ratio of wall thickness/chamber dimension). ::: ::: According to LaPlace’s Law, the load on any region of the myocardium is given as follows: (pressure×radius)/(2×wall thickness); thus, an increase in pressure can be offset by an increase in wall thickness. Because systolic stress (afterload) is a major determinant of ejection performance, the normalization of systolic stress helps maintain a normal ejection fraction even when needing to generate high levels of systolic pressure.1 Volume overload in conditions such as chronic aortic regurgitation, mitral regurgitation, or anemia engenders myocyte lengthening by sarcomere replication in series and an increase in ventricular volume. This pattern of eccentric hypertrophy (cavity dilatation with a decrease in ratio of wall thickness/chamber dimension) is also initially compensatory, such that the heart can meet the demand to sustain a high stroke volume. However, chronic hypertrophy may be deleterious because it increases the risk for the development of heart failure and premature death. ::: ::: This review will focus on the pathogenesis of pressure- versus volume-overload types of left ventricular hypertrophy (LVH), detection, …


title: Breaking the Softmax Bottleneck: A High-Rank RNN Language Model

abstract: We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natural language is highly context-dependent, this further implies that in practice Softmax with distributed word embeddings does not have enough capacity to model natural language. We propose a simple and effective method to address this issue, and improve the state-of-the-art perplexities on Penn Treebank and WikiText-2 to 47.69 and 40.68 respectively.


title: DRCD: a Chinese Machine Reading Comprehension Dataset

abstract: In this paper, we introduce DRCD (Delta Reading Comprehension Dataset), an open domain traditional Chinese machine reading comprehension (MRC) dataset. This dataset aimed to be a standard Chinese machine reading comprehension dataset, which can be a source dataset in transfer learning. The dataset contains 10,014 paragraphs from 2,108 Wikipedia articles and 30,000+ questions generated by annotators. We build a baseline model that achieves an F1 score of 53.78%. F1 score of Human performance is 93.30%.


title: Digging Into Self-Supervised Monocular Depth Estimation

abstract: Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this limitation, self-supervised learning has emerged as a promising alternative for training models to perform monocular depth estimation. In this paper, we propose a set of improvements, which together result in both quantitatively and qualitatively improved depth maps compared to competing self-supervised methods. Research on self-supervised monocular training usually explores increasingly complex architectures, loss functions, and image formation models, all of which have recently helped to close the gap with fully-supervised methods. We show that a surprisingly simple model, and associated design choices, lead to superior predictions. In particular, we propose (i) a minimum reprojection loss, designed to robustly handle occlusions, (ii) a full-resolution multi-scale sampling method that reduces visual artifacts, and (iii) an auto-masking loss to ignore training pixels that violate camera motion assumptions. We demonstrate the effectiveness of each component in isolation, and show high quality, state-of-the-art results on the KITTI benchmark.


title: Feature Selective Small Object Detection via Knowledge-based Recurrent Attentive Neural Network

abstract: At present, the performance of deep neural network in general object detection is comparable to or even surpasses that of human beings. However, due to the limitations of deep learning itself, the small proportion of feature pixels, and the occurence of blur and occlusion, the detection of small objects in complex scenes is still an open question. But we can not deny that real-time and accurate object detection is fundamental to automatic perception and subsequent perception-based decision-making and planning tasks of autonomous driving. Considering the characteristics of small objects in autonomous driving scene, we proposed a novel method named KB-RANN, which based on domain knowledge, intuitive experience and feature attentive selection. It can focus on particular parts of image features, and then it tries to stress the importance of these features and strengthenes the learning parameters of them. Our comparative experiments on KITTI and COCO datasets show that our proposed method can achieve considerable results both in speed and accuracy, and can improve the effect of small object detection through self-selection of important features and continuous enhancement of proposed method, and deployed it in our self-developed autonomous driving car.


title: Analysis and design for RCD clamped snubber used in output rectifier of phase-shift full-bridge ZVS converters

abstract: Detailed analysis and parameter design for a resistor-capacitor-diode (RCD) clamped snubber used in the output rectifier of phase-shift full-bridge zero-voltage-switching (PS-FB-ZVS) power converters are presented. Design equations and some properties of the clamped circuit are also highlighted.


Tourism Product Innovation: Positioning Meetings, Incentives, Conferences And Exhibitions, As A Strategy For Product Diversification In Beach Hotels of Mombasa County, Kenya


title: Innovation diffusion and new product growth models: A critical review and research directions

abstract: Diffusion processes of new products and services have become increasingly complex and multifaceted in recent years. Consumers today are exposed to a wide range of influences that include word-of-mouth communications, network externalities, and social signals. Diffusion modeling, the research field in marketing that seeks to understand the spread of innovations throughout their life cycle, has adapted to describe and model these influences.


title: Tracking port scanners on the IP backbone

abstract: Port scanning is the usual precursor to malicious attacks on today's Internet. Although many algorithms have been proposed for different aspects of the scan detection problem, their focused designed space is enterprise gateway level Intrusion Detection. Furthermore, we find few studies that track scanner behaviors over an extended period of time. Operating from a unique vantage point, the IP backbone, we put all the pieces together in designing and implementing a fast and accurate online port scan detection and tracking system. We introduce our flexible architecture, discuss trade-offs and design choices. Specifically, we go in depth to two design choices: the distinct counter data structure and the buffer size tuning. Our choice of a probabilistic counter is proved to have a low average error of 3.5%. The buffer size is derived using Lyapunov drift techniques on an equivalent queuing model. Our initial scanner tracking results show that the scanners' timing behavior follows a 90-10 curve. That is, 90% of scanners are active for a short time, with low scanning rates, while 10% are long term and fast scanners, with a few super-scanners lasting the entire duration of monitoring.


title: Brains or Beauty: How to Engender Trust in User-Agent Interactions

abstract: Software-based agents are becoming increasingly ubiquitous and automated. However, current technology and algorithms are still fallible, which considerably affects users’ trust and interaction with such agents. In this article, we investigate two factors that can engender user trust in agents: reliability and attractiveness of agents. We show that agent reliability is not more important than agent attractiveness. Subjective user ratings of agent trust and perceived accuracy suggest that attractiveness may be even more important than reliability.


title: Test format and corrective feedback modify the effect of testing on long-term retention

abstract: We investigated the effects of format of an initial test and whether or not students received corrective feedback on that test on a final test of retention 3 days later. In Experiment 1, subjects studied four short journal papers. Immediately after reading each paper, they received either a multiple choice (MC) test, a short answer (SA) test, a list of statements to read, or a filler task. The MC test, SA test, and list of statements tapped identical facts from the studied material. No feedback was provided during the initial tests. On a final test 3 days later (consisting of MC and SA questions), having had an intervening MC test led to better performance than an intervening SA test, but the intervening MC condition did not differ significantly from the read statements condition. To better equate exposure to test-relevant information, corrective feedback during the initial tests was introduced in Experiment 2. With feedback provided, having had an intervening SA test led to the best performance on the fi...


title: Improving the Accuracy of Job Search with Semantic Techniques

abstract: In this paper we introduce a prototype job portal which uses semantically annotated job offers and applicants. In our opinion, using Semantic Web technologies substantially increase market transparency, lower transaction costs and speed up the procurement process. However adding semantics is not a panacea for everything. We identify some outstanding problems in job search using the system and outline how the technique of query approximation can be the basis for a solution. Through an Industry-Research co-operation we are extending the prototype with these semantic techniques to demonstrate a more accurate job search.


title: Cardiac natriuretic peptides after myocardial infarction: relationship with infarct size, left ventricular function and remodelling assessed by 99mTc-sestamibi gated-single photon emission tomography

abstract: BACKGROUND ::: The aim of the study was to investigate the potential relationship between A-type (atrial) and B-type (brain) natriuretic peptides (ANP, BNP) and infarct size (IS), left ventricular (LV) function and remodelling as assessed by 99m technetium-hexakis-2-methoxy-isobutyl-isonitrile (99mTc-sestamibi) gated-single photon emission tomography (G-SPET). ::: ::: ::: METHODS ::: Plasma concentrations of ANP and BNP in peripheral blood were measured in 54 patients with coronary artery disease (CAD) and previous myocardial infarction (MI). Of these, 25 subjects had a recent (<2 weeks) and 29 subjects an old (>6 months) MI. IS, left ventricular ejection fraction (LVEF) and end diastolic (EDV), end systolic (ESV) and stroke volume (SV) were quantitatively calculated from at-rest G-SPET. ::: ::: ::: RESULTS ::: In either univariate or multivariate regression analysis that included IS and the other G-SPET derived parameters as co-variables, both BNP and ANP showed a significant association with IS (BNP p<0.002; ANP p<0.01). No significant relationship was found between the two peptides and LVEF, EDV or ESV values. BNP, but not ANP concentrations, were significantly higher in patients with antero-septal vs. infero-lateral (p=0.01) and recent vs. old MI (p=0.003). In these two subgroups, univariate and multivariate analyses confirmed the correlation between BNP and IS, whereas ANP demonstrated a relationship with IS only in subjects with recent MI. CAD extent had no influence on BNP and ANP levels. ::: ::: ::: CONCLUSIONS ::: The present study showed a positive correlation between BNP and the perfusion defect measured by 99mTc-sestamibi G-SPET in patients with previous MI. Consequently, BNP may reflect the functional significance of myocardial damage and may be considered of prognostic value. ANP was also related to scintigraphic defects in the early phase after MI, but not in the chronic phase, confirming that ANP is a less sensitive marker of LV remodelling, depending also on atrial load conditions and dilatation. These preliminary data based on a small group of subjects need to be confirmed by prospective longitudinal studies involving larger populations.


title: IPA - An intelligent personal assistant agent for task performance support

abstract: Assisting users in performing their tasks is an important issue in Human Computer Interaction research. A solution to deal with this challenge is to build a personal assistant agent capable to discover the user's habits, abilities, preferences, and goals, ever more accurately anticipating the user's intentions. In order to solve in an intelligent manner this problem, the assistant agent has to continuously improve its behavior based on previous experiences. By endowing the agent with the learning capability, it will become able to adapt himself to the user's behavior. This paper proposes an intelligent personal assistant agent that learns by supervision to assist users in performing specific tasks. For evaluating the performance of the agent a case study is considered, and a neural network is used by the agent to learn by supervision from its experience. We also provide a comparison of our approach with other similar existing work.


title: Expression of interleukin-16 by microglial cells in inflammatory, autoimmune, and degenerative lesions of the rat brain

abstract: Here we report a comparative analysis of interleukin-16 (IL-16) expression by microglial cells of the normal rat brain in trimethyltin (TMT) neurotoxicity, experimental autoimmune uveitis (EAU), encephalomyelitis (EAE), and viral infection (Borna disease, Borna disease virus) by immunohistochemistry. Striking differences were observed. In contrast to the human brain, IL-16 was not expressed constitutively in the rat brain. Remote activation of microglial cells of the optic tract in EAU did not result into IL-16 expression. TMT intoxication induced expression in microglial cells of the hippocampus. In EAE and BDV, massive IL-16(+) microglial cells could be seen. Thus, IL-16 is a descriptor of microglial cell activation that discriminates between different disease models, and might be a valuable marker for the detection of microglia activation in human and rat central nervous system (CNS) diseases.


title: Hindi-english cross-lingual question-answering system

abstract: We developed a cross-lingual, question-answering (CLQA) system for Hindi and English. It accepts questions in English, finds candidate answers in Hindi newspapers, and translates the answer candidates into English along with the context surrounding each answer. The system was developed as part of the surprise language exercise (SLE) within the TIDES program.


title: A meta-analysis of the Dark Triad and work behavior: a social exchange perspective.

abstract: We reviewed studies of the Dark Triad (DT) personality traits—Machiavellianism, narcissism, and psychopathy—and meta-analytically examined their implications for job performance and counterproductive work behavior (CWB). Relations among the DT traits and behaviors were extracted from original reports published between 1951 and 2011 of 245 independent samples (N 43,907). We found that reductions in the quality of job performance were consistently associated with increases in Machiavellianism and psychopathy and that CWB was associated with increases in all 3 components of the DT, but that these associations were moderated by such contextual factors as authority and culture. Multivariate analyses demonstrated that the DT explains moderate amounts of the variance in counterproductivity, but not job performance. The results showed that the 3 traits are positively related to one another but are sufficiently distinctive to warrant theoretical and empirical partitioning.


title: ETL as a Necessity for Business Architectures

abstract: Today, the new trend that companies are following is to digitalize all data they have in order to reduce costs with physical space and better handle data volume. The new era, the era of bundling data sources and huge volumes of data continues this decade also. In all industries, companies are starting to understand and appreciate the value of data, the value that these volumes of data can produce. Companies are collecting huge volumes of data but not always the actual solution for business intelligence (BI) can handle these volumes. To obtain information, those volumes of data are analyzed with extract-transform-load (ETL) software solutions. Companies, in the current economic context, are finding hard to invest in improvements of BI process including in ETL process. In this paper I will demonstrate why this kind of investment is necessary and also I will demonstrate that ETL process must be included in BI and big data architectures. In the following pages I will refer to business architectures as BI and big data architectures.


title: Temperature rise in an anatomical human head model due to 2.45 and 3.7 GHz inverted-F antennas

abstract: Temperature rise induced by 2.45 and 3.7 GHz inverted-F antennas were studied in an anatomical human head model. These antennas were integrated in a 100 mm by 40 mm by 10 mm metal box. Using an FDTD simulation tool different orientations and separation distances of 2.45 and 3.7 GHz handset antennas were investigated for maximum temperature rise inside the head model. Temperature rise profile with respect to time was studied as well.


title: ISS: The Interactive Smart home Simulator

abstract: Digital homes provide the best services to home's occupants by using modern devices, home appliances that integrate transparently and simplify usability in the home. However, the realization in the real environment is difficult and takes a long time from building the scratch. Thus, to support the implementation in the real smart home, it is necessary to demonstrate that thing can be done in the simulator which deals with virtual appliances and devices models the real smart home environment. In this paper, we propose ISS, an Interactive Smart home Simulator system focusing on controlling and simulating the behavior of an intelligent house. The developed system aims to provide architects, designers a simulation and useful tool for understanding the interaction between environment, people and the impact of embedded and pervasive technology on in daily life. In this research, the smart house is considered as an environment made up of independent and distributed devices interacting to support user's goals and tasks. Therefore, by using ISS, the developer can realize the relationship among virtual home space, surrounded environment, users and home appliances.


title: Design and implementation of hybrid encryption for security of IOT data

abstract: The world is moving towards an interconnected, inter-reliant and ever-growing technology era. Internet of Things (IOT) plays vital role in this aspect, since everything is convolving to cloud based storage and distant point access. Integrity and confidentiality of the information is the next big challenge for IOT. Information generated by the edge device, regardless of the methodology of obtaining, lacks the security in transmitting it to the cloud. In this paper, a hybrid and secure algorithm is proposed for the transmission and storing of the data in the cloud. The proposed algorithm enables the edge device to encrypt the data generated using Advanced Encryption Standard(AES) before transmitting to cloud. The key of the AES is encrypted by using RSA crypto system. The RSA encrypted key is exchanged with the authorized person via electronic mail. The encryption at the edge device is carried out on Field Programmable Gate Array(FPGA) in order make the system independent stand-alone cryptosystem. We implemented the proposed design on Xilinx SPARTAN-6, and verified the result using the synthesis tool of Xilinx ISE-Design 14.5 software. The RSA encryption and decryption is processed on MATLAB by the sender and the receiver respectively.


title: Moving camera video stabilization using homography consistency

abstract: Videos recorded on moving cameras are often known to be shaky due to unstable carrier motion and the video stabilization problem involves inferring the intended smooth motion to keep and the unintended shaky motion to remove. However, conventional methods typically require proper, scenario-specific parameter setting, which does not generalize well across different scenarios. Moreover, we observe that a stable video should satisfy two conditions: a smooth trajectory and consistent inter-frame transition. While conventional methods only target at the former condition, we address these two issues at the same time. In this paper, we propose a homography consistency based algorithm to directly extract the optimal smooth trajectory and evenly distribute the inter-frame transition. By optimizing in the homography domain, our method does not need further matrix decomposition and parameter adjustment, automatically adapting to all possible types of motion (eg. translational or rotational) and video properties (eg. frame rates). We test our algorithm on translational videos recorded from a car and rotational videos from a hovering aerial vehicle, both of high and low frame rates. Results show our method widely applicable to different scenarios without any need of additional parameter adjustment.


title: Universal Custom Complex Magnetic Spring Design Methodology

abstract: A design methodology is presented for creating custom complex magnetic springs through the design of force-displacement curves. This methodology results in a magnet configuration, which will produce a desired force-displacement relationship. Initially, the problem is formulated and solved as a system of linear equations. Then, given the limited likelihood of a single solution being feasibly manufactured, key parameters of the solution are extracted and varied to create a family of solutions. Finally, these solutions are refined using numerical optimization. Given the properties of magnets, this methodology can create any well-defined function of force versus displacement and is model-independent. To demonstrate this flexibility, a number of example magnetic springs are designed; one of which, designed for use in a jumping–gliding robot’s shape memory alloy actuated clutch, is manufactured and experimentally characterized. Due to the scaling of magnetic forces, the displacement region which these magnetic springs are most applicable is that of millimeters and below. However, this region is well situated for miniature robots and smart material actuators, where a tailored magnetic spring, designed to compliment a component, can enhance its performance while adding new functionality. The methodology is also expendable to variable interactions and multi-dimensional magnetic field design.


title: SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels

abstract: Existing scene understanding datasets contain only a limited set of views of a place, and they lack representations of complete 3D spaces. In this paper, we introduce SUN3D, a large-scale RGB-D video database with camera pose and object labels, capturing the full 3D extent of many places. The tasks that go into constructing such a dataset are difficult in isolation -- hand-labeling videos is painstaking, and structure from motion (SfM) is unreliable for large spaces. But if we combine them together, we make the dataset construction task much easier. First, we introduce an intuitive labeling tool that uses a partial reconstruction to propagate labels from one frame to another. Then we use the object labels to fix errors in the reconstruction. For this, we introduce a generalization of bundle adjustment that incorporates object-to-object correspondences. This algorithm works by constraining points for the same object from different frames to lie inside a fixed-size bounding box, parameterized by its rotation and translation. The SUN3D database, the source code for the generalized bundle adjustment, and the web-based 3D annotation tool are all available at http://sun3d.cs.princeton.edu.


title: Review of Three-Phase PWM AC–AC Converter Topologies

abstract: This paper presents first an overview of the well-known voltage and current dc-link converter topologies used to implement a three-phase PWM ac-ac converter system. Starting from the voltage source inverter and the current source rectifier, the basics of space vector modulation are summarized. Based on that, the topology of the indirect matrix converter (IMC) and its modulation are gradually developed from a voltage dc-link back-to-back converter by omitting the dc-link capacitor. In the next step, the topology of the conventional (direct) matrix converter (CMC) is introduced, and the relationship between the IMC and the CMCs is discussed in a figurative manner by investigating the switching states. Subsequently, three-phase ac-ac buck-type chopper circuits are considered as a special case of matrix converters (MCs), and a summary of extended MC topologies is provided, including three-level and hybrid MCs. Therewith, a common knowledge basis of the individual converter topologies is established.


title: The Role of Product Recommendation Agents in Collaborative Online Shopping.

abstract: Over the last decade, a wealth of research has examined the potential benefits of product recommendation agents (PRAs) for improving outcomes for e-commerce consumers and vendors. To date, however, this research has largely overlooked the fundamentally social nature of shopping. In particular, people often shop collaboratively (together) and for hedonic reasons (for enjoyment), but researchers have focused almost exclusively on isolated individuals using a PRA for utilitarian reasons. This study aims to extend past research by examining the effect of PRAs on both utilitarian and hedonic value in the context of collaborative online shopping (COS). Because communication is an inherent part of any collaborative activity, our model examines both the indirect effect of PRA use on shopping value through its effect on communication among shoppers, and the direct effect of PRA use on shopping value. We propose a moderated mediation model that predicts that: task-oriented communication (TOC) positively affects utilitarian shopping value, and social-emotional communication (SEC) positively affects hedonic shopping value; (2) PRA use reduces the amount of SECs, (3) PRA use reduces the importance of TOC; and (4) PRA use directly increases utilitarian value and directly reduces hedonic value. We describe an experiment where we are planning to test the proposed model, and its intended contributions for theory and practice.


title: The Benefits of the Electronic Customer Relationship Management to the Banks and their Customers

abstract: The current competitive market in banking sector emphasizes on the interaction, relationship management and networks to identify and satisfy the customer’s needs in order to achieve higher revenue and profits. The electronic customer relationship management (E-CRM) in the banking sector is implemented to serve customers and to achieve these objectives. Since the E-CRM is relatively new technology and evolves rapidly there is scarce evidence of the benefits of its implementation. The goal of this paper is to identify and describe the benefits of using these advance technologies in the banking sector and help the banks make informed decisions regarding the implementation and/or further improvement of an existing E-CRM. The research found that E-CRM have decreased the work load on the branches, decrease administrative cost, increase cross selling, bank revenues and enable the bankers to analyze the customers’ needs by having access to all past transactions. The e-channels have improved the dissemination of information and enabled the management to introduce new products and schemes at faster rate. The benefits experienced by customers with these advanced technologies in the banks are convenience, time and cost savings, security, and growth of trust and hence maintaining stronger attachment to the bank. The customers experienced early response to their queries and enjoyed a faster processing speed. They were found to be more secure and have greater trust in the bank than previous as a result of more interaction points, increasing communication and closer relationship with the banking staff. Keywords: Banking, Bank Management, Electronic Business, E-CRM,


title: 3D Dense-U-Net for MRI Brain Tissue Segmentation

abstract: This paper presents a fully automatic method for 3D segmentation of brain tissue on MRI scans using modern deep learning approach and proposes 3D Dense-U-Net neural network architecture using densely connected layers. In contrast with many previous methods, our approach is capable of precise segmentation without any preprocessing of the input image and achieved accuracy 99.70 percent on testing data which outperformed human expert results. The architecture proposed in this paper can also be easily applied to any project already using U-net network as a segmentation algorithm to enhance its results. Implementation was done in Keras on Tensorflow backend and complete source-code was released online.


title: Spiders in the Sky: User Perceptions of Drones, Privacy, and Security

abstract: Drones are increasingly being used for various purposes from recording footage in inaccessible areas to delivering packages. A rise in drone usage introduces privacy and security concerns about flying boundaries, what data drones collect in public and private spaces, and how that data is stored and disseminated. However, commercial and personal drone regulations focusing on privacy and security have been fairly minimal in the USA. To inform privacy and security guidelines for drone design and regulation, we need to understand users' perceptions about drones, privacy and security. In this paper, we describe a laboratory study with 20 participants who interacted with a real or model drone to elicit user perceptions of privacy and security issues around drones. We present our results, discuss the implications of our work and make recommendations to improve drone design and regulations that enhance individual privacy and security.


title: Neurological impressions on the organization of language networks in the human brain

abstract: ABSTRACTBackground: More than 95% of right-handed individuals, as well as almost 80% of left-handed individuals, have left hemisphere dominance for language. The perisylvian networks of the dominant hemisphere tend to be the most important language systems in human brains, usually connected by bidirectional fibres originated from the superior longitudinal fascicle/arcuate fascicle system and potentially modifiable by learning. Neuroplasticity mechanisms take place to preserve neural functions after brain injuries. Language is dependent on a hierarchical interlinkage of serial and parallel processing areas in distinct brain regions considered to be elementary processing units. Whereas aphasic syndromes typically result from injuries to the dominant hemisphere, the extent of the distribution of language functions seems to be variable for each individual. Method: Review of the literature Results: Several theories try to explain the organization of language networks in the human brain from a point of view tha...


title: Face recognition with liveness detection using eye and mouth movement

abstract: The recent literature on face recognition technology discusses the issue of face spoofing which can bypass the authentication system by placing a photo/video/mask of the enrolled person in front of the camera. This problem could be minimized by detecting the liveness of the person. Therefore, in this paper, we propose a robust liveness detection scheme based on challenge and response method. The liveness module is added as extra layer of security before the face recognition module. The liveness module utilizes face macro features, especially eye and mouth movements in order to generate random challenges and observing the user's response on account of this. The reliability of liveness module is tested by placing different types of spoofing attacks with various means, like using photograph, videos, etc. In all, five types of attacks have been taken care of and prevented by our system. Experimental results show that system is able to detect the liveness when subjected to all these attacks except the eye & mouth imposter attack. This attack is able to bypass the liveness test but it creates massive changes in face structure. Therefore resultant unrecognized or misclassified by the face recognition module. An experimental test conducted on 65 persons on university of Essex face database confirms that removal of eye and nose components results 75% misclassification.


title: Ricoeur’s Theory of Interpretation: A Method for Understanding Text (Course Text)

abstract: 2 Abstract: Hermeneutics, or the science of interpretation, is well accepted in the humanities. In the field of education, hermeneutics has played a relatively important role in understanding text. It is the task of this essay to introduce the theory and findings of Paul ricoeur's hermeneutics. Specifically, the essay interprets the usefulness of ricoeur's philosophy in understanding text (course text). With respect to the ricoeur's hermeneutic method, understanding test (course texts) accomplish in the three level: 1-explanation 2-understanding 3- appropriation, which ricoeur called it "hermeneutic arc." In the first level, explanation, interpreter explores about this question which text (courses text) tells something? And examine the text inside nature. In the second level (understanding), interpreter explores about this question which text (course text) speaks about something? and do all the endeavors to restore text out ingredient at a relation to live and reach for deeper understanding. In the third level (appropriation), interpreter appropriate text (course text) world for themselves. Thus, expanded interpreter horizon (knowledge and perception).


title: DWMS: Data Warehouse Management System

abstract: After a thorough investigation of end user requirement differences from a Database and a Data Warehouse, a strong case is made for specific Data Warehouse Management Systems (DWMS), in contrast to the trend of utilizing DBMS for Data Warehouse construction. The three type (Temporal, Nested Relational, Multidimensional) DWMS hypothesis, an original concept of Yoshioki Ishii, President, Software AG of Far East, Inc. is explained in detail. Customer solutions and related products are introduced.


title: Simulation and characterization of symmetrical planar spring FR4-based for Electrodynamic Vibration Energy Harvesting

abstract: This paper discusses about vibration energy harvesting using electrodynamic method, where inertial mass (a magnet) moves relative to an idle conductor. An thin planar spring with material of FR4 is used as mechanical spring, an NdFeB magnet is used as an inertial mass, and bobbin-less coil is used as the conductor. The research discussed in this paper compares the maximum voltage and resonance frequency generated by the Electrodynamic Vibration Energy Harvesting (EVEH) device with an FR4 thin planar spring with the dimensio n of 25mm × 25mm, one of which uses two 14mm symmetrical cantilever arm, and the other uses four 7mm symmetrical cantilever arm. Finite Element Analysis (FEA) Comsol 4.3a simulation software is used to design the mechanical spring and EVEH device. Fabrication of the mechanical spring is done using laser cutting method and EVEH device characterization is done using a loudspeaker as vibration energy source. From the experiment, it is found that the two symmetrical cantilever EVEH generated a maximum voltage and resonance frequency of respectively 303 mV and 45 Hz with 0,5g acceleration then 423 mV and 49 Hz with 1g, while the four symmetrical cantilever EVEH generated 235 mV and 102 Hz with 0,5g then 350 mV and 113 Hz with 1g, where g = 9,8 m/s2.


title: A Point-Cloud-Based Multiview Stereo Algorithm for Free-Viewpoint Video

abstract: This paper presents a robust multiview stereo (MVS) algorithm for free-viewpoint video. Our MVS scheme is totally point-cloud-based and consists of three stages: point cloud extraction, merging, and meshing. To guarantee reconstruction accuracy, point clouds are first extracted according to a stereo matching metric which is robust to noise, occlusion, and lack of texture. Visual hull information, frontier points, and implicit points are then detected and fused with point fidelity information in the merging and meshing steps. All aspects of our method are designed to counteract potential challenges in MVS data sets for accurate and complete model reconstruction. Experimental results demonstrate that our technique produces the most competitive performance among current algorithms under sparse viewpoint setups according to both static and motion MVS data sets.


title: Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification

abstract: Abstract We present an analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets or CNNs) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors. In many applications, especially including remote sensing, it is not feasible to fully design and train a new ConvNet, as this usually requires a considerable amount of labeled data and demands high computational costs. Therefore, it is important to understand how to better use existing ConvNets. We perform experiments with six popular ConvNets using three remote sensing datasets. We also compare ConvNets in each strategy with existing descriptors and with state-of-the-art baselines. Results point that fine tuning tends to be the best performing strategy. In fact, using the features from the fine-tuned ConvNet with linear SVM obtains the best results. We also achieved state-of-the-art results for the three datasets used.


title: Classification of Architectural Heritage Images Using Deep Learning Techniques

abstract: The classification of the images taken during the measurement of an architectural asset is an essential task within the digital documentation of cultural heritage. A large number of images are usually handled, so their classification is a tedious task (and therefore prone to errors) and habitually consumes a lot of time. The availability of automatic techniques to facilitate these sorting tasks would improve an important part of the digital documentation process. In addition, a correct classification of the available images allows better management and more efficient searches through specific terms, thus helping in the tasks of studying and interpreting the heritage asset in question. The main objective of this article is the application of techniques based on deep learning for the classification of images of architectural heritage, specifically through the use of convolutional neural networks. For this, the utility of training these networks from scratch or only fine tuning pre-trained networks is evaluated. All this has been applied to classifying elements of interest in images of buildings with architectural heritage value. As no datasets of this type, suitable for network training, have been located, a new dataset has been created and made available to the public. Promising results have been obtained in terms of accuracy and it is considered that the application of these techniques can contribute significantly to the digital documentation of architectural heritage.


title: Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic

abstract: Human motion prediction aims at generating future frames of human motion based on an observed sequence of skeletons. Recent methods employ the latest hidden states of a recurrent neural network (RNN) to encode the historical skeletons, which can only address short-term prediction. In this work, we propose a motion context modeling by summarizing the historical human motion with respect to the current prediction. A modified highway unit (MHU) is proposed for efficiently eliminating motionless joints and estimating next pose given the motion context. Furthermore, we enhance the motion dynamic by minimizing the gram matrix loss for long-term motion prediction. Experimental results show that the proposed model can promisingly forecast the human future movements, which yields superior performances over related state-of-the-art approaches. Moreover, specifying the motion context with the activity labels enables our model to perform human motion transfer.


title: GPSR: greedy perimeter stateless routing for wireless networks

abstract: We present Greedy Perimeter Stateless Routing (GPSR), a novel routing protocol for wireless datagram networks that uses the positions of routers and a packet's destination to make packet forwarding decisions. GPSR makes greedy forwarding decisions using only information about a router's immediate neighbors in the network topology. When a packet reaches a region where greedy forwarding is impossible, the algorithm recovers by routing around the perimeter of the region. By keeping state only about the local topology, GPSR scales better in per-router state than shortest-path and ad-hoc routing protocols as the number of network destinations increases. Under mobility's frequent topology changes, GPSR can use local topology information to find correct new routes quickly. We describe the GPSR protocol, and use extensive simulation of mobile wireless networks to compare its performance with that of Dynamic Source Routing. Our simulations demonstrate GPSR's scalability on densely deployed wireless networks.


title: Safe Model-based Reinforcement Learning with Stability Guarantees

abstract: Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. However, to find optimal policies, most reinforcement learning algorithms explore all possible actions, which may be harmful for real-world systems. As a consequence, learning algorithms are rarely applied on safety-critical systems in the real world. In this paper, we present a learning algorithm that explicitly considers safety, defined in terms of stability guarantees. Specifically, we extend control-theoretic results on Lyapunov stability verification and show how to use statistical models of the dynamics to obtain high-performance control policies with provable stability certificates. Moreover, under additional regularity assumptions in terms of a Gaussian process prior, we prove that one can effectively and safely collect data in order to learn about the dynamics and thus both improve control performance and expand the safe region of the state space. In our experiments, we show how the resulting algorithm can safely optimize a neural network policy on a simulated inverted pendulum, without the pendulum ever falling down.


title: 3D printing of variable stiffness hyper-redundant robotic arm

abstract: Shape memory polymer (SMP) is a type of functional materials that changes Young's modulus when heated above glass transition temperature (Tg). In this work, this property of SMP has been explored for the design and fabrication of a modular omni-directional joint with variable stiffness. When cascading a number of such joints, a variable stiffness hyper-redundant robotic arm can be built. The basic design of the variable stiffness joint is based on a ball joint where the ball is made of two materials: acrylonitrile butadiene styrene (ABS) and SMP, and the socket is made of only ABS material. When heated, the ball joint shows different resistive torques below and above the SMP's glass transition temperature Tg. Moreover, shape recovery property of SMP material above Tg guarantees the design with high repeatability. Both the ball and the socket are made by a 3D printing process fused deposition modeling (FDM). The FDM fabrication of SMP is made possible by a novel process control method in the FDM process. The ball joint's variable stiffness is tested by a number of experiments. Experimental results indicate distinct changes in resistive torques at different test temperatures. Using the proposed modular omni-directional ball joints, a variable stiffness hyper-redundant robotic arm is built.


title: A PCB Dataset for Defects Detection and Classification

abstract: To coupe with the difficulties in the process of inspection and classification of defects in Printed Circuit Board (PCB), other researchers have proposed many methods. However, few of them published their dataset before, which hindered the introduction and comparison of new methods. In this paper, we published a synthesized PCB dataset containing 1386 images with 6 kinds of defects for the use of detection, classification and registration tasks. Besides, we proposed a reference based method to inspect and trained an end-to-end convolutional neural network to classify the defects. Unlike conventional approaches that require pixel-by-pixel processing, our method firstly locate the defects and then classify them by neural networks, which shows superior performance on our dataset.


CAT: Credibility Analysis of Arabic Content on Twitter


title: Theoretical Neuroanatomy: Relating Anatomical and Functional Connectivity in Graphs and Cortical Connection Matrices

abstract: A pneumatic control instrument includes a nozzle-flapper system having improved dynamic response characteristics under vibrational conditions. A light-mass, compliant flapper having a high natural frequency covers the nozzle and serves to cushion the impact of the nozzle striking the flapper by moving at the same amplitude of vibration as the nozzle so as to precisely track the nozzle over a wide range of vibrational frequencies and amplitudes.


title: Intuition: a social cognitive neuroscience approach

abstract: This review proposes that implicit learning processes are the cognitive substrate of social intuition. This hypothesis is supported by (a) the conceptual correspondence between implicit learning and social intuition (nonverbal communication) and (b) a review of relevant neuropsychological (Huntington's and Parkinson's disease), neuroimaging, neurophysiological, and neuroanatomical data. It is concluded that the caudate and putamen, in the basal ganglia, are central components of both intuition and implicit learning, supporting the proposed relationship. Parallel, but distinct, processes of judgment and action are demonstrated at each of the social, cognitive, and neural levels of analysis. Additionally, explicit attempts to learn a sequence can interfere with implicit learning. The possible relevance of the computations of the basal ganglia to emotional appraisal, automatic evaluation, script processing, and decision making are


title: Perspectives on safety culture

abstract: Overviewing selected elements from the literature, this paper locates the notion of safety culture within its parent concept of organisational culture. A distinction is drawn between functionalist and interpretive perspectives on organisational culture. The terms ‘culture’ and ‘climate’ are clarified as they are typically applied to organisations and to safety. A contrast is drawn between strategic top down and data-driven bottom up approaches to human factors as an illustrative aspect of safety. A safety case study is used to illustrate two measurement approaches. Key issues for future study include valid measurement of safety culture and developing methods to adequately represent mechanisms through which safety culture might influence, and be influenced by, other safety factors.


title: Identifying nude pictures

abstract: This paper demonstrates an automatic system for telling whether there are naked people present in an image. The approach combines color and texture properties to obtain a mask for skin regions, which is shown to be effective for a wide range of shades and colors of skin. These skin regions are then fed to a specialized grouper, which attempts to group a human figure using geometric constraints on human structure. This approach introduces a new view of object recognition, where an object model is an organized collection of grouping hints obtained from a combination of constraints on color and texture and constraints on geometric properties such as the structure of individual parts and the relationships between parts. The system demonstrates excellent performance on a test set of 565 uncontrolled images of naked people, mostly obtained from the internet, and 4289 assorted control images, drawn from a wide collection of sources.


title: Male Perineogenital Anatomy and Clinical Applications in Genital Reconstructions and Male-to-Female Sex Reassignment Surgery

abstract: To determine the possibility of providing alternative surgical techniques for male genital reconstruction and for male-to-female sex reassignment surgery, the authors undertook an anatomic investigation of the perineogenital region in male cadavers. Anatomic dissection was performed on 14 male adult human cadavers (fresh and formalin-preserved) studying the main afferent vessels to the anterior perineal region and their mean internal diameters: deep external pudendal artery (0.60 mm), superficial perineal artery (0.50 mm), and funicular artery (0.37 mm). We established their exact topography, together with vascular anatomic variations, main vascular anastomosis circuits (base of the penis, scrotal septum, and perineal fat and lateral spermatic-scrotal fascia), angiosomes, anatomy of the rectovesical septum cavity, and their “critical” key points of dissection. The authors discuss the clinical possibility of elevation of a “tree” of previously described paragenital-genital flaps including mainly those based on the terminal branches of the internal pudendal vascular system, the erectile tissue pedicled flaps, and finally, flaps of the external pudendal system. The authors indicate the concrete vascularization system for each flap. (Plast. Reconstr. Surg. 109: 1301, 2002.)


title: AugAuth: Shoulder-surfing resistant authentication for augmented reality

abstract: As computing system continues to play an increasing role in daily life, user authentication is now an important component. One of the most widely accepted methods for user authentication is through proof of knowledge of a piece of secret information, such as password. However, entering this non-mutable secret for authentication in public space often allows attackers to steal the secret by shoulder surfing or video recording. We observe that it is possible to block attacker's access to user input using augmented reality (AR) display, which is only available to the user. Based on this intuition, we present AugAuth, an authentication scheme in AR using commercial off-the-shelf(COTS) gesture control sensors as an input device. AugAuth can resist against shoulder surfing by presenting user input interface that is only visible to the user and is unique every time. To enable user input with finger movement using the gesture control armband, Myo, we have solved several challenges in electromyogram signal processing, such as annotating the start of signal and finger classification. The experiment results for our input system of a group of volunteers show that our finger classification function has high accuracy and AugAuth is practical for use in real life authentication scenarios.


title: Link Prediction via Matrix Factorization

abstract: We propose to solve the link prediction problem in graphs using a supervised matrix factorization approach. The model learns latent features from the topological structure of a (possibly directed) graph, and is shown to make better predictions than popular unsupervised scores. We show how these latent features may be combined with optional explicit features for nodes or edges, which yields better performance than using either type of feature exclusively. Finally, we propose a novel approach to address the class imbalance problem which is common in link prediction by directly optimizing for a ranking loss. Our model is optimized with stochastic gradient descent and scales to large graphs. Results on several datasets show the efficacy of our approach.


title: Compressive Coded Aperture Keyed Exposure Imaging with Optical Flow Reconstruction

abstract: This paper describes a coded aperture and keyed exposure approach to compressive video measurement which admits a small physical platform, high photon efficiency, high temporal resolution, and fast reconstruction algorithms. The proposed projections satisfy the Restricted Isometry Property (RIP), and hence compressed sensing theory provides theoretical guarantees on the video reconstruction quality. Moreover, the projections can be easily implemented using existing optical elements such as spatial light modulators (SLMs). We extend these coded mask designs to novel dual-scale masks (DSMs) which enable the recovery of a coarse-resolution estimate of the scene with negligible computational cost. We develop fast numerical algorithms which utilize both temporal correlations and optical flow in the video sequence as well as the innovative structure of the projections. Our numerical experiments demonstrate the efficacy of the proposed approach on short-wave infrared data.


title: Inferring Anchor Links Based on Social Network Structure

abstract: Nowadays, people usually participate in multiple social networks simultaneously, e.g., Facebook and Twitter. Formally, the correspondences of the accounts that belong to the same user are defined as anchor links, and the networks aligned by anchor links can be denoted as aligned networks. In this paper, we study the problem of anchor link prediction (ALP) across a pair of aligned networks based on social network structure. First, three similarity metrics (CPS, CCS, and CPS+) are proposed. Different from the previous works, we focus on the theoretical guarantees of our metrics. We prove mathematically that the node pair with the maximum CPS or CPS+ should be an anchor link with high probability and a correctly predicted anchor link must have a high value of CCS. Second, using the CPS+ and CCS, we present a two-stage iterative algorithm CPCC to solve the problem of the ALP. More specifically, we present an early termination strategy to make a tradeoff between precision and recall. At last, a series of experiments are conducted on both synthetic and real-world social networks to demonstrate the effectiveness of the CPCC.


Parent-Adolescent Communication, Role Model in Sexuality and Their Impact on Sexual Health of Teens


title: Big data analytics for demand response: Clustering over space and time

abstract: The pervasive deployment of advanced sensing infrastructure in Cyber-Physical systems, such as the Smart Grid, has resulted in an unprecedented data explosion. Such data exhibit both large volumes and high velocity characteristics, two of the three pillars of Big Data, and have a time-series notion as datasets in this context typically consist of successive measurements made over a time interval. Time-series data can be valuable for data mining and analytics tasks such as identifying the "right" customers among a diverse population, to target for Demand Response programs. However, time series are challenging to mine due to their high dimensionality. In this paper, we motivate this problem using a real application from the smart grid domain. We explore novel representations of time-series data for BigData analytics, and propose a clustering technique for determining natural segmentation of customers and identification of temporal consumption patterns. Our method is generizable to large-scale, real-world scenarios, without making any assumptions about the data. We evaluate our technique using real datasets from smart meters, totaling ∼ 18,200,000 data points, and show the efficacy of our technique in efficiency detecting the number of optimal number of clusters.


title: EAF2- A Framework for Categorizing Enterprise Architecture Frameworks

abstract: What constitutes an enterprise architecture framework is a contested subject. The contents of present enterprise architecture frameworks thus differ substantially. This paper aims to alleviate the confusion regarding which framework contains what by proposing a meta framework for enterprise architecture frameworks. By using this meta framework, decision makers are able to express their requirements on what their enterprise architecture framework must contain and also to evaluate whether the existing frameworks meets these requirements. An example classification of common EA frameworks illustrates the approach.


title: BIDMach: Large-scale Learning with Zero Memory Allocation

abstract: This paper describes recent work on the BIDMach toolkit for large-scale machine learning. BIDMach has demonstrated single-node performance that exceeds that of published cluster systems for many common machine-learning task. BIDMach makes full use of both CPU and GPU acceleration (through a sister library BIDMat), and requires only modest hardware (commodity GPUs). One of the challenges of reaching this level of performance is the allocation barrier. While it is simple and expedient to allocate and recycle matrix (or graph) objects in expressions, this approach is too slow to match the arithmetic throughput possible on either GPUs or CPUs. In this paper we describe a caching approach that allows code with complex matrix (graph) expressions to run at massive scale, i.e. multi-terabyte data, with zero memory allocation after initial start-up. We present a number of new benchmarks that leverage this approach.


title: Distributed social graph embedding

abstract: Distributed recommender systems are becoming increasingly important for they address both scalability and the Big Brother syndrome. Link prediction is one of the core mechanism in recommender systems and relies on extracting some notion of proximity between entities in a graph. Applied to social networks, defining a proximity metric between users enable to predict potential relevant future relationships. In this paper, we propose SoCS (Social Coordinate Systems}, a fully distributed algorithm that embeds any social graph in an Euclidean space, which can easily be used to implement link prediction. To the best of our knowledge, SoCS is the first system explicitly relying on graph embedding. Inspired by recent works on non-isomorphic embeddings, the SoCS embedding preserves the community structure of the original graph, while being easy to decentralize. Nodes thus get assigned coordinates that reflect their social position. We show through experiments on real and synthetic data sets that these coordinates can be exploited for efficient link prediction.


title: Asynchronous Stochastic Variational Inference

abstract: Stochastic variational inference (SVI) employs stochastic optimization to scale up Bayesian computation to massive data. Since SVI is at its core a stochastic gradient-based algorithm, horizontal parallelism can be harnessed to allow larger scale inference. We propose a lock-free parallel implementation for SVI which allows distributed computations over multiple slaves in an asynchronous style. We show that our implementation leads to linear speed-up while guaranteeing an asymptotic ergodic convergence rate $O(1/\sqrt(T)$ ) given that the number of slaves is bounded by $\sqrt(T)$ ($T$ is the total number of iterations). The implementation is done in a high-performance computing (HPC) environment using message passing interface (MPI) for python (MPI4py). The extensive empirical evaluation shows that our parallel SVI is lossless, performing comparably well to its counterpart serial SVI with linear speed-up.


title: Satellite radar interferometry for monitoring ice sheet motion: application to an antarctic ice stream.

abstract: Satellite radar interferometry (SRI) provides a sensitive means of monitoring the flow velocities and grounding-line positions of ice streams, which are indicators of response of the ice sheets to climatic change or internal instability. The detection limit is about 1.5 millimeters for vertical motions and about 4 millimeters for horizontal motions in the radar beam direction. The grounding line, detected by tidal motions where the ice goes afloat, can be mapped at a resolution of approximately 0.5 kilometer. The SRI velocities and grounding line of the Rutford Ice Stream, Antarctica, agree fairly well with earlier ground-based data. The combined use of SRI and other satellite methods is expected to provide data that will enhance the understanding of ice stream mechanics and help make possible the prediction of ice sheet behavior.


title: Semantic Parsing to Probabilistic Programs for Situated Question Answering

abstract: Situated question answering is the problem of answering questions about an environment such as an image or diagram. This problem requires jointly interpreting a question and an environment using background knowledge to select the correct answer. We present Parsing to Probabilistic Programs (P3), a novel situated question answering model that can use background knowledge and global features of the question/environment interpretation while retaining efficient approximate inference. Our key insight is to treat semantic parses as probabilistic programs that execute nondeterministically and whose possible executions represent environmental uncertainty. We evaluate our approach on a new, publicly-released data set of 5000 science diagram questions, outperforming several competitive classical and neural baselines.


title: Dynamic and temporal user profiling for personalized recommenders using heterogeneous data sources

abstract: In modern Web applications, the process of user-profiling provides a way to capture user-specific information, which then serves as a source for designing personalized user experiences. Currently, such information about a particular user is available from multiple online sources/services, like social media applications, professional/social networking sites, location based service providers or even from simple Web-pages. The nature of this data being truly heterogeneous, high in volume and also highly dynamic over time, the problem of collecting these data artifacts from disparate sources, to enable complete user-profiling can be challenging. In this paper, we present an approach to dynamically build a structured user profile, that emphasizes the temporal nature to capture dynamic user behavior. The user profile is compiled from multiple, heterogeneous data sources which capture dynamic user actions over time, to capture changing preferences accurately. Natural language processing techniques, machine learning and concepts of the semantic Web were used for capturing relevant user data and implement the proposed “3D User Profile”. Our technique also supports the representation of the generated user profiles as structured data so that other personalized recommendation systems and Semantic Web/Linked Open Data applications can consume them for providing intelligent, personalized services.


title: Cross-linked poly(arylene ether ketone) membranes sulfonated on both backbone and pendant position for high proton conduction and low water uptake

abstract: Abstract A series of cross-linkable poly(arylene ether ketone)s (PAEKs) with sulfonic acid groups on both the backbone and pendant positions are synthesized through direct condensation polymerization. The degree of sulfonation (DS) of the polymer backbone is controlled by changing the feed ratios of sulfonated to unsulfonated monomers. Post-polymerization reactions are successfully employed to prepare polymer electrolyte membranes for fuel cell applications. The chemical structures of the synthesized polymers are confirmed by 1 H nuclear magnetic resonance (NMR) and Fourier transform infrared (FT-IR) spectroscopy. The thermal and mechanical properties of the membranes are characterized by thermogravimetric analysis and stress–strain test. The dependence of water uptake, methanol permeability, proton conductivity, and selectivity on DS is studied. The cross-linked sulfonated PAEK (CSPAEK) membranes with DS higher than 120% exhibit higher proton conductivity than those of Nafion ® 117. The CSPAEK membranes maintain very low methanol permeability and water uptake. The properties of all the membrane are correlated with ionic cluster dimension measured by small angle X-ray scattering (SAXS).


title: Centralized and Distributed Anonymization for High-Dimensional Healthcare Data

abstract: Sharing healthcare data has become a vital requirement in healthcare system management; however, inappropriate sharing and usage of healthcare data could threaten patients’ privacy. In this article, we study the privacy concerns of sharing patient information between the Hong Kong Red Cross Blood Transfusion Service (BTS) and the public hospitals. We generalize their information and privacy requirements to the problems of centralized anonymization and distributed anonymization, and identify the major challenges that make traditional data anonymization methods not applicable. Furthermore, we propose a new privacy model called LKC-privacy to overcome the challenges and present two anonymization algorithms to achieve LKC-privacy in both the centralized and the distributed scenarios. Experiments on real-life data demonstrate that our anonymization algorithms can effectively retain the essential information in anonymous data for data analysis and is scalable for anonymizing large datasets.


title: Threat Intelligence Computing

abstract: Cyber threat hunting is the process of proactively and iteratively formulating and validating threat hypotheses based on security-relevant observations and domain knowledge. To facilitate threat hunting tasks, this paper introduces threat intelligence computing as a new methodology that models threat discovery as a graph computation problem. It enables efficient programming for solving threat discovery problems, equipping threat hunters with a suite of potent new tools for agile codifications of threat hypotheses, automated evidence mining, and interactive data inspection capabilities. A concrete realization of a threat intelligence computing platform is presented through the design and implementation of a domain-specific graph language with interactive visualization support and a distributed graph database. The platform was evaluated in a two-week DARPA competition for threat detection on a test bed comprising a wide variety of systems monitored in real time. During this period, sub-billion records were produced, streamed, and analyzed, dozens of threat hunting tasks were dynamically planned and programmed, and attack campaigns with diverse malicious intent were discovered. The platform exhibited strong detection and analytics capabilities coupled with high efficiency, resulting in a leadership position in the competition. Additional evaluations on comprehensive policy reasoning are outlined to demonstrate the versatility of the platform and the expressiveness of the language.


title: Sensorized pneumatic muscle for force and stiffness control

abstract: This paper presents the design and experimental validation of a soft pneumatic artificial muscle with position and force sensing capabilities. Conductive liquid-based soft sensors are embedded in a fiber-reinforced contractile actuator to measure two modes of deformation — axial strain and diametral expansion — which, together, are used to determine the stroke length and contractile force generated under internal pressure. We validate the proposed device by using data from the embedded sensors to estimate the force output of the actuator at fixed lengths and the stiffness and force output of a one degree-of-freedom hinge joint driven by an antagonist pair of the sensorized pneumatic muscles.


title: Neural Network Translation Models for Grammatical Error Correction

abstract: Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy. The superiority of SMT systems comes from their ability to learn text transformations from erroneous to corrected text, without explicitly modeling error types. However, phrase-based SMT systems suffer from limitations of discrete word representation, linear mapping, and lack of global context. In this paper, we address these limitations by using two different yet complementary neural network models, namely a neural network global lexicon model and a neural network joint model. These neural networks can generalize better by using continuous space representation of words and learn non-linear mappings. Moreover, they can leverage contextual information from the source sentence more effectively. By adding these two components, we achieve statistically significant improvement in accuracy for grammatical error correction over a state-of-the-art GEC system.


title: Image Captioning at Will: A Versatile Scheme for Effectively Injecting Sentiments into Image Descriptions

abstract: Automatic image captioning has recently approached human-level performance due to the latest advances in computer vision and natural language understanding. However, most of the current models can only generate plain factual descriptions about the content of a given image. However, for human beings, image caption writing is quite flexible and diverse, where additional language dimensions, such as emotion, humor and language styles, are often incorporated to produce diverse, emotional, or appealing captions. In particular, we are interested in generating sentiment-conveying image descriptions, which has received little attention. The main challenge is how to effectively inject sentiments into the generated captions without altering the semantic matching between the visual content and the generated descriptions. In this work, we propose two different models, which employ different schemes for injecting sentiments into image captions. Compared with the few existing approaches, the proposed models are much simpler and yet more effective. The experimental results show that our model outperform the state-of-the-art models in generating sentimental (i.e., sentiment-bearing) image captions. In addition, we can also easily manipulate the model by assigning different sentiments to the testing image to generate captions with the corresponding sentiments.