title: Autoerotic asphyxiation in a female

abstract: Autoerotic asphyxiation is an unusual but increasingly more frequently occurring phenomenon, with >1000 fatalities in the United States per year. Understanding of this manner of death is likewise increasing, as noted by the growing number of cases reported in the literature. However, this form of accidental death is much less frequently seen in females (male:female ratio >50:1), and there is correspondingly less literature on female victims of autoerotic asphyxiation. The authors present the case of a 31-year-old woman who died of an autoerotic ligature strangulation and review the current literature on the subject. The forensic examiner must be able to discern this syndrome from similar forms of accidental and suicidal death, and from homicidal hanging/strangulation. Language: en


title: Salivary Biomarkers and Oral Microbial Load in Relation to the Dental Status of Adults with Cystic Fibrosis

abstract: The mutation of cystic fibrosis transmembrane conductance regulator (CFTR) can modify the physical and chemical properties of saliva, which in turn can affect the oral microflora and oral health in patients with cystic fibrosis (CF). The aim of the study was to examine oral health status, salivary properties, and total oral bacteria count in CF adults. Dental status was assessed using the decayed missing filled surfaces (DMF-S) index, and oral clearness using the approximal plaque index (API). The Saliva-Check BUFFER test was used to assess saliva, and real-time polymerase chain reaction (PCR) test to determine the total oral bacteria count. CF patients in comparison to healthy controls showed a higher level of examined clinical indices, higher total oral bacteria count, lower salivary flow rate, lower salivary pH, and increased viscosity. Conclusions: In CF patients, saliva properties, accompanied by insufficient dental care, might be an essential dental caries risk factor. In CF patients, among the etiological factors for dental caries, the bacterial agent seems to be less significant. The frequent and long-term infectious pharmacotherapy can probably explain that. A great deal of the information collected on the oral environment in CF patients, which has helped us understand the etiological conditions for inflammation and infection in this area of the body, indicates that proper dental care can mostly counteract these pathologies.


title: Analytical modeling for EVM in OFDM transmitters including the effects of IIP3, I/Q imbalance, noise, AM/AM and AM/PM distortion

abstract: In this paper, we present a method for accurately calculating the Error Vector Magnitude (EVM) of OFDM transmitters based on their IQ mismatch, IIP3, noise, AM/AM and AM/PM distortion. The effects of these impairments are correlated. Thus, modeling and analyzing them in isolation results in large errors. We derive the analytical relation of the received symbol when all types of impairments are present at once and compute EVM based on this overall relation. This method helps test engineers to compute the EVM based on already measured parameters and eliminates the need to develop and set-up EVM measurements. Simulations and hardware measurements show that this calculation can be done with less than 1% error for a large range of EVM values. This error level has been shown to be within the uncertainty bounds of EVM measurements.


title: Problem-Based Learning: What and How Do Students Learn?

abstract: Problem-based approaches to learning have a long history of advocating experience-based education. Psychological research and theory suggests that by having students learn through the experience of solving problems, they can learn both content and thinking strategies. Problem-based learning (PBL) is an instructional method in which students learn through facilitated problem solving. In PBL, student learning centers on a complex problem that does not have a single correct answer. Students work in collaborative groups to identify what they need to learn in order to solve a problem. They engage in self-directed learning (SDL) and then apply their new knowledge to the problem and reflect on what they learned and the effectiveness of the strategies employed. The teacher acts to facilitate the learning process rather than to provide knowledge. The goals of PBL include helping students develop 1) flexible knowledge, 2) effective problem-solving skills, 3) SDL skills, 4) effective collaboration skills, and 5) intrinsic motivation. This article discusses the nature of learning in PBL and examines the empirical evidence supporting it. There is considerable research on the first 3 goals of PBL but little on the last 2. Moreover, minimal research has been conducted outside medical and gifted education. Understanding how these goals are achieved with less skilled learners is an important part of a research agenda for PBL. The evidence suggests that PBL is an instructional approach that offers the potential to help students develop flexible understanding and lifelong learning skills.


title: The relationships among the Big Five Personality factors, self-esteem, narcissism, and sensation-seeking to Chinese University students' uses of social networking sites (SNSs)

abstract: It has been suggested that personality factors are related to an individuals' use of social networking sites (SNSs). The present research explores the relationships between the Big Five Personality factors, narcissism, self-esteem, and sensation seeking and individuals' use of specific features of SNS. Self-reports were collected from 265 SNS users from a university in China. Regression analyses revealed that personality factors play an important role in how SNS are used. Specifically, extraverts are more likely to use the communicative function of SNS including status update, comment, and adding more friends. Neurotic are more likely to use the feature of status update as a way of self-expression. Agreeable individuals tend to make more comments on others' profiles. Individuals with high self-esteem are more likely to comment on others' profiles. Users scoring high on openness and sensation seeking are more likely to play online games on SNS. Narcissistic users are more likely to upload their attractive photos on SNS and tend to use update status more frequently for self-presentation. Gender also played an important role in predicting types of SNS use. Males reported more SNS friends and were more likely to play online games than were females, who were more likely to upload self-photos and update their status.


title: Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition

abstract: In this paper, a novel architecture for a deep recurrent neural network, residual LSTM is introduced. A plain LSTM has an internal memory cell that can learn long term dependencies of sequential data. It also provides a temporal shortcut path to avoid vanishing or exploding gradients in the temporal domain. The residual LSTM provides an additional spatial shortcut path from lower layers for efficient training of deep networks with multiple LSTM layers. Compared with the previous work, highway LSTM, residual LSTM separates a spatial shortcut path with temporal one by using output layers, which can help to avoid a conflict between spatial and temporal-domain gradient flows. Furthermore, residual LSTM reuses the output projection matrix and the output gate of LSTM to control the spatial information flow instead of additional gate networks, which effectively reduces more than 10% of network parameters. An experiment for distant speech recognition on the AMI SDM corpus shows that 10-layer plain and highway LSTM networks presented 13.7% and 6.2% increase in WER over 3-layer aselines, respectively. On the contrary, 10-layer residual LSTM networks provided the lowest WER 41.0%, which corresponds to 3.3% and 2.8% WER reduction over plain and highway LSTM networks, respectively.


Retrieving Semantically Similar Questions in Community Question


title: Analysis of large scale photovoltaic power system integration into the existing utility grid using PSAT

abstract: When alternative power sources are connected to the utility grid the core characteristics of the system are changed. This fact is especially true for intermittent, volatile power sources such as photovoltaic (PV) systems. In recent years, PV power systems have become increasingly cost competitive leading to greater penetrations of both large and small scale PV systems. The large scale integration of PV power systems into the existing utility grid may have significant - and potentially negative - impacts on the power flow, power quality, and overall stability of the system. This paper analyzes the impacts of connecting a large scale photovoltaic power source to the existing utility grid using the MATLAB based Power System Analysis Toolbox (PSAT). This paper reveals a solid method for analyzing the effects a large scale PV system can have on the stability of the power grid and the results of this analysis reveal the potentially harmful effects of integrating such a system into the existing grid.


title: Semantic integration of security knowledge sources

abstract: For evaluating the security in enterprise systems, we need a security model that is amenable to integration, automated comparison and assessment. Over the years, practitioners have used various frameworks or standards (COBIT, ISO 2700x family) as checklists against which they could assess the security level in enterprise systems. On the other hand, researchers were more concerned with analysing security requirements or designing software for describing those requirements. These security models are either too general and contain unnecessary information for a particular organisation, or are highly dependent on the expertise of a security engineer. Besides these standards that are giving guidelines regarding security, there are many other sources like books, blogs, news, trainings and courses that produce security recommendations, there are regulations that mandate awareness about security, but still, organisations are missing appropriate knowledge and their systems are often attacked and breached. Having all these means available at a click of a mouse, it is questionable how can we use them in order to find the right solution that generates appropriate behaviour. This paper intends to tackle this problem by formulating a proposal for a methodology to setup a hybrid security knowledge repository for enterprise systems. The proposed solution will enable various stakeholders to add/share security knowledge in/from a repository and at the same time can use external trusted security data (from collaborative partners or reliable organisations) to enrich this knowledge base via semantic inter-operability techniques. Combining various online sources with knowledge from security officers would enable inferring new insights. The security knowledge holds some key characteristics that are considered as imperative. We referred to these as the 5C: Complexity, Continuity, Comprehensive and Cross-organisations, Compliance and Common representation. Our approach is making use of a common representation based on semantic technology for describing both security requirements pertaining to an organisation and trusted external security data.


title: Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning

abstract: This paper presents KeypointNet, an end-to-end geometric reasoning framework to learn an optimal set of category-specific 3D keypoints, along with their detectors. Given a single image, KeypointNet extracts 3D keypoints that are optimized for a downstream task. We demonstrate this framework on 3D pose estimation by proposing a differentiable objective that seeks the optimal set of keypoints for recovering the relative pose between two views of an object. Our model discovers geometrically and semantically consistent keypoints across viewing angles and instances of an object category. Importantly, we find that our end-to-end framework using no ground-truth keypoint annotations outperforms a fully supervised baseline using the same neural network architecture on the task of pose estimation. The discovered 3D keypoints on the car, chair, and plane categories of ShapeNet are visualized at this http URL


title: Adversarial examples: A survey

abstract: Adversarial examples are a phenomenon that have gathered a lot of attention in recent studies. The fact that the addition of very small, but carefully crafted perturbations to the inputs of sophisticated and high performing machine learning models may cause them to make significant errors, is both fascinating and important. A survey of findings connected with adversarial examples is presented and discussed in this paper.


Malum Prohibitum and Retributivism


title: Interactive volumetric fog display

abstract: Traditional fog screens are 2D. We propose a new design of fog screen that can generate fog at different depth positions and at only where necessary. Our approach applies projection mapping onto a non-planar and reconfigurable fog screen, thus enabling interactive visual contents to be displayed at multiple depth levels. Viewers can perceive the three-dimensionality naturally, and interact with the unencumbered images by touching them directly in mid-air. The display can be used in mixed reality settings where physical objects co-exist and interact with the 3D imagery in physical space.


title: Preclinical Pharmacokinetics and Biodistribution Studies of 89Zr-Labeled Pembrolizumab.

abstract: Pembrolizumab is a humanized monoclonal antibody targeting programmed cell death protein 1 (PD-1) found on T and pro-B cells. Pembrolizumab prevents PD-1 ligation by both PD-L1 and PD-L2, preventing the immune dysregulation that otherwise occurs when T-cells encounter cells expressing these ligands. Clinically, PD-1 blockade elicits potent antitumor immune responses, and antibodies blocking PD-1 ligation, including pembrolizumab, have recently received Food and Drug Administration approval for the treatment of advanced melanoma, renal cell cancer, and non-small cell lung cancer. ::: ::: ::: METHODS ::: In this study, we evaluated the pharmacokinetics, biodistribution, and dosimetry of pembrolizumab in vivo, accomplished through radiolabeling with the positron emitter 89Zr. PET imaging was used to evaluate the whole-body distribution of 89Zr-deferoxamine (Df)-pembrolizumab in two rodent models (mice and rats). Data obtained from PET scans and biodistribution studies were extrapolated to humans to estimate the dosimetry of the tracer. As a proof of concept, the biodistribution of 89Zr-Df-pembrolizumab was further investigated in a humanized murine model. ::: ::: ::: RESULTS ::: The tracer remained stable in blood circulation throughout the study and accumulated the greatest in liver and spleen tissues. Both mice and rats showed similar biodistribution and pharmacokinetics of 89Zr-Df-pembrolizumab. In the humanized mouse model, T-cell infiltration into the salivary and lacrimal glands could be successfully visualized. ::: ::: ::: CONCLUSION ::: These data will augment our understanding of the pharmacokinetics and biodistribution of radiolabeled pembrolizumab in vivo, while providing detailed dosimetry data that may lead to better dosing strategies in the future. These findings further demonstrate the utility of noninvasive in vivo PET imaging to dynamically track T-cell checkpoint receptor expression and localization in a humanized mouse model.


title: Defects of the nose, lip, and cheek: rebuilding the composite defect.

abstract: BACKGROUND ::: The face can be divided into regions (units) with characteristic skin quality, border outline, and three-dimensional contour. A defect may lie entirely within a single major unit or encompass several adjacent units, creating unique problems for repair. Composite defects overlap two or more facial units. Nasal defects often extend into the adjacent lip and cheek. The simplest solution may appear to be to simply "fill the hole"--just replace the missing bulk. Poor contour, incorrect dimension, malposition, asymmetry, poor blending into adjacent normal tissues, and a patch-like repair often follow. ::: ::: ::: METHODS ::: The following principles of regional unit repair were applied to guide these complex reconstructions: (1) reconstruct units, not defects; (2) alter the wound in site, size, shape, and depth; (3) consider using separate grafts and flaps for each unit and subunit, if appropriate; (4) use "like" tissue for "like" tissue; (5) restore a stable platform; (6) build in stages; (7) use distant tissue for "invisible" needs and local skin for resurfacing; and (8) disregard old scars. ::: ::: ::: RESULTS ::: Clinical cases of increasing composite complexity were repaired with local, regional, and distant tissues. Excellent aesthetics and function were obtained. ::: ::: ::: CONCLUSIONS ::: Careful visual analysis of the normal face and the defect can direct the choice, timing, and technique of facial repair. Regional unit principles provide a coordinated approach to the vision, planning, and fabrication of these difficult wounds. The entire repair should be intellectually planned, designed step by step, and laid out in a series of coordinated steps, with general principles applied to successfully repair composite defects of the nose, lip, and cheek.


title: A possible alternate pathway for intravascular thrombosis - Investigation of the circumstantial evidence by microfluidics

abstract: In light of the literature, in this hypothesis, we aimed to settle up an experimental procedure and possible mechanism for bacteremia induced disseminated intravascular coagulation via QA. Bacteremia resulting in sepsis and disseminated intravascular coagulation (DIC) are known for thrombosis and coagulopathy. DIC, which results in simultaneous activation and consumption of coagulation factors, could be investigated using microfluidics as a tool. Here, we propose the hypothesis that bacteria (e.g. E.coli) mediated DIC results from a collective phenomenon called “quorum acting” (QA). If our hypothesis is true, than the coagulation cascade will be activated before systemic inflammation. To check for QA we propose to perform a hemodynamic experiment where blood is controllably flown over E.coli clusters in a microfluidic device. Further, manipulation of the physical properties (flow rate mimicking condition like venous stasis) and chemical properties (hyperglycaemia as in uncontrolled diabetes mellitus) of blood could be done using microfluidic dev i ce to mimic their etiopathogenesis and to validate our proposed mechanism that quorum acting mediated DIC occurs rapidly in venous stasis and uncontrolled diabetes mellitus respectively.


title: Dual-Band Slot Helix Antenna for Global Positioning Satellite Applications

abstract: A slot helix antenna working in Global Positioning System (GPS) L1 and L2 bands is proposed in this paper. Adopting sequential rotation technique, the proposed antenna is equipped with six helix radiation arms and fed by a microstrip line with folded shape. The whole antenna can be built on a full printed circuit board; so, it is easy to process the proposed antenna. The circularly polarized radiation mechanism of a circular array is analyzed in a new perspective to guide the design. A double-faced slot radiation structure is also established for dual-band operation. Additionally, a traveling wave feeding network suitable for the proposed antenna is presented. The experimental results show that the proposed antenna has good properties in GPS L1 and L2 bands.


title: Theoretical Considerations on Men’s Experiences of Intimate Partner Violence: An Interview-Based Study

abstract: This study aims at exploring and interpreting men’s experiences of intimate partner violence (IPV) in the light of selected current theoretical contributions to the field, with an emphasis on Michael P. Johnson’s violence typology. The material consisted of twenty interviews with men who self-identified as having been subjected to IPV. Men generally did not consider physical violence to be threatening when it was perpetrated by women. They were also not subjected to the multiple control tactics that define the intimate terrorism category of Johnson’s violence typology, lending support to the argument that women’s and men’s experiences of IPV differ in opposite-sex relationships. Furthermore, our findings encourage the integration of structural inequalities related to gender and sexuality in analyses of men’s experiences of IPV.


title: Application of artificial neural network in detection of DOS attacks

abstract: A solo attack may cause a big loss in computer and network systems, its prevention is, therefore, very inevitable. Precise detection is very important to prevent such losses. Such detection is a pivotal part of any security tools like intrusion detection system, intrusion prevention system, and firewalls etc. Therefore, an approach is provided in this paper to analyze denial of service attack by using a supervised neural network. The methodology used sampled data from Kddcup99 dataset, an attack database that is a standard for judgment of attack detection tools. The system uses multiple layered perceptron architecture and resilient backpropagation for its training and testing. The developed system is then applied to denial of service attacks. Moreover, its performance is also compared to other neural network approaches which results more accuracy and precision in detection rate.


MEAT FRESHNESS IDENTIFICATION SYSTEM USING GAS SENSOR ARRAY AND COLOR SENSOR IN CONJUNCTION WITH NEURAL NETWORK PATTERN RECOGNITION


title: Ontology-based Interpretation of Keywords for Semantic Search

abstract: Current information retrieval (IR) approaches do not formally capture the explicit meaning of a keyword query but provide a comfortable way for the user to specify information needs on the basis of keywords. Ontology-based approaches allow for sophisticated semantic search but impose a query syntax more difficult to handle. In this paper, we present an approach for translating keyword queries to DL conjunctive queries using background knowledge available in ontologies. We present an implementation which shows that this interpretation of keywords can then be used for both exploration of asserted knowledge and for a semantics-based declarative query answering process. We also present an evaluation of our system and a discussion of the limitations of the approach with respect to our underlying assumptions which directly points to issues for future work.


title: Multi-Instance Visual-Semantic Embedding

abstract: Visual-semantic embedding models have been recently proposed and shown to be effective for image classification and zero-shot learning, by mapping images into a continuous semantic label space. Although several approaches have been proposed for single-label embedding tasks, handling images with multiple labels (which is a more general setting) still remains an open problem, mainly due to the complex underlying corresponding relationship between image and its labels. In this work, we present Multi-Instance visual-semantic Embedding model (MIE) for embedding images associated with either single or multiple labels. Our model discovers and maps semantically-meaningful image subregions to their corresponding labels. And we demonstrate the superiority of our method over the state-of-the-art on two tasks, including multi-label image annotation and zero-shot learning.


title: A Fully Coreference-annotated Corpus of Scholarly Papers from the ACL Anthology

abstract: We describe a large coreference annotation task performed on a corpus of 266 papers from the ACL Anthology, a publicly, electronically available collection of scientific papers in the domain of computational linguistics and language technology. The annotation comprises mainly noun phrase coreference of the full textual content of each paper in the Anthology subset. It has been performed carefully and at least twice for each paper (initial annotation and secondary correction phase). The purpose of this paper is to summarize the comprehensive annotation schema and release the corpus publicly, along with this paper. The corpus is by far larger than the ACE coreference corpora. It can be used to train coreference resolution systems in the Computational Linguistics and Language Technology domain for semantic search, taxonomy extraction, question answering, citation analysis, scientific discourse analysis, etc.


title: Graph-Sparse LDA: A Topic Model with Structured Sparsity

abstract: Topic modeling is a powerful tool for uncovering latent structure in many domains, including medicine, finance, and vision. The goals for the model vary depending on the application: sometimes the discovered topics are used for prediction or another downstream task. In other cases, the content of the topic may be of intrinsic scientific interest. Unfortunately, even when one uses modern sparse techniques, discovered topics are often difficult to interpret due to the high dimensionality of the underlying space. To improve topic interpretability, we introduce Graph-Sparse LDA, a hierarchical topic model that uses knowledge of relationships between words (e.g., as encoded by an ontology). In our model, topics are summarized by a few latent concept-words from the underlying graph that explain the observed words. Graph-Sparse LDA recovers sparse, interpretable summaries on two real-world biomedical datasets while matching state-of-the-art prediction performance.


title: GoQBot: a caterpillar-inspired soft-bodied rolling robot

abstract: Rolling locomotion using an external force such as gravity has evolved many times. However, some caterpillars can curl into a wheel and generate their own rolling momentum as part of an escape repertoire. This change in body conformation occurs well within 100 ms and generates a linear velocity over 0.2 m s−1, making it one of the fastest self-propelled wheeling behaviors in nature. Inspired by this behavior, we construct a soft-bodied robot to explore the dynamics and control issues of ballistic rolling. This robot, called GoQBot, closely mimics caterpillar rolling. Analyzing the whole body kinematics and 2D ground reaction forces at the robot ground anchor reveals about 1G of acceleration and more than 200 rpm of angular velocity. As a novel rolling robot, GoQBot demonstrates how morphing can produce new modes of locomotion. Furthermore, mechanical coupling of the actuators improves body coordination without sensory feedback. Such coupling is intrinsic to soft-bodied animals because there are no joints to isolate muscle-generated movements. Finally, GoQBot provides an estimate of the mechanical power for caterpillar rolling that is comparable to that of a locust jump. How caterpillar musculature produces such power in such a short time is yet to be discovered.


title: Artificial Intelligence Technique Applied to Intrusion Detection

abstract: Communication network is facilitated with different protocol .Each protocol supported to increase the network performance in a secured manner. In communication process, user’s connectivity, violations of policy on access of information are handles through intrusion. Intrusion prevention is the process of performing intrusion detection and attempting to stop detected possible incidents. It focused on identifying possible incidents, logging information about them, attempting to stop them, and reporting them to security administrators. However, organizations use Intrusion detection and prevention system (IDPS) for other purposes, such as identifying problems with security policies, documenting existing threats, and determining individuals from violating security policies. Communication architecture is built up on IP.Internet Control Message Protocol (ICMP ) protocol is tightly integrated with IP. ICMP messages, delivered in IP packets, are used for out-of-band messages related to network operation or mis-operation.In this paper describes about HDLC , ICMP protocol sequences which is used to detect the intrusion on hybrid network and its attributes are recommend the standardized protocol for the intrusion detection process. This standardization protocol compare the previous part of the research of HDLC and ICMP protocols.


title: Named Entity Tagging a Very Large Unbalanced Corpus: Training and Evaluating NE Classifiers

abstract: We describe a systematic and application-oriented approach to training and evaluating named entity recognition and classification (NERC) systems, the purpose of which is to identify an optimal system and to train an optimal model for named entity tagging DeReKo, a very large general-purpose corpus of contemporary German (Kupietz et al., 2010). DeReKo 's strong dispersion wrt. genre, register and time forces us to base our decision for a specific NERC system on an evaluation performed on a representative sample of DeReKo instead of performance figures that have been reported for the individual NERC systems when evaluated on more uniform and less diverse data. We create and manually annotate such a representative sample as evaluation data for three different NERC systems, for each of which various models are learnt on multiple training data. The proposed sampling method can be viewed as a generally applicable method for sampling evaluation data from an unbalanced target corpus for any sort of natural language processing.


FRPAs and High-Gain Directional Antennas


title: THE DUAL CONTROL MODEL: THE ROLE OF SEXUAL INHIBITION & EXCITATION IN SEXUAL AROUSAL AND BEHAVIOR

abstract: Psychophysiological studies of sexuality have largely, if not completely, ignored issues of individual variability in responsiveness. Most of the research published in this area involves the comparison of subject groups, experimental conditions, or treatments. The few attempts to evaluate individual variability have been restricted to the measurement of sexual attitudes and behavioral tendencies. Erotophobia-erotophilia is a well-known example (Fisher, Byrne, White, & Kelley, 1988). This construct is measured with the Sexual Opinion Survey (SOS), a questionnaire that assesses affective and evaluative (attitudinal) responses to different types of sexual activity or stimuli. Until recently, no models or measures existed that focus specifically on individual differences in sexual response. The Kinsey Institute’s “Dual Control Model” postulates that sexual arousal and associated behaviors depend on the balance between sexual excitation and inhibition. It is an example of a state-trait model, although most research so far has focused on the trait dimension of the model. The model proposes that the weighing of excitatory and inhibitory processes determines whether or not a sexual response occurs within an individual in a given situation, and at the same time it assumes individual variability in the propensity for these processes. The model was developed in an attempt to synthesize existing research findings in the area of male sexual dysfunction, to contribute to this research by underscoring the relevance of exploring individual differences, and to stimulate new research, in this and other areas of sexual response and behavior. In this paper, we will present the model’s background, discuss findings from several lines of research, reflect on the model’s strengths and weaknesses, and consider directions for future research. Background


title: Modeling shape and textural variations in aging faces

abstract: We propose a two fold approach towards modeling facial aging in adults. Firstly, we develop a shape transformation model that is formulated as a physically-based parametric muscle model that captures the subtle deformations facial features undergo with age. The model implicitly accounts for the physical properties and geometric orientations of the individual facial muscles. Next, we develop an image gradient based texture transformation function that characterizes facial wrinkles and other skin artifacts often observed during different ages. Facial growth statistics (both in terms of shape and texture) play a crucial role in developing the aforementioned transformation models. From a database that comprises of pairs of age separated face images of many individuals, we extract age-based facial measurements across key fiducial features and further, study textural variations across ages. We present experimental results that illustrate the applications of the proposed facial aging model in tasks such as face recognition and facial appearance prediction across aging.


title: A practical guide to performing multiple-point statistical simulations with the Direct Sampling algorithm

abstract: The Direct Sampling (DS) algorithm is a recently developed multiple-point statistical simulation technique. It directly scans the training image (TI) for a given data event instead of storing the training probability values in a catalogue prior to simulation. By using distances between the given data events and the TI patterns, DS allows to simulate categorical, continuous and multivariate problems. Benefiting from the wide spectrum of potential applications of DS, requires understanding of the user-defined input parameters. Therefore, we list the most important parameters and assess their impact on the generated simulations. Real case TIs are used, including an image of ice-wedge polygons, a marble slice and snow crystals, all three as continuous and categorical images. We also use a 3D categorical TI representing a block of concrete to demonstrate the capacity of DS to generate 3D simulations. First, a quantitative sensitivity analysis is conducted on the three parameters balancing simulation quality and CPU time: the acceptance threshold t, the fraction of TI to scan f and the number of neighbors n. Next to a visual inspection of the generated simulations, the performance is analyzed in terms of speed of calculation and quality of pattern reproduction. Whereas decreasing the CPU time by influencing t and n is at the expense of simulation quality, reducing the scanned fraction of the TI allows substantial computational gains without degrading the quality as long as the TI contains enough reproducible patterns. We also illustrate the quality improvement resulting from post-processing and the potential of DS to simulate bivariate problems and to honor conditioning data. We report a comprehensive guide to performing multiple-point statistical simulations with the DS algorithm and provide recommendations on how to set the input parameters appropriately.


Running Head : GENDER AND SELF-DISCLOSURE ON FACEBOOK 1 “ I ’ m an Open ( Face ) Book ” : Gender and Self-Disclosure on Facebook


title: Convolutional Neural Network-Based Block Up-Sampling for Intra Frame Coding

abstract: Inspired by the recent advances of image super-resolution using convolutional neural network (CNN), we propose a CNN-based block up-sampling scheme for intra frame coding. A block can be down-sampled before being compressed by normal intra coding, and then up-sampled to its original resolution. Different from previous studies on down/up-sampling-based coding, the up-sampling methods in our scheme have been designed by training CNN instead of hand-crafted. We explore a new CNN structure for up-sampling, which features deconvolution of feature maps, multi-scale fusion, and residue learning, making the network both compact and efficient. We also design different networks for the up-sampling of luma and chroma components, respectively, where the chroma up-sampling CNN utilizes the luma information to boost its performance. In addition, we design a two-stage up-sampling process, the first stage being within the block-by-block coding loop, and the second stage being performed on the entire frame, so as to refine block boundaries. We also empirically study how to set the coding parameters of down-sampled blocks for pursuing the frame-level rate-distortion optimization. Our proposed scheme is implemented into the high-efficiency video coding (HEVC) reference software, and a comprehensive set of experiments have been performed to evaluate our methods. Experimental results show that our scheme achieves significant bits saving compared with the HEVC anchor, especially at low bit rates, leading to on average 5.5% BD-rate reduction on common test sequences and on average 9.0% BD-rate reduction on ultrahigh definition test sequences.


Fitting in or opting out: A review of key social-psychological factors influencing a sense of belonging for women in physics


title: Text-based Adventures of the Golovin AI Agent

abstract: The domain of text-based adventure games has been recently established as a new challenge of creating the agent that is both able to understand natural language, and acts intelligently in text-described environments. In this paper, we present our approach to tackle the problem. Our agent, named Golovin, takes advantage of the limited game domain. We use genre-related corpora (including fantasy books and decompiled games) to create language models suitable to this domain. Moreover, we embed mechanisms that allow us to specify, and separately handle, important tasks as fighting opponents, managing inventory, and navigating on the game map. We validated usefulness of these mechanisms, measuring agent's performance on the set of 50 interactive fiction games. Finally, we show that our agent plays on a level comparable to the winner of the last year Text-Based Adventure AI Competition.


title: Autoencoder Feature Selector

abstract: High-dimensional data in many areas such as computer vision and machine learning brings in computational and analytical difficulty. Feature selection which select a subset of features from original ones has been proven to be effective and efficient to deal with high-dimensional data. In this paper, we propose a novel AutoEncoder Feature Selector (AEFS) for unsupervised feature selection. AEFS is based on the autoencoder and the group lasso regularization. Compared to traditional feature selection methods, AEFS can select the most important features in spite of nonlinear and complex correlation among features. It can be viewed as a nonlinear extension of the linear method regularized self-representation (RSR) for unsupervised feature selection. In order to deal with noise and corruption, we also propose robust AEFS. An efficient iterative algorithm is designed for model optimization and experimental results verify the effectiveness and superiority of the proposed method.


of the Thesis Classification of Imbalanced Data Using Synthetic OverSampling Techniques by


title: Screening Twitter Users for Depression and PTSD with Lexical Decision Lists

abstract: This paper describes various systems from the University of Minnesota, Duluth that participated in the CLPsych 2015 shared task. These systems learned decision lists based on lexical features found in training data. These systems typically had average precision in the range of .70 ‐ .76, whereas a random baseline attained .47 ‐ .49.


title: Challenge Benchmarks That Must be Conquered to Sustain the GPU Revolution

abstract: The shift from GPUs to GPGPUs has brought with it many changes to the GPU architecture (e.g. more caches, more concurrent kernels, better synchronization). As GPUs press further into the general-purpose domain, architects must continue to address the performance of challenging workloads. This paper presents a set of challenge benchmarks and their key performance limitations to help direct future GPU architecture research. Our study shows GPUs must develop multiple innovative architectural techniques to efficiently execute these applications to continue making inroads into general purpose computing.


title: A Case of Acute Q Fever Hepatitis Diagnosed by F-18 FDG PET/CT

abstract: A 53-year-old man with fever of unknown origin underwent F-18 fluorodeoxyglucose positron emission tomography/computed tomography (F-18 FDG PET/CT) as a workup for a fever of unknown origin. On presentation, he complained of fever, chills, and myalgia. The F-18 FDG PET/CT scan showed diffusely increased uptake of the liver with mild hepatomegaly. A liver biopsy then revealed fibrin-ring granulomas typically seen in Q fever. The patient was later serologically diagnosed as having acute Q fever as the titers for C. burnetii IgM and IgG were 64:1 and 16:1, respectively. He recovered completely following administration of doxycycline. This indicates that F-18 FDG PET/CT may be helpful for identifying hepatic involvement in Q fever as a cause of fever of unknown origin.


title: Vacuum-deposited selective-absorber coatings for solar receivers

abstract: Vacuum deposition methods and coatings which are candidates for solar thermal receivers are discussed. Also, an economic analysis of coating costs for larger production volumes is presented.


title: Invading the Mediterranean Sea: biodiversity patterns shaped by human activities

abstract: Human activities, such as shipping, aquaculture, and the opening of the Suez Canal, have led to the introduction of nearly 1,000 alien species into the Mediterranean Sea. We investigated how human activities, by providing pathways for the introduction of alien species, may shape the biodiversity patterns in the Mediterranean Sea. Richness of Red Sea species introduced through the Suez Canal (Lessepsian species) is very high along the eastern Mediterranean coastline, reaching a maximum of 129 species per 100 km2, and declines towards the north and west. The distribution of species introduced by shipping is strikingly different, with several hotspot areas occurring throughout the Mediterranean basin. Two main hotspots for aquaculture-introduced species are observed (the Thau and Venice lagoons). Certain taxonomic groups were mostly introduced through specific pathways – fish through the Suez Canal, macrophytes by aquaculture, and invertebrates through the Suez Canal and by shipping. Hence, the local taxonomic identity of the alien species was greatly dependent on the dominant maritime activities/interventions and the related pathways of introduction. The composition of alien species differs among Mediterranean ecoregions; such differences are greater for Lessepsian and aquaculture-introduced species. The spatial pattern of native species biodiversity differs from that of alien species: the overall richness of native species declines from the north-western to the south-eastern regions, while the opposite trend is observed for alien species. The biodiversity of the Mediterranean Sea is changing, and further research is needed to better understand how the new biodiversity patterns shaped by human activities will affect the Mediterranean food webs, ecosystem functioning, and the provision of ecosystem services.


Influence of Fluoride Ions Contamination in Front Opening Unified Pod (FOUP) Generating Defective Bonding Pad


title: Improving Distributed Gradient Descent Using Reed-Solomon Codes

abstract: Today's massively-sized datasets have made it necessary to often perform computations on them in a distributed manner. In principle, a computational task is divided into subtasks which are distributed over a cluster operated by a taskmaster. One issue faced in practice is the delay incurred due to the presence of slow machines, known as \emph{stragglers}. Several schemes, including those based on replication, have been proposed in the literature to mitigate the effects of stragglers and more recently, those inspired by coding theory have begun to gain traction. In this work, we consider a distributed gradient descent setting suitable for a wide class of machine learning problems. We adapt the framework of Tandon et al. (arXiv:1612.03301) and present a deterministic scheme that, for a prescribed per-machine computational effort, recovers the gradient from the least number of machines $f$ theoretically permissible, via an $O(f^2)$ decoding algorithm. We also provide a theoretical delay model which can be used to minimize the expected waiting time per computation by optimally choosing the parameters of the scheme. Finally, we supplement our theoretical findings with numerical results that demonstrate the efficacy of the method and its advantages over competing schemes.


title: Human resource management and performance: A neural network analysis

abstract: Abstract This study utilizes an innovative research methodology (Kohonen’s Self-Organizing Maps) to explore a subject relatively understudied in Europe. It focuses on the connection between human resource management as a source of competitive advantage and perceived organizational performance in the European Union’s private and public sectors. While practices in these two sectors did not differ significantly, three diverse but overlapping HRM models did emerge, each of which involved a different set of EU member states. Training & Development practices were strongly related to performance in all three models and Communication practices in two. These results show the usefulness of an innovative technique when applied to research so far conducted through traditional methodologies, and brings to the surface questions about the universal applicability of the widely accepted relationship between superior HRM and superior business performance.


title: Uptake of chloride ion from aqueous solution by calcined layered double hydroxides: equilibrium and kinetic studies.

abstract: Layered double hydroxides (LDH) calcined within a certain temperature range (denoted as CLDH) have been shown to recover their original layered structure in the presence of appropriate anions. In the light of this so-called "memory effect", uptake of chloride ion from aqueous solution by calcined MgAl-CO3 LDH was investigated in batch mode. The equilibrium isotherm showed that the uptake of chloride ion by CLDH was consistent with the Langmuir and Freundlich equations and that the Langmuir model gave a better fit to the experimental data than the Freundlich model. The maximum uptake capacity of CLDH for chloride ion was 149.5 mg/g, close to the stoichiometric uptake (168 mg/g). The influence of varying pH of solution, initial chloride concentration, adsorbent quantity, and temperature on the kinetics of chloride removal has also been explored. Four kinetic models were used to fit the experimental data, and it was found that the pseudo-second-order kinetics model could be used to describe the uptake process satisfactorily. The calculated value of Ea was found to be 56.8 kJ/mol, which suggests that the process of uptake of chloride ion is controlled by the rate of reaction of chloride ion with the CLDH rather than diffusion. A mechanism for removal of chloride ion has been confirmed by X-ray diffraction, FT-IR spectroscopy and TG-MS measurements.


title: An empirical evaluation of some articulatory and cognitive aspects of marking menus

abstract: We describe marking menus, an extension of pie menus, which are well suited for stylus-based interfaces. Pie menus are circular menus subdivided into sectors, each of which might correspond to a different command. One moves the cursor from the center of the pie into the desired sector. Marking menus are invisible pie menus in which the movement of the cursor during a selection leaves an "ink trail" similar to a pen stroke on paper. The combination of a pie menu and a marking menu supports an efficient transition from novice to expert performance. Novices can "pop-up" a pie menu and make a selection, whereas experts can simply make the corresponding mark without waiting for the menu to appear. ::: ::: This article describes an experiment in which we explored both articulatory and cognitive aspects of marking menus for different numbers of items per menu and using different input devices (mouse, trackball, and stylus). The articulatory aspects are how well subjects could execute the physical actions necessary to select from pie marking menus. Articulatory aspects were investigated by presenting one group of subjects with the task of selecting from fully visible menus. Because one feature of marking menus is that users should be able to select from them without seeing the menus (by making a mark), we also ran two groups of subjects with invisible pie menus: one group with an ink trail and one without. These subjects were therefore faced with the task of either mentally representing the menu or associating marks with the commands they invoked through practice. These then are the cognitive aspects to which we refer. Our results indicate that subjects' performance degraded as the number of items increased. When menus were hidden, however, subjects performance did not degrade as rapidly when menus contained even numbers of items. We also found subjects performed better with the mouse and stylus than with the trackball.


title: Customer Satisfaction Analysis

abstract: The objective of this master thesis is to identify “key-drivers” embedded in customer satisfaction data. The data was collected by a large transportation sector corporation during five years and in ...


title: SMART IRRIGATION SYSTEM THROUGH WIRELESS SENSOR NETWORKS

abstract: Irrigation is one of the most powerful sources in India but it is hard for an individual person to monitor continuously and regularly. This is due to laziness of mankind. In order to make this irrigation easier our system comprises some changes in the usual irrigation system. The newly developed project controls water supply automatically in water crisis areas through moisture sensor. This paper covers the application of Sensor based Irrigation system through wireless sensor networks, which uses a renewable energy as a source. In this system Wireless Sensor Networks Plays a major role in Environment monitoring system and provides unmanned irrigation. WSN consists of moisture sensors, Energy harvesting systems, embedded controllers and uses Super capacitors as storage device.


title: A Practical Method for Modeling PCB Transmission Lines with Conductor Surface Roughness and Wideband Dielectric Properties

abstract: This paper discusses the modeling techniques to account for transmission line high frequency effects, and proposes a method to integrate these techniques into a practical and design-worthy model generation flow. The frequency tabulated transmission line models are capable of predicting wideband dielectric characteristics and high frequency conductor losses due to skin effect and surface roughness. They can be used to accurately model high volume, low cost printed circuit board traces which often have roughened trace surfaces and pronounced frequency variation in dielectric properties. The model accuracy is verified up to 20 GHz in frequency domain, thus suitable for Multi-Gigabit signaling analysis.


title: On Pruning Techniques for Multi-Player Games

abstract: Max n (Luckhardt and Irani, 1986) is the extension of the minimax backup rule to multi-player games. We have shown that only a limited version of alpha-beta pruning, shallow pruning, can be applied to a max n search tree. We extend this work by calculating the exact bounds needed to use this pruning technique. In addition, we show that branch-and-bound pruning, using a monotonic heuristic, has the same limitations as alpha-beta pruning in a max n tree. We present a hybrid of these algorithms, alpha-beta branch-and-bound pruning, which combines a monotonic heuristic and backed-up values to prune even more effectively. We also briefly discuss the reduction of a n-player game to a ‘paranoid’ 2-player game. In Sergeant Major, a 3-player card game, we averaged node expansions over 200 height 15 trees. Shallow pruning and branch-and-bound each reduced node expansions by a factor of about 100. Alpha-beta branch-and-bound reduced the expansions by an additional factor of 19. The 2-player reduction was a factor of 3 better than alpha-beta branchand-bound. Using heuristic bounds in the 2-player reduction reduced node expansions another factor of 12.


title: Essential Dimensions of Latent Semantic Indexing (LSI)

abstract: Latent semantic indexing (LSI) is commonly used to match queries to documents in information retrieval applications. LSI has been shown to improve retrieval performance for some, but not all, collections, when compared to traditional vector space retrieval. In this paper, we first develop a model for understanding which values in the reduced dimensional space contain the term relationship (latent semantic) information. We then test this model by developing a modified version of LSI that captures this information, essential dimensions of LSI (EDLSI). EDLSI significantly improves retrieval performance on corpora that previously did not benefit from LSI, and offers improved runtime performance when compared with traditional LSI. Traditional LSI requires the use of a dimensionality reduction parameter which must be tuned for each collection. Applying our model, we have also shown that a small, fixed dimensionality reduction parameter (k=10) can be used to capture the term relationship information in a corpus


title: Ameloblastic fibroma: A rare case appearing as a mixed radiographic image

abstract: Ameloblastic fibroma (AF) is a benign tumor of mixed odontogenic origin, which affects predominantly young individuals. AF appearing as a mixed radiographic image is very rare. This report describes a case of AF in a 12-year-old male identified during a routine radiographic exam for orthodontic treatment planning. The panoramic radiography revealed a well-defined multilocular mixed image located in the mandible between the roots of the left mandibular second premolar and first molar. The lesion was excised under local anesthesia. Histopathological analysis revealed islands of epithelial cells and columnar peripheral cells showing a nucleus in inverted polarization, interspersed with spindle-shaped cells and abundant extracellular matrix deposition. No atypia was observed. The diagnosis of AF was established. No tumor recurred up to 30 months after treatment. Although rare, AF should be also considered in the differential diagnosis of mixed radiographic images of the jaws in young patients. ::: ::: Key words:Ameloblastic fibroma, differential diagnosis, incidental finding, mixed image, radiographic features.


title: Minimizing air consumption of pneumatic actuators in mobile robots

abstract: This paper introduces a new control method for pneumatic actuators, called "Proportional Position and Stiffness (PPS)" controller. The PPS method provides both position and stiffness control for a robot joint driven by a pneumatic cylinder with four ON-OFF valves. In addition, the proposed control system consumes much less compressed air than comparable strategies. These features make the PPS method highly suitable to application in mobile robots.


title: Answer Set Programming for Procedural Content Generation: A Design Space Approach

abstract: Procedural content generators for games produce artifacts from a latent design space. This space is often only implicitly defined, an emergent result of the procedures used in the generator. In this paper, we outline an approach to content generation that centers on explicit description of the design space, using domain-independent procedures to produce artifacts from the described space. By concisely capturing a design space as an answer set program, we can rapidly define and expressively sculpt new generators for a variety of game content domains. We walk through the reimplementation of a reference evolutionary content generator in a tutorial example, and review existing applications of answer set programming to generative-content design problems in and outside of a game context.


title: Social motivation and implicit theory of mind in children with autism spectrum disorder

abstract: According to the social motivation theory of autism, children who develop Autism Spectrum Disorder (ASD) have early deficits in social motivation, which is expressed by decreased attention to social information. These deficits are said to lead to impaired socio-cognitive development, such as theory of mind (ToM). There is little research focused on the relation between social motivation and ToM in this population. The goal of the present study was to investigate the link between one aspect of social motivation, social orienting, and ToM in preschoolers with ASD. It was expected that, in contrast to typically developing (TD) children, children with ASD would show impaired performance on tasks measuring social orienting and ToM. It was also expected that children's performance on the social orienting tasks would be correlated with their performance on the ToM task. A total of 17 children with ASD and 16 TD children participated in this study. Participants completed two social orienting tasks, a face preference task and a biological motion preference task, as well an implicit false belief task. Results reveal that TD children, but not children with ASD, exhibited social preference as measured by a preference for faces and biological motion. Furthermore, children with ASD tended to perform worse on the ToM task compared to their TD counterparts. Performance on the social motivation tasks and the ToM task tended to be related but only for the TD children. These findings suggest that ToM is multifaceted and that motivational deficits might have downstream effects even on implicit ToM. Autism Res 2017, 10: 1834–1844. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. ::: ::: Lay Summary ::: The goal of the present study was to examine the link between poor attention to social information and mindreading abilities in children with autism spectrum disorder (ASD). Results demonstrated that children with ASD tended to perform worse than neurotypical children on both social orienting and theory of mind tasks. Preference for human faces and motion tended to be related but only for the neurotypical children. These findings provide partial support for the social motivation theory.


title: Robust Face Recognition with Deep Multi-View Representation Learning

abstract: This paper describes our proposed method targeting at the MSR Image Recognition Challenge MS-Celeb-1M. The challenge is to recognize one million celebrities from their face images captured in the real world. The challenge provides a large scale dataset crawled from the Web, which contains a large number of celebrities with many images for each subject. Given a new testing image, the challenge requires an identify for the image and the corresponding confidence score. To complete the challenge, we propose a two-stage approach consisting of data cleaning and multi-view deep representation learning. The data cleaning can effectively reduce the noise level of training data and thus improves the performance of deep learning based face recognition models. The multi-view representation learning enables the learned face representations to be more specific and discriminative. Thus the difficulties of recognizing faces out of a huge number of subjects are substantially relieved. Our proposed method achieves a coverage of 46.1% at 95% precision on the random set and a coverage of 33.0% at 95% precision on the hard set of this challenge.


title: Consistently faster and smaller compressed bitmaps with Roaring

abstract: Compressed bitmap indexes are used in databases and search engines. Many bitmap compression techniques have been proposed, almost all relying primarily on run-length encoding (RLE). However, on unsorted data, we can get superior performance with a hybrid compression technique that uses both uncompressed bitmaps and packed arrays inside a two-level tree. An instance of this technique, Roaring, has recently been proposed. Due to its good performance, it has been adopted by several production platforms (e.g., Apache Lucene, Apache Spark, Apache Kylin and Druid). ::: Yet there are cases where run-length encoded bitmaps are smaller than the original Roaring bitmaps---typically when the data is sorted so that the bitmaps contain long compressible runs. To better handle these cases, we build a new Roaring hybrid that combines uncompressed bitmaps, packed arrays and RLE compressed segments. The result is a new Roaring format that compresses better. ::: Overall, our new implementation of Roaring can be several times faster (up to two orders of magnitude) than the implementations of traditional RLE-based alternatives (WAH, Concise, EWAH) while compressing better. We review the design choices and optimizations that make these good results possible.


title: A short amino acid sequence able to specify nuclear location

abstract: Abstract A short sequence of amino acids including Lys-128 is required for the normal nuclear accumulation of wild-type and deleted forms of SV40 large T antigen. A cytoplasmic large T mutant that lacks sequences from around Lys-128 localizes to the nucleus if the missing sequence is attached to its amino terminus. The implication that the sequence element around Lys-128 acts as an autonomous signal capable of specifying nuclear location was tested directly by transferring it to the amino termini of β-galactosidase and of pyruvate kinase, normally a cytoplasmic protein. Sequences that included the putative signal induced each of the fusion proteins to accumulate completely in the nucleus but had no discernible effect when Lys-128 was replaced by Thr. By reducing the size of the transposed sequence we conclude that Pro-Lys-Lys-Lys-Arg-Lys-Val can act as a nuclear location signal. The sequence may represent a prototype of similar sequences in other nuclear proteins.


title: SBA: A software package for generic sparse bundle adjustment

abstract: Bundle adjustment constitutes a large, nonlinear least-squares problem that is often solved as the last step of feature-based structure and motion estimation computer vision algorithms to obtain optimal estimates. Due to the very large number of parameters involved, a general purpose least-squares algorithm incurs high computational and memory storage costs when applied to bundle adjustment. Fortunately, the lack of interaction among certain subgroups of parameters results in the corresponding Jacobian being sparse, a fact that can be exploited to achieve considerable computational savings. This article presents sba, a publicly available C/C++ software package for realizing generic bundle adjustment with high efficiency and flexibility regarding parameterization.


title: Fake News vs Satire: A Dataset and Analysis

abstract: Fake news has become a major societal issue and a technical chal- lenge for social media companies to identify. This content is dif- cult to identify because the term "fake news" covers intention- ally false, deceptive stories as well as factual errors, satire, and sometimes, stories that a person just does not like. Addressing the problem requires clear de nitions and examples. In this work, we present a dataset of fake news and satire stories that are hand coded, veri ed, and, in the case of fake news, include rebutting stories. We also include a thematic content analysis of the articles, identifying major themes that include hyperbolic support or con- demnation of a gure, conspiracy theories, racist themes, and dis- crediting of reliable sources. In addition to releasing this dataset for research use, we analyze it and show results based on language that are promising for classi cation purposes. Overall, our contri- bution of a dataset and initial analysis are designed to support fu- ture work by fake news researchers.


title: Multi-Reward Reinforced Summarization with Saliency and Entailment

abstract: Abstractive text summarization is the task of compressing and rewriting a long document into a short summary while maintaining saliency, directed logical entailment, and non-redundancy. In this work, we address these three important aspects of a good summary via a reinforcement learning approach with two novel reward functions: ROUGESal and Entail, on top of a coverage-based baseline. The ROUGESal reward modifies the ROUGE metric by up-weighting the salient phrases/words detected via a keyphrase classifier. The Entail reward gives high (length-normalized) scores to logically-entailed summaries using an entailment classifier. Further, we show superior performance improvement when these rewards are combined with traditional metric (ROUGE) based rewards, via our novel and effective multi-reward approach of optimizing multiple rewards simultaneously in alternate mini-batches. Our method achieves the new state-of-the-art results on CNN/Daily Mail dataset as well as strong improvements in a test-only transfer setup on DUC-2002.


title: The Effects of Background Music on Primary School Pupils' Task Performance

abstract: Research on the effects of background music has a long history. Early work was not embedded within a theoretical framework, was often poorly conceptualised and produced equivocal findings. This paper reports two studies exploring the effects of music, perceived to be calming and relaxing, on performance in arithmetic and on a memory task in children aged 10-12. The calming music led to better performance on both tasks when compared with a no-music condition. Music perceived as arousing, aggressive and unpleasant disrupted performance on the memory task and led to a lower level of reported altruistic behaviour by the children. This suggests that the effects of music on task performance are mediated by arousal and mood rather than affecting cognition directly. The findings are discussed in relation to possible practical applications in the primary school and the home.


title: Hierarchical Feature Extraction With Local Neural Response for Image Recognition

abstract: In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.


title: Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream

abstract: Topic models have proven to be a useful tool for discovering latent structures in document collections. However, most document collections often come as temporal streams and thus several aspects of the latent structure such as the number of topics, the topics' distribution and popularity are time-evolving. Several models exist that model the evolution of some but not all of the above aspects. In this paper we introduce infinite dynamic topic models, iDTM, that can accommodate the evolution of all the aforementioned aspects. Our model assumes that documents are organized into epochs, where the documents within each epoch are exchangeable but the order between the documents is maintained across epochs. iDTM allows for unbounded number of topics: topics can die or be born at any epoch, and the representation of each topic can evolve according to a Markovian dynamics. We use iDTM to analyze the birth and evolution of topics in the NIPS community and evaluated the efficacy of our model on both simulated and real datasets with favorable outcome.


title: Glucocorticoid Signaling Defines a Novel Commitment State during Adipogenesis In Vitro

abstract: Differentiation of 3T3-L1 preadipocytes can be induced by a 2-d treatment with a factor “cocktail” (DIM) containing the synthetic glucocorticoid dexamethasone (dex), insulin, the phosphodiesterase inhibitor methylisobutylxanthine (IBMX) and fetal bovine serum (FBS). We temporally uncoupled the activities of the four DIM components and found that treatment with dex for 48 h followed by IBMX treatment for 48 h was sufficient for adipogenesis, whereas treatment with IBMX followed by dex failed to induce significant differentiation. Similar results were obtained with C3H10T1/2 and primary mesenchymal stem cells. The 3T3-L1 adipocytes differentiated by sequential treatment with dex and IBMX displayed insulin sensitivity equivalent to DIM adipocytes, but had lower sensitivity to ISO-stimulated lipolysis and reduced triglyceride content. The nondifferentiating IBMX–then-dex treatment produced transient expression of adipogenic transcriptional regulatory factors C/EBPβ and C/EBPδ, and little induction of terminal differentiation factors C/EBPα and PPARγ. Moreover, the adipogenesis inhibitor preadipocyte factor-1 (Pref-1) was repressed by DIM or by dex-then-IBMX, but not by IBMX-then-dex treatment. We conclude that glucocorticoids drive preadipocytes to a novel intermediate cellular state, the dex-primed preadipocyte, during adipogenesis in cell culture, and that Pref-1 repression may be a cell fate determinant in preadipocytes.


title: Submitted for publication. Multi-domain Modeling of Cyber-Physical Systems Using Architectural Views

abstract: Designing cyber-physical systems (CPSs) increasingly requires the use of multi-domain models throughout the development process. Ensuring consistent relationships between various system models is an important part of an integrated design methodology. This paper describes an architectural approach to reasoning about relations between heterogeneous system models. The run-time base architecture of the system is used as a unifying representation to compare the structure and semantics of the associated models. Each model is related to the base architecture through the abstraction of an architectural view, which captures structural and semantic correspondences between model elements and system entities. The use of the architectural view framework to relate system models from different domains is illustrated in the context of a quadrotor air vehicle. Keywords-architectural views; multi-domain modeling; cyberphysical systems; component-connector;


title: PhaseMax: Convex Phase Retrieval via Basis Pursuit

abstract: We consider the recovery of a (real- or complex-valued) signal from magnitude-only measurements, known as phase retrieval. We formulate phase retrieval as a convex optimization problem, which we call PhaseMax. Unlike other convex methods that use semidefinite relaxation and lift the phase retrieval problem to a higher dimension, PhaseMax is a “non-lifting” relaxation that operates in the original signal dimension. We show that the dual problem to PhaseMax is basis pursuit, which implies that the phase retrieval can be performed using algorithms initially designed for sparse signal recovery. We develop sharp lower bounds on the success probability of PhaseMax for a broad range of random measurement ensembles, and we analyze the impact of measurement noise on the solution accuracy. We use numerical results to demonstrate the accuracy of our recovery guarantees, and we showcase the efficacy and limits of PhaseMax in practice.


title: Recurrent Fully Convolutional Networks for Video Segmentation

abstract: Image segmentation is an important step in most visual tasks. While convolutional neural networks have shown to perform well on single image segmentation, to our knowledge, no study has been been done on leveraging recurrent gated architectures for video segmentation. Accordingly, we propose a novel method for online segmentation of video sequences that incorporates temporal data. The network is built from fully convolutional element and recurrent unit that works on a sliding window over the temporal data. We also introduce a novel convolutional gated recurrent unit that preserves the spatial information and reduces the parameters learned. Our method has the advantage that it can work in an online fashion instead of operating over the whole input batch of video frames. The network is tested on the change detection dataset, and proved to have 5.5\% improvement in F-measure over a plain fully convolutional network for per frame segmentation. It was also shown to have improvement of 1.4\% for the F-measure compared to our baseline network that we call FCN 12s.


title: The wear out effect of a game-based student response system

abstract: The Bring Your Own Device (BYOD) wave and advancement in technical infrastructures and in learning technology opens for new ways of teaching in the classroom. The teachers' laptops connected to a video projector, access to wireless network and the students smartphones, tablets or laptops can be utilized to enhance the interaction between the teacher and students, as well as boost the students motivation, engagement and learning. The introduction of new learning technology in the classroom normally results in immediate enthusiasm and excitement both from the teacher and the students. However, the immediate positive effects might fade when the new learning technology has become familiar to the teacher and the students. This paper shows the results from investigating the wear off effect of using the game-based student response system Kahoot! in classroom teaching. More specifically, it compares the results from students using Kahoot! for the first time in a single motivational lecture vs. using Kahoot! in every lecture in a class for five months. The quasi-experiment focused on how the students' perception changed in relation to user-friendliness, engagement, motivation, classroom dynamics, concentration, and perceived learning. The results show a slight reduction in the students motivation and engagement, but the only statistically significant wear out effect found was related to classroom dynamics. At large, the game-based student response system managed to boost students engagement, motivation and learning after using it repeatedly for five months. The core factor to keep the students attention after heavy repeated usage was found to be the competitive nature of Kahoot!. We examine short time vs. long time effect of a game-based student response system.The effects studied were engagement, motivation, and classroom dynamics.Other effects studied were concentration, and perceived learning.The only statistically significant difference was found in classroom dynamics.No major wear off found on engagement, motivation, concentration and learning.


ROPDetect : Detection of Code Reuse Attacks


title: "The wheel" - an innovative visual model for interacting with a social web of things

abstract: Thanks to the evolution of identification and empowering technologies, interconnected and intelligent things are becoming popular in many domains. Depending on the kind of service smart things can offer, the possibility to identify and exchange services with them arises the significant problem of how users should interact and communicate with things themselves. It is therefore important to provide a proper interaction model, in order to help users in establishing a correct dialogue with empowered objects of the real world. Nevertheless, very little attention has been dedicated to the conceptualization of a good human-smart object interaction model and of an innovative visualization technique for networks of smart connected objects. In this paper we therefore present The Wheel, an interaction concept developed for WantEat suit of applications, designed to support the visualization and the exploration of identifiable things of the real world and their connections with other things. We propose a paradigm that enables a personalized, social and serendipitous interaction with networked things, allowing a continuos transition between real and digital world.


title: The Efficiency of Attentional Networks in Early and Late Bilinguals: The Role of Age of Acquisition

abstract: Previous studies have demonstrated a bilingual advantage in the efficiency of executive attention. A question remains, however, about the impact of the age of L2 acquisition and relative balance of the two languages on the enhancement of executive functions in bilinguals, and whether this is modulated by the similarity of the bilingual’s two languages. The present study explores these issues by comparing the efficiency of attentional networks amongst three groups of young adults living in Australia: English monolinguals and early and late Chinese-English bilinguals. We also address the impact of bilingualism on hemispheric lateralization of cognitive functions, which is of interest since a recent study on early bilinguals revealed reduced hemispheric asymmetry in attentional functioning. In the present study, participants performed a modified version of the lateralized attention network test (LANT). Both early and late bilinguals were found to have more efficient executive network than monolinguals. The late bilinguals, who were also reported to be more balanced in the proficiency and usage of their two languages, showed the greatest advantage in conflict resolution, whereas early bilinguals seemed to show enhanced monitoring processes. These group differences were observed when controlling for nonverbal intelligence and socioeconomic status. Such results suggest that specific factors of language experience may differentially influence the mechanisms of cognitive control. Since the bilinguals had distinct language sets, it seems that the influence of bilingualism on executive functions is present regardless of the similarity between the two languages. As for hemispheric lateralization, although the results were not clear-cut, they suggest the reduced lateralization in early bilinguals.


title: Alzheimer's Disease: Genes, Proteins, and Therapy

abstract: Rapid progress in deciphering the biological mechanism of Alzheimer's disease (AD) has arisen from the application of molecular and cell biology to this complex disorder of the limbic and association cortices. In turn, new insights into fundamental aspects of protein biology have resulted from research on the disease. This beneficial interplay between basic and applied cell biology is well illustrated by advances in understanding the genotype-to-phenotype relationships of familial Alzheimer's disease. All four genes definitively linked to inherited forms of the disease to date have been shown to increase the production and/or deposition of amyloid β-protein in the brain. In particular, evidence that the presenilin proteins, mutations in which cause the most aggressive form of inherited AD, lead to altered intramembranous cleavage of the β-amyloid precursor protein by the protease called γ-secretase has spurred progress toward novel therapeutics. The finding that presenilin itself may be the long-sought γ-...


title: Reinforcement learning for robot soccer

abstract: Batch reinforcement learning methods provide a powerful framework for learning efficiently and effectively in autonomous robots. The paper reviews some recent work of the authors aiming at the successful application of reinforcement learning in a challenging and complex domain. It discusses several variants of the general batch learning framework, particularly tailored to the use of multilayer perceptrons to approximate value functions over continuous state spaces. The batch learning framework is successfully used to learn crucial skills in our soccer-playing robots participating in the RoboCup competitions. This is demonstrated on three different case studies.


title: A new ECG feature extractor for biometric recognition

abstract: In this paper, a new wavelet based framework is developed and evaluated for automatic analysis of single lead electrocardiogram (ECG) for application in human recognition. The proposed system utilizes a robust preprocessing stage that enables it to handle noise and outliers so that it is directly applied on the raw ECG signal. Moreover, it is capable of handling ECGs regardless of the heart rate (HR) which renders making presumptions on the individual's stress level unnecessary. One of the novelties of this paper is the design of personalized heartbeat template so that the gallery set consists of only one heartbeart per subject. This substantial reduction of the gallery size, decreases the storage requirements of the system significantly. Furthermore, the classification process is speeded up by eliminating the need for dimensionality reduction techniques such as PCA or LDA. Experimental results for identification over PTB and MIT healthy ECG databases indicate a robust subject identification rate of 99.61% using only 2 heartbeats in average for each individual.


title: Deep Unsupervised Similarity Learning Using Partially Ordered Sets

abstract: Unsupervised learning of visual similarities is of paramount importance to computer vision, particularly due to lacking training data for fine-grained similarities. Deep learning of similarities is often based on relationships between pairs or triplets of samples. Many of these relations are unreliable and mutually contradicting, implying inconsistencies when trained without supervision information that relates different tuples or triplets to each other. To overcome this problem, we use local estimates of reliable (dis-)similarities to initially group samples into compact surrogate classes and use local partial orders of samples to classes to link classes to each other. Similarity learning is then formulated as a partial ordering task with soft correspondences of all samples to classes. Adopting a strategy of self-supervision, a CNN is trained to optimally represent samples in a mutually consistent manner while updating the classes. The similarity learning and grouping procedure are integrated in a single model and optimized jointly. The proposed unsupervised approach shows competitive performance on detailed pose estimation and object classification.


title: Prostate Histopathology: Learning Tissue Component Histograms for Cancer Detection and Classification

abstract: Radical prostatectomy is performed on approximately 40% of men with organ-confined prostate cancer. Pathologic information obtained from the prostatectomy specimen provides important prognostic information and guides recommendations for adjuvant treatment. The current pathology protocol in most centers involves primarily qualitative assessment. In this paper, we describe and evaluate our system for automatic prostate cancer detection and grading on hematoxylin & eosin-stained tissue images. Our approach is intended to address the dual challenges of large data size and the need for high-level tissue information about the locations and grades of tumors. Our system uses two stages of AdaBoost-based classification. The first provides high-level tissue component labeling of a superpixel image partitioning. The second uses the tissue component labeling to provide a classification of cancer versus noncancer, and low-grade versus high-grade cancer. We evaluated our system using 991 sub-images extracted from digital pathology images of 50 whole-mount tissue sections from 15 prostatectomy patients. We measured accuracies of 90% and 85% for the cancer versus noncancer and high-grade versus low-grade classification tasks, respectively. This system represents a first step toward automated cancer quantification on prostate digital histopathology imaging, which could pave the way for more accurately informed postprostatectomy patient care.


title: No-Reference Image Quality Assessment Using Visual Codebooks

abstract: The goal of no-reference objective image quality assessment (NR-IQA) is to develop a computational model that can predict the human-perceived quality of distorted images accurately and automatically without any prior knowledge of reference images. Most existing NR-IQA approaches are distortion specific and are typically limited to one or two specific types of distortions. In most practical applications, however, information about the distortion type is not really available. In this paper, we propose a general-purpose NR-IQA approach based on visual codebooks. A visual codebook consisting of Gabor-filter-based local features extracted from local image patches is used to capture complex statistics of a natural image. The codebook encodes statistics by quantizing the feature space and accumulating histograms of patch appearances. This method does not assume any specific types of distortions; however, when evaluating images with a particular type of distortion, it does require examples with the same or similar distortion for training. Experimental results demonstrate that the predicted quality score using our method is consistent with human-perceived image quality. The proposed method is comparable to state-of-the-art general-purpose NR-IQA methods and outperforms the full-reference image quality metrics, peak signal-to-noise ratio and structural similarity index on the Laboratory for Image and Video Engineering IQA database.


title: Legacy systems migration-a method and its tool-kit framework

abstract: The problems posed by mission-critical legacy systems: brittleness, inflexibility, isolation, non-extensibility, lack of openness etc., are well known, but practical solutions have been slow to emerge. Most approaches are "ad hoc" and tailored to peculiarities of individual systems. This paper presents an approach to mission-critical legacy system migration: the Butterfly methodology, its data migration engine and supporting toolkit framework. Data migration is the primary focus of the Butterfly methodology, however, it is placed in the overall context of a complete legacy system migration. The fundamental premise of the Butterfly methodology is to question the need for parallel operation of the legacy and target systems during migration. Much of the complexity of the current migration methodologies is eliminated by removing this interoperation assumption.


title: Learning to Identify Review Spam

abstract: In the past few years, sentiment analysis and opinion mining becomes a popular and important task. These studies all assume that their opinion resources are real and trustful. However, they may encounter the faked opinion or opinion spam problem. In this paper, we study this issue in the context of our product review mining system. On product review site, people may write faked reviews, called review spam, to promote their products, or defame their competitors' products. It is important to identify and filter out the review spam. Previous work only focuses on some heuristic rules, such as helpfulness voting, or rating deviation, which limits the performance of this task. ::: ::: In this paper, we exploit machine learning methods to identify review spam. Toward the end, we manually build a spam collection from our crawled reviews. We first analyze the effect of various features in spam identification. We also observe that the review spammer consistently writes spam. This provides us another view to identify review spam: we can identify if the author of the review is spammer. Based on this observation, we provide a twoview semi-supervised method, co-training, to exploit the large amount of unlabeled data. The experiment results show that our proposed method is effective. Our designed machine learning methods achieve significant improvements in comparison to the heuristic baselines.


title: InformationSense: Trade-offs for the Design and the Implementation of a Large Highly Deformable Cloth Display

abstract: Deformable displays can provide two major benefits compared to rigid displays: Objects of different shapes and deformabilities, situated in our physical environment, can be equipped with deformable displays, and users can benefit from their pre-existing knowledge about the interaction with physical objects when interacting with deformable displays. In this article we present InformationSense, a large, highly deformable cloth display. The article contributes to two research areas in the context of deformable displays: It presents an approach for the tracking of large, highly deformable surfaces, and it presents one of the first UX analyses of cloth displays that will help with the design of future interaction techniques for this kind of display. The comparison of InformationSense with a rigid display interface unveiled the trade-off that while users are able to interact with InformationSense more naturally and significantly preferred InformationSense in terms of joy of use, they preferred the rigid display interfaces in terms of efficiency. This suggests that deformable displays are already suitable if high hedonic qualities are important but need to be enhanced with additional digital power if high pragmatic qualities are required.


title: Intelligent Phishing Website Detection and Prevention System by Using Link Guard Algorithm

abstract: Phishing is a new type of network attack where the attacker creates a replica of an existing Web page to fool users (e.g., by using specially designed e-mails or instant messages) into submitting personal, financial, or password data to what they think is their service provides' Web site. In this research paper, we proposed a new end-host based anti-phishing algorithm, which we call Link Guard, by utilizing the generic characteristics of the hyperlinks in phishing attacks. These characteristics are derived by analyzing the phishing data archive provided by the Anti-Phishing Working Group (APWG). Because it is based on the generic characteristics of phishing attacks, Link Guard can detect not only known but also unknown phishing attacks. We have implemented Link Guard in Windows XP. Our experiments verified that Link Guard is effective to detect and prevent both known and unknown phishing attacks with minimal false negatives. Link Guard successfully detects 195 out of the 203 phishing attacks. Our experiments also showed that Link Guard is light weighted and can detect and prevent phishing attacks in real time.


title: A 49-to-62 GHz Quadrature VCO With Bimodal Enhanced-Magnetic-Tuning Technique

abstract: A bimodal enhanced-magnetic-tuning technique is proposed to extend the frequency tuning range of millimeter-wave (mmW) quadrature VCOs (QVCOs). First, the conventional magnetic tuning is reviewed, and its limitations for the frequency tuning at mmW frequencies are high-lighted. To enlarge the variable range of the magnetic field, a new transformer-based topology called enhanced-magnetic-tuning (EMT) is proposed and analyzed. In addition, by switching the polarity of the phase shift of the coupling currents, both of the two intrinsic modes in a quadrature VCO are exploited to ideally double the frequency tuning range. With a generic model, the proposed bimodal enhanced-magnetic-tuning (B-EMT) is analyzed mathematically and compared with the conventional frequency tuning techniques. Implemented in a 65 nm CMOS process, the QVCO prototype with the proposed B-EMT technique and 1-bit digitally controlled varactor measures a tuning range of 24% from 48.8 to 62.3 GHz and phase noise of -89 to -96 dBc/Hz at 1 MHz offset while drawing 13 to 25 mA from a 1.2-V supply, corresponding to FoM from 173 to 176 dBc and FoMT from 181 to 184 dBc/Hz.


Factors Affecting the Adoption of Electronic Word-of-Mouth in the Tourism Industry


title: DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model

abstract: The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations; and (3) an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speed-up factors. Evaluation is done on two single-person and two multi-person pose estimation benchmarks. The proposed approach significantly outperforms best known multi-person pose estimation results while demonstrating competitive performance on the task of single person pose estimation (Models and code available at http://pose.mpi-inf.mpg.de).


Non-Local Patch-Based Image Inpainting


title: The Mechanism of Confirming Big Data Property Rights Based on Smart Contract

abstract: The confirmation of data property rights is one of the most important functions of big data trading institutions. This paper builds a specialized classifier which aims to the normalization process of data property confirmation in the process of big data transaction and proposes the mechanism of confirming big data property rights based on Smart Contract.


Deep Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Imagery.


title: Browser Language Preferences as a Metric for Identifying ESL Speakers in MOOCs

abstract: Open access and low cost make Massively Open Online Courses (MOOCs) an attractive learning platform for students all over the world. However, the majority of MOOCs are deployed in English, which can pose an accessibility problem for students with English as a Second Language (ESL). In order to design appropriate interventions for ESL speakers, it is important to correctly identify these students using a method that is scalable to the high number of MOOC enrollees. Our findings suggest that a new metric, browser language preference, may be better than the commonly-used IP address for inferring whether or not a student is ESL.


title: Oxytocin increases trust in humans

abstract: Trust pervades human societies. Trust is indispensable in friendship, love, families and organizations, and plays a key role in economic exchange and politics. In the absence of trust among trading partners, market transactions break down. In the absence of trust in a country's institutions and leaders, political legitimacy breaks down. Much recent evidence indicates that trust contributes to economic, political and social success. Little is known, however, about the biological basis of trust among humans. Here we show that intranasal administration of oxytocin, a neuropeptide that plays a key role in social attachment and affiliation in non-human mammals, causes a substantial increase in trust among humans, thereby greatly increasing the benefits from social interactions. We also show that the effect of oxytocin on trust is not due to a general increase in the readiness to bear risks. On the contrary, oxytocin specifically affects an individual's willingness to accept social risks arising through interpersonal interactions. These results concur with animal research suggesting an essential role for oxytocin as a biological basis of prosocial approach behaviour.


title: Pooling of Unshared Information in Group Decision Making: Biased Information Sampling During Discussion

abstract: Decision-making groups can potentially benefit from pooling members' information, particularly when members individually have partial and biased information but collectively can compose an unbiased characterization of the decision alternatives. The proposed biased sampling model of group discussion, however, suggests that group members often fail to effectively pool their information because discussion tends to be dominated by (a) information that members hold in common before discussion and (b) information that supports members' existent preferences. In a political caucus simulation, group members individually read candidate descriptions that contained partial information biased against the most favorable candidate and then discussed the candidates as a group. Even though groups could have produced unbiased composites of the candidates through discussion, they decided in favor of the candidate initially preferred by a plurality rather than the most favorable candidate. Group members' pre- and postdiscussion recall of candidate attributes indicated that discussion tended to perpetuate, not to correct, members' distorted pictures of the candidates.


title: H-PARAFAC: Hierarchical Parallel Factor Analysis of Multidimensional Big Data

abstract: It has long been an important issue in various disciplines to examine massive multidimensional data superimposed by a high level of noises and interferences by extracting the embedded multi-way factors. With the quick increases of data scales and dimensions in the big data era, research challenges arise in order to (1) reflect the dynamics of large tensors while introducing no significant distortions in the factorization procedure and (2) handle influences of the noises in sophisticated applications. A hierarchical parallel processing framework over a GPU cluster, namely H-PARAFAC, has been developed to enable scalable factorization of large tensors upon a “divide-and-conquer” theory for Parallel Factor Analysis (PARAFAC). The H-PARAFAC framework incorporates a coarse-grained model for coordinating the processing of sub-tensors and a fine-grained parallel model for computing each sub-tensor and fusing sub-factors. Experimental results indicate that (1) the proposed method breaks the limitation on the scale of multidimensional data to be factorized and dramatically outperforms the traditional counterparts in terms of both scalability and efficiency, e.g., the runtime increases in the order of $n^2$ when the data volume increases in the order of $n^3$ , (2) H-PARAFAC has potentials in refraining the influences of significant noises, and (3) H-PARAFAC is far superior to the conventional window-based counterparts in preserving the features of multiple modes of large tensors.