title: Understanding and Controlling Memory in Recurrent Neural Networks

abstract: To be effective in sequential data processing, Recurrent Neural Networks (RNNs) are required to keep track of past events by creating memories. While the relation between memories and the network's hidden state dynamics was established over the last decade, previous works in this direction were of a predominantly descriptive nature focusing mainly on locating the dynamical objects of interest. In particular, it remained unclear how dynamical observables affect the performance, how they form and whether they can be manipulated. Here, we utilize different training protocols, datasets and architectures to obtain a range of networks solving a delayed classification task with similar performance, alongside substantial differences in their ability to extrapolate for longer delays. We analyze the dynamics of the network's hidden state, and uncover the reasons for this difference. Each memory is found to be associated with a nearly steady state of the dynamics which we refer to as a 'slow point'. Slow point speeds predict extrapolation performance across all datasets, protocols and architectures tested. Furthermore, by tracking the formation of the slow points we are able to understand the origin of differences between training protocols. Finally, we propose a novel regularization technique that is based on the relation between hidden state speeds and memory longevity. Our technique manipulates these speeds, thereby leading to a dramatic improvement in memory robustness over time, and could pave the way for a new class of regularization methods.


title: Analysis and Design of Adders for Approximate Computing

abstract: The concept of approximate computing, that is, to sacrifice computation quality for computation efforts, has recently emerged as a promising design approach. Over the past decade, several research works have explored approximate computing at both the software level and hardware level of abstraction with encouraging results. At the hardware level of abstraction, adders (being the fundamental and most widely used data operators in digital systems) have attracted a significant attention for approximation. In this article, we first explain briefly the need/significance of approximate adders. We then propose four Approximate Full Adders (AFAs) for high-performance energy-efficient approximate computing. The key design objective behind the proposed AFAs is to curtail the length of carry propagation subjected to minimal error rate. Next, we exploit one of the proposed AFAs (optimal one) to construct an N-bit approximate adder that hereinafter is referred as “ApproxADD.” An emergent property of ApproxADD is that carries do not propagate in it, and, consequently, it provides bit-width-aware constant delay (O(1)). ApproxADD also provides improvement in dynamic power consumption by 46.31% and in area by 28.57% w.r.t. Ripple Carry Adder (RCA), which exhibits the lowest power and area. Although ApproxADD provides a significant improvement in delay, power, and area, it may not be preferred for some of the error-resilient applications because its: (i) Error Distance (ED) is too high; and (ii) Error Rate (ER) increases rapidly with bit-width (N). To improve ED and ER, we exploit the concept of carry-lifetime and Error Detection and Correction logic, respectively. In this way, we introduce two more (improved) versions of ApproxADD--ApproxADDu1 and ApproxADD. We call these as ApproxADDu1 and ApproxADDu2 with existing approximate adders based on conventional design metrics and approximate computing design metrics. Furthermore, to inspect effectiveness of the proposed approach in real-life applications, we demonstrate image compression and decompression by replacing the conventional addition operations in Discrete Cosine Transform (DCT) and Inverse Discrete Cosine Transform (IDCT) modules with ApproxADDu2.


title: Multi-scale convolutional neural networks for crowd counting

abstract: Crowd counting on static images is a challenging problem due to scale variations. Recently deep neural networks have been shown to be effective in this task. However, existing neural-networks-based methods often use the multi-column or multi-network model to extract the scale-relevant features, which is more complicated for optimization and computation wasting. To this end, we propose a novel multi-scale convolutional neural network (MSCNN) for single image crowd counting. Based on the multi-scale blobs, the network is able to generate scale-relevant features for higher crowd counting performances in a single-column architecture, which is both accuracy and cost effective for practical applications. Complemental results show that our method outperforms the state-of-the-art methods on both accuracy and robustness with far less number of parameters.


title: Optimized Product Quantization

abstract: Product quantization (PQ) is an effective vector quantization method. A product quantizer can generate an exponentially large codebook at very low memory/time cost. The essence of PQ is to decompose the high-dimensional vector space into the Cartesian product of subspaces and then quantize these subspaces separately. The optimal space decomposition is important for the PQ performance, but still remains an unaddressed issue. In this paper, we optimize PQ by minimizing quantization distortions w.r.t the space decomposition and the quantization codebooks. We present two novel solutions to this challenging optimization problem. The first solution iteratively solves two simpler sub-problems. The second solution is based on a Gaussian assumption and provides theoretical analysis of the optimality. We evaluate our optimized product quantizers in three applications: (i) compact encoding for exhaustive ranking [1], (ii) building inverted multi-indexing for non-exhaustive search [2], and (iii) compacting image representations for image retrieval [3]. In all applications our optimized product quantizers outperform existing solutions.


title: A Cross-Lingual Mobile Medical Communication System Prototype for Foreigners and Subjects with Speech, Hearing, and Mental Disabilities Based on Pictograms

abstract: People with speech, hearing, or mental impairment require special communication assistance, especially for medical purposes. Automatic solutions for speech recognition and voice synthesis from text are poor fits for communication in the medical domain because they are dependent on error-prone statistical models. Systems dependent on manual text input are insufficient. Recently introduced systems for automatic sign language recognition are dependent on statistical models as well as on image and gesture quality. Such systems remain in early development and are based mostly on minimal hand gestures unsuitable for medical purposes. Furthermore, solutions that rely on the Internet cannot be used after disasters that require humanitarian aid. We propose a high-speed, intuitive, Internet-free, voice-free, and text-free tool suited for emergency medical communication. Our solution is a pictogram-based application that provides easy communication for individuals who have speech or hearing impairment or mental health issues that impair communication, as well as foreigners who do not speak the local language. It provides support and clarification in communication by using intuitive icons and interactive symbols that are easy to use on a mobile device. Such pictogram-based communication can be quite effective and ultimately make people’s lives happier, easier, and safer.


title: The persistent effect of pre-college computing experience on college CS course grades

abstract: Many college computer science majors have little or no pre-college computing experience. Previous work has shown that inexperienced students under-perform their experienced peers when placed in the same introductory courses and are more likely to drop out of the CS program. However, not much is known about what, if any, differences may persist beyond the introductory sequence for students who remain in the program. We conducted a study across all levels of a CS program at a large public university in the United States to determine whether grade differences exist between students with and without pre-college experience, and if so, for what types of experiences. We find significant grade differences in courses at all levels of the program. We further find that students who took AP Computer Science receive significantly higher average grades---by up to a half grade---in nearly all courses we studied. Pre-college experience appears to have a weaker relationship with retention and with low-stakes assessment grades. We discuss the limitations of these findings and implications for high school and college level CS courses and programs.


title: Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval

abstract: The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classification, focusing on the distributional assumptions made about word occurrences in documents.


title: GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild

abstract: We introduce here a large tracking database that offers an unprecedentedly wide coverage of common moving objects in the wild, called GOT-10k. Specifically, GOT-10k is built upon the backbone of WordNet structure and it populates the majority of over 560 classes of moving objects and 87 motion patterns, magnitudes wider than the most recent similar-scale counterparts. The contributions of this paper are summarized in the following: (1) GOT-10k offers over 10,000 video segments with more than 1.5 million manually labeled bounding boxes, enabling unified training and stable evaluation of deep trackers. (2) GOT-10k is by far the first video trajectory dataset that uses the semantic hierarchy of WordNet to guide class population. (3) For the first time, GOT-10k introduces the one-shot protocol for tracker evaluation, where the training and test classes are zero-overlapped. The protocol avoids biased evaluation results towards familiar objects and it promotes generalization in tracker development. (4) We conduct extensive tracking experiments with 39 typical tracking algorithms on GOT-10k and analyze their results in this paper. (5) Finally, we develop a comprehensive platform for the tracking community that offers full-featured evaluation toolkits, an online evaluation server, and a responsive leaderboard. The annotations of GOT-10k's test data are kept private to avoid tuning parameters on it. The database, toolkits, evaluation server and baseline results are available at this http URL


title: Development, dimensions, reliability and validity of the novel Manchester COPD fatigue scale

abstract: INTRODUCTION ::: Fatigue is a prominent symptom in chronic obstructive pulmonary disease (COPD) and it has distinctive features; however, there is a need for a robust scale to measure fatigue in COPD. ::: ::: ::: METHODS ::: At baseline, 122 patients with COPD (forced expiratory volume in 1 s (FEV(1)) 52%, women 38%, mean age 66 years) completed a pilot fatigue scale covering a pool of 57 items and underwent a range of tests, including indicators of mood and a short general fatigue questionnaire. All patients responded to the 57-item scale and it was readministered to a subset of 30 patients. The pilot scale was first subjected to constructive validated shortening steps and then to a principal components analysis. ::: ::: ::: RESULTS ::: The Manchester COPD fatigue scale (MCFS) consists of 27 items, loading into three dimensions: physical, cognitive and psychosocial fatigue. Internal consistency (Cronbach's alpha = 0.97) and test-retest repeatability (r = 0.97, p<0.001) were tested. It had significant convergent validity, correlating with the FACIT (Functional Assessment of Chronic Illness Therapy) fatigue scale and the fatigue in Borg scale at baseline and after a 6 minute walk distance (6MWD) test (r = -0.81, 0.53 and 0.63, respectively, p<0.001). Its scores were associated with BODE, SGRQ (St George's Respiratory Questionnaire) and MRC (Medical Research Council) dyspnoea scores (r = 0.46, 0.8 and 0.51, respectively, p<0.001). The scale demonstrated meaningful discriminating ability; patients who walked <350 m in a 6MWD test as well as depressed patients (>or=16 scores in the Center for Epidemiologic Study on Depression (CES-D) scale) had nearly twice as high fatigue scores as those who walked >or=350 m or were not depressed (p<0.001). ::: ::: ::: CONCLUSION ::: The MCFS provides a simple, reliable and valid measurement of total and dimensional fatigue in moderate stable COPD.


title: Working for free? Motivations of participating in open source projects

abstract: The success of the Linux operating system has demonstrated the viability of an alternative form of software development: open source software, which challenges traditional assumptions about software markets. Understanding what drives open source developers to participate in open source projects is crucial for assessing the impact of open source software. The article identifies two broad types of motivations that account for their participation in open source projects. The first category includes internal factors such as intrinsic motivation and altruism, and the second category focuses on external rewards such as expected future returns and personal needs. The article also reports the results of a survey administered to open source programmers.


title: Foldable Printed Circuit Boards on Paper Substrates

abstract: This paper describes several low-cost methods for fabricating flexible electronic circuits on paper. The circuits comprise i) metallic wires (e.g., tin or zinc) that are deposited on the substrate by evaporation, sputtering, or airbrushing, and ii) discrete surface-mountable electronic components that are fastened with conductive adhesive directly to the wires. These electronic circuits—like conventional printed circuit boards—can be produced with electronic components that connect on both sides of the substrate. Unlike printed circuit boards made from fiberglass, ceramics, or polyimides, however, paper can be folded and creased (repeatedly), shaped to form three-dimensional structures, trimmed using scissors, used to wick fluids (e.g., for microfluidic applications) and disposed of by incineration. Paper-based electronic circuits are thin and lightweight; they should be useful for applications in consumer electronics and packaging, for disposable systems for uses in the military and homeland security, for applications in medical sensing or low-cost portable diagnostics, for paper-based microelectromechanical systems, and for applications involving textiles.


title: DecomposeMe: Simplifying ConvNets for End-to-End Learning

abstract: Deep learning and convolutional neural networks (ConvNets) have been successfully applied to most relevant tasks in the computer vision community. However, these networks are computationally demanding and not suitable for embedded devices where memory and time consumption are relevant. ::: In this paper, we propose DecomposeMe, a simple but effective technique to learn features using 1D convolutions. The proposed architecture enables both simplicity and filter sharing leading to increased learning capacity. A comprehensive set of large-scale experiments on ImageNet and Places2 demonstrates the ability of our method to improve performance while significantly reducing the number of parameters required. Notably, on Places2, we obtain an improvement in relative top-1 classification accuracy of 7.7\% with an architecture that requires 92% fewer parameters compared to VGG-B. The proposed network is also demonstrated to generalize to other tasks by converting existing networks.


title: An efficient representation for irradiance environment maps

abstract: We consider the rendering of diffuse objects under distant illumination, as specified by an environment map. Using an analytic expression for the irradiance in terms of spherical harmonic coefficients of the lighting, we show that one needs to compute and use only 9 coefficients, corresponding to the lowest-frequency modes of the illumination, in order to achieve average errors of only 1%. In other words, the irradiance is insensitive to high frequencies in the lighting, and is well approximated using only 9 parameters. In fact, we show that the irradiance can be procedurally represented simply as a quadratic polynomial in the cartesian components of the surface normal, and give explicit formulae. These observations lead to a simple and efficient procedural rendering algorithm amenable to hardware implementation, a prefiltering method up to three orders of magnitude faster than previous techniques, and new representations for lighting design and image-based rendering.


title: Deep Layer Aggregation

abstract: Visual recognition requires rich representations that span levels from low to high, scales from small to large, and resolutions from fine to coarse. Even with the depth of features in a convolutional network, a layer in isolation is not enough: compounding and aggregating these representations improves inference of what and where. Architectural efforts are exploring many dimensions for network backbones, designing deeper or wider architectures, but how to best aggregate layers and blocks across a network deserves further attention. Although skip connections have been incorporated to combine layers, these connections have been "shallow" themselves, and only fuse by simple, one-step operations. We augment standard architectures with deeper aggregation to better fuse information across layers. Our deep layer aggregation structures iteratively and hierarchically merge the feature hierarchy to make networks with better accuracy and fewer parameters. Experiments across architectures and tasks show that deep layer aggregation improves recognition and resolution compared to existing branching and merging schemes.


title: A high temperature, double-sided cooling SiC power electronics module

abstract: A high temperature, wire-bondless power electronics module with a double-sided cooling capability is proposed and successfully fabricated. In this module, a low-temperature co-fired ceramic (LTCC) substrate was used as the dielectric and chip carrier. Conducting vias were created on the LTCC carrier to realize the interconnection. The absent of a base plate reduced the overall thermal resistance and also improved the fatigue life by eliminating a large-area solder layer. Nano silver paste was used to attach power devices to the DBC substrate as well as to pattern the gate connection. Finite element simulations were used to compare the thermal performance to several reported double-sided power modules. Electrical measurements of a SiC MOSFET and SiC diode switching position demonstrated the functionality of the module.


title: Classifiers Comparison for Convolutional Neural Networks (CNNs) in Image Classification

abstract: This paper presents a comparison between five different classifiers (Multi-class Logistic Regression (MLR), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Random Forest (RF) and Gaussian Naive Bayes (GNB)) to be used in a Convolutional Neural Network (CNN) in order to perform images classification. For our experiments we have used a dataset composed of images of objects belonging to 256 widely varied categories called Caltech 256.


title: Underactuated finger mechanism for natural motion and self-adaptive grasping towards bionic partial hand

abstract: This paper presents an underactuated finger mechanism enabling both self-adaptive grasping and natural motion such as flexion and extension. It has three degrees-of-freedom mechanism composed of one actuator and a couple of passive components including torsional springs and mechanical stoppers. In detail, the proposed mechanism consists of two five-bar and one four-bar linkages. Since each five-bar linkage contains passive components, it is allowed to have adaptive grasping as well as natural motion for human finger-like behavior. Kinematics and static force analysis are performed to reveal the operational principle of the proposed mechanism. Finally both simulation and experiments are conducted to show the design feasibility of the proposed mechanism.


title: Detecting splicing in digital audios using local noise level estimation

abstract: One common form of tampering in digital audio signals is known as splicing, where sections from one audio is inserted to another audio. In this paper, we propose an effective splicing detection method for audios. Our method achieves this by detecting abnormal differences in the local noise levels in an audio signal. This estimation of local noise levels is based on an observed property of audio signals that they tend to have kurtosis close to a constant in the band-pass filtered domain. We demonstrate the efficacy and robustness of the proposed method using both synthetic and realistic audio splicing forgeries.


title: Simulating the business model canvas using system dynamics

abstract: The business model canvas is a well-known tool used to understand the way in which an organization creates and delivers value. Though it is mostly recognized as a 9 block template with sticky notes, it is much more complex than what it seems. Unfortunately, the static representation of the model has contributed to the loss of its dynamics and the information that comes with it. Enterprises do not contemplate the flow network involved in their business model, and miss the opportunity to identify problems and test changes. This situation invites to enrich the canvas model and show more benefits that can be obtained with its application. This paper presents an enriched design of the business model canvas, based on system dynamics and the results obtained after simulating it.


title: Learning the weight of the query term from the relevance feedback

abstract: We address the problem of assigning each query word an appropriate weight in the retrieval function. Term weight assignment is important, which depends on the relationship among the query words and also impacts the retrieval performance directly. However, various retrieval models can be adopted by the system, which requires different approaches to set term weight and those empirical settings can not ensure to improve the retrieval quality. We propose an unified approach for different retrieval functions to set a unique weight to each individual word. We explore the popular retrieval functions and propose to regard the retrieval function as a linear classification model, which is aimed to predicate the relevance of the document. Thus the parameters in the learning model can be explained as the term weight in the retrieval model. For each query topic, we adopt the generative model and the discriminative model to estimate the term weight by taking the relevance feedback information as the training data. Our analysis gives more insight into the Rocchio's framework on relevance feedback, which can be taken as a special case in the generative model. Experimental results on the benchmark datasets show that by estimating proper weight to each query word, our approach can outperform the baseline methods of BM25 and obtain an equivalent performance with the probability language model.


title: Modeling Autobiographical Memory in Human-Like Autonomous Agents

abstract: Although autobiographical memory is an important part of the human mind, there has been little effort on modeling autobiographical memory in autonomous agents. With the motivation of developing human-like intelligence, in this paper, we delineate our approach to enable an agent to maintain memories of its own and to wander in mind. Our model, named Autobiographical Memory-Adaptive Resonance Theory network (AM-ART), is designed to capture autobiographical memories, comprising pictorial snapshots of one's life experiences together with the associated context, namely time, location, people, activity, and emotion. In terms of both network structure and dynamics, AM-ART coincides with the autobiographical memory model established by the psychologists, which has been supported by neural imaging evidence. Specifically, the bottom-up memory search and the top-down memory readout operations of AM-ART replicate how the brain encodes and retrieves autobiographical memories. Furthermore, the wandering in reminiscence function of AM-ART mimics how human wanders in mind. For evaluations, we conducted experiments on a data set collected from the public domain to test the performance of AM-ART in response to exact, partial, and noisy memory retrieval cues. Moreover, our statistical analysis shows that AM-ART can simulate the phenomenon of wandering in reminiscence.


title: Cloak and Swagger: Understanding Data Sensitivity through the Lens of User Anonymity

abstract: Most of what we understand about data sensitivity is through user self-report (e.g., surveys), this paper is the first to use behavioral data to determine content sensitivity, via the clues that users give as to what information they consider private or sensitive through their use of privacy enhancing product features. We perform a large-scale analysis of user anonymity choices during their activity on Quora, a popular question-and-answer site. We identify categories of questions for which users are more likely to exercise anonymity and explore several machine learning approaches towards predicting whether a particular answer will be written anonymously. Our findings validate the viability of the proposed approach towards an automatic assessment of data sensitivity, show that data sensitivity is a nuanced measure that should be viewed on a continuum rather than as a binary concept, and advance the idea that machine learning over behavioral data can be effectively used in order to develop product features that can help keep users safe.


title: Goodput Analysis and Link Adaptation for IEEE 802.11a Wireless LANs

abstract: Link adaptation to dynamically select the data transmission rate at a given time has been recognized as an effective way to improve the goodput performance of the IEEE 802.11 wireless local-area networks (WLANs). Recently, with the introduction of the new high-speed 802.11a physical layer (PHY), it is even more important to have a well-designed link adaptation scheme work with the 802.11a PHY such that its multiple transmission rates can be exploited. In this paper, we first present a generic method to analyze the goodput performance of an 802.11a system under the distributed coordination function (DCF) and express the expected effective goodput as a closed-form function of the data payload length, the frame retry count, the wireless channel condition, and the selected data transmission rate. Then, based on the theoretical analysis, we propose a novel MPDU (MAC protocol data unit)-based link adaptation scheme for the 802.11a systems. It is a simple table-driven approach and the basic idea is to preestablish a best PHY mode table by applying the dynamic programming technique. The best PHY mode table is indexed by the system status triplet that consists of the data payload length, the wireless channel condition, and the frame retry count. At runtime, a wireless station determines the most appropriate PHY mode for the next transmission attempt by a simple table lookup, using the most up-to-date system status as the index. Our in-depth simulation shows that the proposed MPDU-based link adaptation scheme outperforms the single-mode schemes and the autorate fallback (ARF) scheme-which is used in Lucent Technologies' WaveLAN-II networking devices-significantly in terms of the average goodput, the frame drop rate, and the average number of transmission attempts per data frame delivery.


title: A reliable and valid measurement scale for assessing internal crisis communication

abstract: There appears to be a growing concern over the lack of scales for measuring internal crisis communication (ICC) in the course of an organizational crisis. Noting this compelling evidence, an analysis of results from earlier exploratory studies demonstrated a strong need for a sound scale with adequate psychometric properties. On that account, the purpose of this paper is to develop a valid scale for ICC.,This study examined the conceptualization and operationalization of ICC based on a comprehensive literature search. The in-depth interviews consist of 12 key informants with a sample size of 251 respondents for scale development and testing wherein all data items were evaluated and validated by 13 expert reviewers.,The results were measured during a crisis situation and provided rationale and initial psychometric properties of ICC. Exploratory and confirmatory analyses have provided evidence that ICC scale has achieved a valid and reliable factor structure.,Therefore, practitioners such as policy makers, government, researchers and crisis managers can adopt this scale of assessment to enhance a more accurate ICC; in this regard, provide useful implications to help minimize risks as they happen, thereupon find a better way to manage crisis situations.,This study contributes to the subject area and builds the existing literature by developing 11 items for ICC. The research provides a starting point for empirical investigation on important factors of influence on organizational internal stakeholders.


title: Real-time Deep Video Deinterlacing

abstract: Interlacing is a widely used technique, for television broadcast and video recording, to double the perceived frame rate without increasing the bandwidth. But it presents annoying visual artifacts, such as flickering and silhouette "serration," during the playback. Existing state-of-the-art deinterlacing methods either ignore the temporal information to provide real-time performance but lower visual quality, or estimate the motion for better deinterlacing but with a trade-off of higher computational cost. In this paper, we present the first and novel deep convolutional neural networks (DCNNs) based method to deinterlace with high visual quality and real-time performance. Unlike existing models for super-resolution problems which relies on the translation-invariant assumption, our proposed DCNN model utilizes the temporal information from both the odd and even half frames to reconstruct only the missing scanlines, and retains the given odd and even scanlines for producing the full deinterlaced frames. By further introducing a layer-sharable architecture, our system can achieve real-time performance on a single GPU. Experiments shows that our method outperforms all existing methods, in terms of reconstruction accuracy and computational performance.


Compressed Nonparametric Language Modelling


title: A Recurrent Neural Network–based Forecasting System for Telecommunications Call Volume

abstract: A recurrent neural network-based forecasting system for telecommunications call volume is proposed in this work. In particular, the forecaster is a Block-Diagonal Recurrent Neural Network with internal feedback. Model's performance is evaluated by use of real-world telecommunications data, where an extensive comparative analysis with a series of existing forecasters is conducted, including both traditional models as well as neural and fuzzy approaches.


title: Crowded Scene Analysis: A Survey

abstract: Automated scene analysis has been a topic of great interest in computer vision and cognitive science. Recently, with the growth of crowd phenomena in the real world, crowded scene analysis has attracted much attention. However, the visual occlusions and ambiguities in crowded scenes, as well as the complex behaviors and scene semantics, make the analysis a challenging task. In the past few years, an increasing number of works on the crowded scene analysis have been reported, which covered different aspects including crowd motion pattern learning, crowd behavior and activity analyses, and anomaly detection in crowds. This paper surveys the state-of-the-art techniques on this topic. We first provide the background knowledge and the available features related to crowded scenes. Then, existing models, popular algorithms, evaluation protocols, and system performance are provided corresponding to different aspects of the crowded scene analysis. We also outline the available datasets for performance evaluation. Finally, some research problems and promising future directions are presented with discussions.


title: A Measure of Suffering in relation to Anxiety and Quality of Life in IBS Patients: Preliminary Results

abstract: Irritable bowel syndrome (IBS) is a chronic gastrointestinal (GI) disorder with a severe impact on quality of life (QoL). We explored the relationship of a visual measure of suffering, the PRISM-RII, with quality of life (QoL) and anxiety measures in IBS patients. Participants were 44 IBS patients who completed several questionnaires and kept a symptom diary for two weeks. The measures used were PRISM-RII (self-illness separation (SIS); illness perception measure (IPM)); IBS-36 (IBS health related QoL); SF-36 (physical and mental health related QoL); State-Trait Anxiety Inventory (STAI-T); Visceral Sensitivity Index (VSI; GI-specific anxiety); and a symptom diary. SIS was negatively correlated to VSI, while IPM was negatively correlated to SIS and the physical component of SF-36 and positively to VSI and symptom severity. We found significant differences between participants who perceive their illness as small and those who perceive it as medium in SIS, symptom severity, VSI, and the mental component of SF-36. Participants, who perceived their illness as small, represented their illness as more distant, showed lower average symptom severity, and had lower GI-specific anxiety and higher QoL. The results indicate that IPM and SIS can be useful in discriminating patients with more prominent psychological difficulties and QoL impairment.


title: The Impact of Social Networking Websites to Facilitate the Effectiveness of Viral Marketing

abstract: The Internet and the World Wide Web have become two key components in today's technology based organizations and businesses. As the Internet is becoming more and more popular, it is starting to make a big impact on people's day-to-day life. As a result of this revolutionary transformation towards the modern technology, social networking on the World Wide Web has become an integral part of a large number of people's lives. Social networks are websites which allow users to communicate, share knowledge about similar interests, discuss favorite topics, review and rate products/services, etc. These websites have become a powerful source in shaping public opinion on virtually every aspect of commerce. Marketers are challenged with identifying influential individuals in social networks and connecting with them in ways that encourage viral marketing content movement and there has been little empirical research study about of this website to diffuse of viral marketing content. In this article, we explore the role of social network websites which has influence on viral marketing, and the characteristics of the most influential users to spread share viral content. Structural equation modeling is used to examine the patterns of inter-correlations among the constructions and to empirically test the hypotheses.


title: Control Flow Integrity for COTS Binaries ∗

abstract: Control-Flow Integrity (CFI) has been recognized as an important low-level security property. Its enforcement can defeat most injected and existing code attacks, including those based on Return-Oriented Programming (ROP). Previous implementations of CFI have required compiler support or the presence of relocation or debug information in the binary. In contrast, we present a technique for applying CFI to stripped binaries on ×86/Linux. Ours is the first work to apply CFI to complex shared libraries such as glibc. Through experimental evaluation, we demonstrate that our CFI implementation is effective against control-flow hijack attacks, and eliminates the vast majority of ROP gadgets. To achieve this result, we have developed robust techniques for disassembly, static analysis, and transformation of large binaries. Our techniques have been tested on over 300MB of binaries (executables and shared libraries).


title: Cyber security: challenges for society- literature review

abstract: Cyber security is the activity of protecting information and information systems (networks, computers, data bases, data centres and applications) with appropriate procedural and technological security measures. Firewalls, antivirus software, and other technological solutions for safeguarding personal data and computer networks are essential but not sufficient to ensure security. As our nation rapidly building its Cyber- Infrastructure, it is equally important that we educate our population to work properly with this infrastructure. Cyber-Ethics, Cyber-Safety, and Cyber-Security issues need to be integrated in the educational process beginning at an early age. Security counter measures help ensure the confidentiality, availability, and integrity of information systems by preventing or mitigating asset losses from Cyber security attacks. Recently cyber security has emerged as an established discipline for computer systems andinfrastructures with a focus on protection of valuable information stored on those systems fromadversaries who want to obtain, corrupt, damage, destroy or prohibit access to it. An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. A wide range of metaphors was considered, including those relating to: military and other types of conflict, biological, health care, markets, three-dimensional space, and physical asset protection. These in turn led to consideration of a variety of possible approaches for improving cyber security in the future. These approaches were labelled "Heterogeneity" ,"Motivating Secure Behaviour" and "Cyber Wellness" . Cyber Security plays an important role in the development of information technology as well as Internet services. Our attention is usually drawn on "Cyber Security" when we hear about "Cyber Crimes". Our first thought on "National Cyber Security" therefore starts on how good is our infrastructure for handling "Cyber Crimes". This paper focus on cyber security emerging trends while adopting new technologies such as mobile computing, cloud computing, e-commerce, and social networking. The paper also describes the challenges due to lack of coordination between Security agencies and the Critical IT Infrastructure. Keywords - cyber safety,e-commerce ,intrusion detection system (IDS), internet engineering task force (IETF),metaphors


title: MOSFET design for forward body biasing scheme

abstract: Forward body biasing is a solution for continued scaling of bulk-Si CMOS technology. In this letter, the dependence of 30-nm-gate MOSFET performance on body bias is experimentally evaluated for devices with various channel-doping profiles to provide guidance for channel engineering in a forward body-biasing scheme. Furthermore, simulations of 10-nm-gate CMOS (hp22-nm node) devices are performed to study the optimal channel-doping profile and gate work function engineering for a forward biasing scheme.


title: A real time microscopy study of the growth of giant Au microplates

abstract: A simple air-thermolysis of a precursor, (AuCl4)− phase transferred from aqueous medium and stabilized in toluene by tetraoctylammonium bromide (ToABr), is found to yield unusually large (∼12 000 μm2) hexagonal and triangular Au microplates. The optimal conditions for the growth of microplates with edge lengths over 100 μm are (AuCl4)−/ToABr ratio of 1:4.4 for phase transfer and thermolysis on a flat substrate such as Si at 130 °C for 24 h. Scanning electron microscopy performed using a heated stage showed nucleation of nanoparticles into dendrimeric structures and rudimentary shapes, which took several minutes to anneal into well-formed single crystalline triangles and hexagons. Amazingly quite often the plates were held vertical to the substrate surface during the growth even at large sizes, as evidenced by in situ optical microscopy. Three growth modes, namely, nucleation of tiny nanoparticles into dendrimeric structures which assemble into specific shapes, atom-by-atom addition from the precursor feed...


title: Surrogate Benchmarks for Hyperparameter Optimization

abstract: Since hyperparameter optimization is crucial for achieving peak performance with many machine learning algorithms, an active research community has formed around this problem in the last few years. The evaluation of new hyperparameter optimization techniques against the state of the art requires a set of benchmarks. Because such evaluations can be very expensive, early experiments are often performed using synthetic test functions rather than using real-world hyperparameter optimization problems. However, there can be a wide gap between the two kinds of problems. In this work, we introduce another option: cheap-to-evaluate surrogates of real hyperparameter optimization benchmarks that share the same hyperparameter spaces and feature similar response surfaces. Specifically, we train regression models on data describing a machine learning algorithm's performance under a wide range of hyperparameter configurations, and then cheaply evaluate hyperparameter optimization methods using the model's performance predictions in lieu of the real algorithm. We evaluate the effectiveness for using a wide range of regression techniques to build these surrogate benchmarks, both in terms of how well they predict the performance of new configurations and of how much they affect the overall performance of hyperparameter optimizers. Overall, we found that surrogate benchmarks based on random forests performed best: for benchmarks with few hyperparameters they yielded almost perfect surrogates, and for benchmarks with more complex hyperparameter spaces they still yielded surrogates that were qualitatively similar to the real benchmarks they model.


title: Local random-phase noise for procedural texturing

abstract: Local random-phase noise is a noise model for procedural texturing. It is defined on a regular spatial grid by local noises, which are sums of cosines with random phase. Our model is versatile thanks to separate sampling in the spatial and spectral domains. Therefore, it encompasses Gabor noise and noise by Fourier series. A stratified spectral sampling allows for a faithful yet compact and efficient reproduction of an arbitrary power spectrum. Noise by example is therefore obtained faster than state-of-the-art techniques. As a second contribution we address texture by example and generate not only Gaussian patterns but also structured features present in the input. This is achieved by fixing the phase on some part of the spectrum. Generated textures are continuous and non-repetitive. Results show unprecedented framerates and a flexible visual result: users can control with one parameter the blending between noise by example and structured texture synthesis.


title: Twin gate rectangular recessed channel (TG-RRC) MOSFET for digital-logic applications

abstract: In this paper, twin gate rectangular recessed channel (TG-RRC) MOSFET with independent gate control is used to realize its application in digital electronics by using it as two input logic. The input logic is controlled by the independent gates which have different work functions (Φ1 for gate 1 and Φ2 gate 2) which are separated by oxide layer of 2 nm, thus controlling various electrical parameters such as current density, potential etc. across the channel in a swift manner and also increasing the switching characteristics of the device. It can give the full functionality of “AND” and “NAND” gate. We have shown the structure and functioning of the gates with a simulation on Silvaco TCAD. Since it is one of the fundamental gates has much applicability in digital logics starting from basic Flip-flops to sequential circuits in SRAM or DRAM cells.


title: A Log-Linear Model for Unsupervised Text Normalization

abstract: We present a unified unsupervised statistical model for text normalization. The relationship between standard and non-standard tokens is characterized by a log-linear model, permitting arbitrary features. The weights of these features are trained in a maximumlikelihood framework, employing a novel sequential Monte Carlo training algorithm to overcome the large label space, which would be impractical for traditional dynamic programming solutions. This model is implemented in a normalization system called UNLOL, which achieves the best known results on two normalization datasets, outperforming more complex systems. We use the output of UNLOL to automatically normalize a large corpus of social media text, revealing a set of coherent orthographic styles that underlie online language variation.


title: Too arrogant for their own good? Why and when narcissists dismiss advice

abstract: Advice taking is central to making better decisions, but some individuals seem unwilling to use advice. The present research examined the relationship between narcissism and advice taking. In particular, we studied the mechanisms that explain why narcissists are dismissive of advice. In three studies, we found that narcissism and advice taking were negatively related, but only when measuring narcissism at the state level or when controlling for extraversion at the trait level. We also tested two mechanisms and found that confidence did not mediate the relationship; disregard for others did. In Study 4, participants were placed under different accountability pressures to affect self-enhancement. Results showed that the narcissism–advice taking relationship was strongly negative under process accountability. Taken together, these results suggest that narcissists eschew advice not because of greater confidence, but because they think others are incompetent and because they fail to reduce their self-enhancement when expecting to be assessed.


title: Feature selection in clinical data processing for classification

abstract: Data mining includes wide varieties of data such as clinical, scientific, biological, remote sensing etc. Clinical data can be used for clinical data mining. Clinical information often con- sists of Time-series and Cross-sectional data. In order to analyze clinical data a method which deals with both these types of data is therefore essential. This was tackled by an algorithm with period merging for the datas which were of multiple-time-series. Most popular primary liver cancer is hepatocellular carcinoma (HCC). Its occurrence make it stand fifth in the world.For a definite time period, reports were taken and merged using the merging algorithm.From this,features were selected based on random forests. After data processing, a classification method, support vector machine (MMSVM)for multiple measurements with kernel as RBF, was used to predict the hepatocellular carcinoma (HCC) recurrence. To enhance the accuracy and efficiency of predicting the outputs of clinical processes by using multiple measurements,a novel approach for feature selection is proposed. The relevant and most representative features are selected by this method.


title: Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization

abstract: We continue to develop our neural network (NN) based forecasting approach to anomaly detection (AD) using the Secure Water Treatment (SWaT) industrial control system (ICS) testbed dataset. We propose genetic algorithms (GA) to find the best NN architecture for a given dataset, using the NAB metric to assess the quality of different architectures. The drawbacks of the F1-metric are analyzed. Several techniques are proposed to improve the quality of AD: exponentially weighted smoothing, mean p-powered error measure, individual error weight for each variable, disjoint prediction windows. Based on the techniques used, an approach to anomaly interpretation is introduced.


title: Fast on-the-fly composition for weighted finite-state transducers in 1.8 million-word vocabulary continuous speech recognition

abstract: This paper proposes a new on-the-fly composition algorithm for Weighted Finite-State Transducers (WFSTs) in large-vocabulary continuous-speech recognition. In general on-the-fly composition, two transducers are composed during decoding, and a Viterbi search is performed based on the composed search space. In this new method, a Viterbi search is performed based on the first of two transducers. The second transducer is only used to rescore the hypotheses generated during the search. Since this rescoring is very efficient, the total amount of computation in the new method is almost the same as when using only the first transducer. In a 30kword vocabulary spontaneous lecture speech transcription task, our proposed method significantly outperformed the general on-the-fly composition method. Furthermore the speed of our method was slightly faster than that of decoding with a single fully composed and optimized WFST, where our method consumed only 20% of the memory usage required for decoding with the single WFST. Finally, we have achieved one-pass real-time speech recognition in an extremely large vocabulary of 1.8 million words.


title: The making of ENCODE: Lessons for big-data projects

abstract: To be successful, consortia need clear management, codes of conduct and participants who are committed to working for the common good, says ENCODE lead analysis coordinator Ewan Birney.


title: Task switching

abstract: Everyday life requires frequent shifts between cognitive tasks. Research reviewed in this article probes the control processes that reconfigure mental resources for a change of task by requiring subjects to switch frequently among a small set of simple tasks. Subjects' responses are substantially slower and, usually, more error-prone immediately after a task switch. This 'switch cost' is reduced, but not eliminated, by an opportunity for preparation. It seems to result from both transient and long-term carry-over of 'task-set' activation and inhibition as well as time consumed by task-set reconfiguration processes. Neuroimaging studies of task switching have revealed extra activation in numerous brain regions when subjects prepare to change tasks and when they perform a changed task, but we cannot yet separate 'controlling' from 'controlled' regions.


title: Lack of inhibition of endothelial nitric oxide synthase in the isolated rat aorta by doxorubicin.

abstract: Besides inducing cardiotoxicity, doxorubicin also affects the vasculature. Recent observations in cultured endothelial cells indicated that the endothelial form of nitric oxide synthase might be inhibited by doxorubicin thereby seriously interfering with vascular function. We have investigated the effect of doxorubicin on the relaxation induced by the muscarinic agonist carbachol in the isolated rat aorta. It was found that doxorubicin at concentrations up to 50 microM does not alter the relaxant response to carbachol. Direct measurement of nitrite, the metabolite of NO*, by the Griess assay confirmed our observation that NO*)production is not inhibited by doxorubicin.


title: A Novel Popular Tourist Attraction Discovering Approach Based on Geo-Tagged Social Media Big Data

abstract: In the big data era, the social media data that contain users’ geographical locations are growing explosively. These kinds of spatiotemporal data provide a new perspective for us to observe the human movement behavior. By mining such spatiotemporal data, we can incorporate the users’ collective wisdom, build novel services and bring convenience to people. Through spatial clustering of the original user locations, both the ‘natural’ boundaries and the human activity information of the tourist attractions are generated, which facilitate performing popularity analysis of tourist attractions and extracting the travelers’ spatio-temporal patterns or travel laws. On the one hand, the potential extracted knowledge could provide decision supports to the tourism management department in both tourism planning and resource development; on the other hand, the travel preferences are able to be extracted from the clustering-generated attractions, and thus, intelligent tourism recommendation services could be developed for the tourist to promote the realization of ‘smart tourism’. Hence, this paper proposes a new method for discovering popular tourist attractions, which extracts hotspots through integrating spatial clustering and text mining approaches. We carry out tourist attraction discovery experiments based on the Flickr geotagged images within the urban area of Beijing from 2005 to 2016. The results show that compared with the traditional DBSCAN method, this novel approach can distinguish adjacent high-density areas when discovering popular tourist attractions and has better adaptability in the case of an uneven density distribution. In addition, based on the finding results of scenic hotspots, this paper analyzes the popularity distribution laws of Beijing’s tourist attractions under different temporal and weather contexts.


title: Vision-Based Front and Rear Surround Understanding Using Embedded Processors

abstract: Vision-based driver assistance systems involve a range of data-intensive operations, which pose challenges in implementing them as robust and real-time systems on resource constrained embedded computing platforms. In order to achieve both high accuracy and real-time performance, the constituent algorithms need to be designed and optimized such that they lend well for embedded realization. In this paper, we present a novel two-camera-based embedded driver assistance framework that analyzes the dynamics of vehicles in the front and rear surround views of the host vehicle (ego-vehicle). In order to do this, we propose a set of integrated techniques that combine contextual cues and lane information to detect vehicles that pose high threat to the ego-vehicle. The threat analysis is then used for generating a safe maneuver zone by the proposed system, which is implemented by using two Snapdragon 810 embedded CPUs. A detailed performance evaluation and tradeoff analysis is presented using a novel multiperspective dataset (DualCam Dataset) that is released as part of this paper. In terms of accuracy, the detailed evaluations show high robustness with true positive rates greater than 95% with less than 6% false alarm rate. The proposed embedded system operates at real-time frame rates in our testbed under real-world highway driving conditions. The proposed framework was presented as a live demonstration at the 2016 Consumer Electronics Show.


title: Adult Attachment Orientations, Stress, and Romantic Relationships

abstract: Abstract In this chapter, we discuss attachment theory and our programs of research on how individuals with different adult attachment orientations think, feel, and especially behave when they and their romantic partners encounter different types of stressful situations. In Section 2 , we review some basic principles of attachment theory, discuss what adult attachment orientations are, and summarize what they correlate with in the context of relationships. We also review how the different adult attachment orientations are associated with how individuals regulate negative emotions in threatening situations. In Section 3 , we discuss diathesis–stress thinking in attachment theory, and we introduce the general diathesis–stress process model that has guided most of our research on adult attachment, stress, and relationships during the past 20 years. In Section 4 , we review the various programs of attachment research we have conducted, which have tested how adults who have different attachment orientations cognitively, emotionally, and behaviorally react to different types of threat/stress vis-a-vis their romantic partners and relationships. We conclude the chapter by summarizing our diathesis–stress findings and by discussing promising directions for future research.


title: Triggered massive-star formation at the border of the HII region Sh 104

abstract: We present millimetre-line imaging of the Galactic H II region Sh 104. We show that it is surrounded by a ring of molecular gas and dust. Four large molecular condensations are regularly spaced around the ring. These condensations are themselves fragmented and contain several massive dense cores. A deeply embedded cluster is observed in the near IR towards the largest condensation. It contains at least one massive star ionizing an ultra-compact H II region. The Sh 104 region is a good illustration of the "collect and collapse" model for star formation triggered by the expansion of an H II region.


title: An extended Kalman filter for frequent local and infrequent global sensor data fusion

abstract: In this paper we compare the performance of a dead-reckoning system for robot navigation to a system using an extended Kalman filter (EKF). Dead-reckoning systems are able to approximate position and orientation by feeding data (provided usually by local sensors) to the kinematic model of the vehicle. These systems are subject to many different sources of error. EKFs have the ability to combine the same information and compensate for most of these errors to yield a better estimate. Our simulation results using a simplified kinematic model of Rocky 7 [an experimental rover used in the Mars exploration program at Jet Propulsion Laboratory (JPL)] show that an improvement in performance up to 40% (position error) can be achieved. The local sensors used are: wheel encoders, steering angle potentiometer and gyroscope. Involvement of global sensor measurements can drastically increase the accuracy of the estimate. The lack of GPS or magnetic field on Mars narrows our choices for global localization. Landmarks, such as the sun can be used as natural beacons (reference point for absolute measuremnts). A sun sensor (SS) that measures the absolute orientation of the rover has been built by Lockheed Martin and now is part of the sensor suite of Rocky 7. The SS measurement is crucial for the estimation filter and we show that the accuracy of the estimation decreases exponentially as the frequency of the SS data fed to the EKF decreases.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: PhishZoo: Detecting Phishing Websites by Looking at Them

abstract: Phishing is a security attack that involves obtaining sensitive or otherwise private data by presenting oneself as a trustworthy entity. Phishers often exploit users' trust on the appearance of a site by using web pages that are visually similar to an authentic site. This paper proposes a phishing detection approach -- PhishZoo -- that uses profiles of trusted websites' appearances to detect phishing. Our approach provides similar accuracy to blacklisting approaches (96%), with the advantage that it can classify zero-day phishing attacks and targeted attacks against smaller sites (such as corporate intranets). A key contribution of this paper is that it includes a performance analysis and a framework for making use of computer vision techniques in a practical way.


title: Monetary Cost Optimizations for Hosting Workflow-as-a-Service in IaaS Clouds

abstract: Recently, we have witnessed workflows from science and other data-intensive applications emerging on Infrastructure-as-a-Service (IaaS) clouds, and many workflow service providers offering workflow-as-a-service (WaaS). The major concern of WaaS providers is to minimize the monetary cost of executing workflows in the IaaS clouds. The selection of virtual machines (instances) types significantly affects the monetary cost and performance of running a workflow. Moreover, IaaS cloud environment is dynamic , with high performance dynamics caused by the interference from concurrent executions and price dynamics like spot prices offered by Amazon EC2. Therefore, we argue that WaaS providers should have the notion of offering probabilistic performance guarantees for individual workflows to explicitly expose the performance and cost dynamics of IaaS clouds to users. We develop a scheduling system called Dyna to minimize the expected monetary cost given the user-specified probabilistic deadline guarantees. Dyna includes an ${A^\star}$ -based instance configuration method for performance dynamics, and a hybrid instance configuration refinement for using spot instances. Experimental results with three scientific workflow applications on Amazon EC2 and a cloud simulator demonstrate (1) the ability of Dyna on satisfying the probabilistic deadline guarantees required by the users; (2) the effectiveness on reducing monetary cost in comparison with the existing approaches.


title: Comparative biodistribution of potential anti-glioblastoma conjugates [111In]DTPA-hEGF and [111In]Bz-DTPA-hEGF in normal mice.

abstract: EGF-receptors (EGFR) are overexpressed in gliomas, as well as in tumors of breast, lung, and urinary bladder. For this reason, EGFR may be an attractive target for both visualization and therapy of malignant tumors using radioactive nuclides. Natural ligand of EGFR, epidermal growth factor (EGF) is a small 53-amino-acid protein. Low molecular weight of EGF may enable better intratumoral penetration in comparison to antibodies. [111In]DTPA-EGF was proposed for the targeting of glioblastoma and breast cancer, and its tumor-seeking properties were confirmed in animal studies. The aim of this study was to evaluate how the substitution of heptadentate DTPA for octadentate benzyl-DTPA (Bz-DTPA) effects the biodistribution of indium-labeled human EGF (hEGF) in normal NMRI mice. [111In]DTPA-hEGF and [111In]Bz-DTPA-hEGF, obtained by the coupling of ITC-benzyl-DTPA to hEGF, were injected into the tail vein. At 0.5, 1, 4, and 24 hours postinjection, the animals were sacrificed, and radioactivity in different organs was measured. The blood clearance of both conjugates was fast. The uptake of both conjugates in the liver, spleen, stomach, pancreas, intestines, and submaxillary gland was most likely receptor-mediated. The uptake in a majority of organs was similar. However, indium uptake in the case of [111In]DTPA-hEGF was significantly higher in the kidneys and bones. In conclusion, [111In]Bz-DTPA-hEGF seems to have more favourable in vivo distribution in comparison to [111In]DTPA-hEGF.


title: Coverage-based search result diversification

abstract: Traditional retrieval models may provide users with less satisfactory search experience because documents are scored independently and the top ranked documents often contain excessively redundant information. Intuitively, it is more desirable to diversify search results so that the top-ranked documents can cover different query subtopics, i.e., different pieces of relevant information. In this paper, we study the problem of search result diversification in an optimization framework whose objective is to maximize a coverage-based diversity function. We first define the diversity score of a set of search results through measuring the coverage of query subtopics in the result set, and then discuss how to use them to derive diversification methods. The key challenge here is how to define an appropriate coverage function given a query and a set of search results. To address this challenge, we propose and systematically study three different strategies to define coverage functions. They are based on summations, loss functions and evaluation measures respectively. Each of these coverage functions leads to a result diversification method. We show that the proposed coverage based diversification methods not only cover several state-of-the-art methods but also allows us to derive new ones. We compare these methods both analytically and empirically. Experiment results on two standard TREC collections show that all the methods are effective for diversification and the new methods can outperform existing ones.


title: Extensive Facial Landmark Localization with Coarse-to-Fine Convolutional Network Cascade

abstract: We present a new approach to localize extensive facial landmarks with a coarse-to-fine convolutional network cascade. Deep convolutional neural networks (DCNN) have been successfully utilized in facial landmark localization for two-fold advantages: 1) geometric constraints among facial points are implicitly utilized, 2) huge amount of training data can be leveraged. However, in the task of extensive facial landmark localization, a large number of facial landmarks (more than 50 points) are required to be located in a unified system, which poses great difficulty in the structure design and training process of traditional convolutional networks. In this paper, we design a four-level convolutional network cascade, which tackles the problem in a coarse-to-fine manner. In our system, each network level is trained to locally refine a subset of facial landmarks generated by previous network levels. In addition, each level predicts explicit geometric constraints (the position and rotation angles of a specific facial component) to rectify the inputs of the current network level. The combination of coarse-to-fine cascade and geometric refinement enables our system to locate extensive facial landmarks (68 points) accurately in the 300-W facial landmark localization challenge.


title: Secure multi-protocol gateway for Internet of Things

abstract: The diversity of applications and protocols is a big challenge for the Internet of Things (IoT) devices to communicate in collecting data or exchanging messages without a hitch of protocol differences. In addition, security features are required to secure data transmitted between the end devices and the server. In this study, we developed a secure multiprotocol gateway that supports for RF, ZigBee, BLE, and Ethernet protocols and utilizes TCP / IP protocols for advanced data processing after the gateway exit. Additionally, we added a data encryption algorithm for data security transmitted through the proposed gateway. To display the data the server receives, we added a dashboard to the gateway. The proposed multiprotocol gateway has been simulated using testbed environment in which the transmitted data was encrypted by default. The results showed that WiFi has the highest throughput compared to BLE and XBee, while the transmission delay tends to increase for all protocols but WiFI has the lowest delay compared to XBee and BLE.


title: Age Differences in Sensation Seeking and Impulsivity as Indexed by Behavior and Self-Report: Evidence for a Dual Systems Model.

abstract: It has been hypothesized that sensation seeking and impulsivity, which are often conflated, in fact develop along different timetables and have different neural underpinnings, and that the difference in their timetables helps account for heightened risk taking during adolescence. In order to test these propositions, the authors examined age differences in sensation seeking and impulsivity in a socioeconomically and ethnically diverse sample of 935 individuals between the ages of 10 and 30, using self-report and behavioral measures of each construct. Consistent with the authors' predictions, age differences in sensation seeking, which are linked to pubertal maturation, follow a curvilinear pattern, with sensation seeking increasing between 10 and 15 and declining or remaining stable thereafter. In contrast, age differences in impulsivity, which are unrelated to puberty, follow a linear pattern, with impulsivity declining steadily from age 10 on. Heightened vulnerability to risk taking in middle adolescence may be due to the combination of relatively higher inclinations to seek excitement and relatively immature capacities for self-control that are typical of this period of development.


title: Shamela: A Large-Scale Historical Arabic Corpus

abstract: Arabic is a widely-spoken language with a rich and long history spanning more than fourteen centuries. Yet existing Arabic corpora largely focus on the modern period or lack sufficient diachronic information. We develop a large-scale, historical corpus of Arabic of about 1 billion words from diverse periods of time. We clean this corpus, process it with a morphological analyzer, and enhance it by detecting parallel passages and automatically dating undated texts. We demonstrate its utility with selected case-studies in which we show its application to the digital humanities.


title: Advanced universal remote controller for home automation and security

abstract: There has been inconvenience in controlling each digital home appliance which requires its own remote controller. In this paper, we present an advanced universal remote controller (URC) with the total solution for home automation and security. All kinds of home appliances can be controlled with the URC, which can be also connected to a PC dealing with Internet as well. To use the URC, we need several receivers with wired or wireless communication methods to be connected to all appliances. The receivers have many channels and IDs to control many appliances at the same time and to support multi-zone services. In addition, we propose a PC-based interface for end-users to use the URC conveniently. With the proposed URC, we can easily construct a ubiquitous home automation and security environment with the total solution. Furthermore, this solution can be applied to the automated control of all kinds of appliances installed within buildings for companies, schools, hospitals, and so on.


title: Competitive Analysis System for Theatrical Movie Releases Based on Movie Trailer Deep Video Representation

abstract: Audience discovery is an important activity at major movie studios. Deep models that use convolutional networks to extract frame-by-frame features of a movie trailer and represent it in a form that is suitable for prediction are now possible thanks to the availability of pre-built feature extractors trained on large image datasets. Using these pre-built feature extractors, we are able to process hundreds of publicly available movie trailers, extract frame-by-frame low level features (e.g., a face, an object, etc) and create video-level representations. We use the video-level representations to train a hybrid Collaborative Filtering model that combines video features with historical movie attendance records. The trained model not only makes accurate attendance and audience prediction for existing movies, but also successfully profiles new movies six to eight months prior to their release.


title: Understanding VAEs in Fisher-Shannon Plane

abstract: In information theory, Fisher information and Shannon information (entropy) are respectively used to quantify the uncertainty associated with the distribution modeling and the uncertainty in specifying the outcome of given variables. These two quantities are complementary and are jointly applied to information behavior analysis in most cases. The uncertainty property in information asserts a fundamental trade-off between Fisher information and Shannon information, which enlightens us the relationship between the encoder and the decoder in variational auto-encoders (VAEs). In this paper, we investigate VAEs in the Fisher-Shannon plane, and demonstrate that the representation learning and the log-likelihood estimation are intrinsically related to these two information quantities. Through extensive qualitative and quantitative experiments, we provide with a better comprehension of VAEs in tasks such as high-resolution reconstruction, and representation learning in the perspective of Fisher information and Shannon information. We further propose a variant of VAEs, termed as Fisher auto-encoder (FAE), for practical needs to balance Fisher information and Shannon information. Our experimental results have demonstrated its promise in improving the reconstruction accuracy and avoiding the noninformative latent code as occurred in previous works.


title: A hybrid framework combining background subtraction and deep neural networks for rapid person detection

abstract: Currently, the number of surveillance cameras is rapidly increasing responding to security issues. But constructing an intelligent detection system is not easy because it needs high computing performance. This study aims to construct a real-world video surveillance system that can effectively detect moving person using limited resources. To this end, we propose a simple framework to detect and recognize moving objects using outdoor CCTV video footages by combining background subtraction and Convolutional Neural Networks (CNNs). A background subtraction algorithm is first applied to each video frame to find the regions of interest (ROIs). A CNN classification is then carried out to classify the obtained ROIs into one of the predefined classes. Our approach much reduces the computation complexity in comparison to other object detection algorithms. For the experiments, new datasets are constructed by filming alleys and playgrounds, places where crimes are likely to occur. Different image sizes and experimental settings are tested to construct the best classifier for detecting people. The best classification accuracy of 0.85 was obtained for a test set from the same camera with training set and 0.82 with different cameras.


DESIGN AND DEVELOPMENT OF ADJUSTABLE STAIR CLIMBING ROBOT


title: Exploratory search: from finding to understanding

abstract: Research tools critical for exploratory search success involve the creation of new interfaces that move the process beyond predictable fact retrieval.


title: Using Different Cost Functions to Train Stacked Auto-Encoders

abstract: Deep neural networks comprise several hidden layers of units, which can be pre-trained one at a time via an unsupervised greedy approach. A whole network can then be trained (fine-tuned) in a supervised fashion. One possible pre-training strategy is to regard each hidden layer in the network as the input layer of an auto-encoder. Since auto-encoders aim to reconstruct their own input, their training must be based on some cost function capable of measuring reconstruction performance. Similarly, the supervised fine-tuning of a deep network needs to be based on some cost function that reflects prediction performance. In this work we compare different combinations of cost functions in terms of their impact on layer-wise reconstruction performance and on supervised classification performance of deep networks. We employed two classic functions, namely the cross-entropy (CE) cost and the sum of squared errors (SSE), as well as the exponential (EXP) cost, inspired by the error entropy concept. Our results were based on a number of artificial and real-world data sets.


title: Dual Use of Mobile IT and Work-to-Life Conflict in the Context of IT Consumerization

abstract: Using the same technologies for both work and private life is an intensifying phenomenon. Mostly driven by the availability of consumer IT in the marketplace, individuals—more often than not—are tempted to use privately-owned IT rather than enterprise IT in order to get their job done. However, this dual-use of technologies comes at a price. It intensifies the blurring of the boundaries between work and private life—a development in stark contrast to the widely spread desire of employees to segment more clearly between their two lives. If employees cannot follow their segmentation preference, it is proposed that this misfit will result in work-to-life conflict (WtLC). This paper investigates the relationship between organizational encouragement for dual use and WtLC. Via a quantitative survey, we find a significant relationship between the two concepts. In line with boundary theory, the effect is stronger for people that strive for work-life segmentation.


title: A 0.2–0.6 V ring oscillator design using bootstrap technique

abstract: This paper presents a bootstrapped inverter based ring oscillator for 0.2–0.6V operation. The proposed delay cell provides a boosted voltage swing to enhance the driving capability and suppress the large process variation in the low-voltage region. As compared with conventional inverter-based ring oscillators, the proposed one provides better linearity when controlled by the supply voltage. A test chip is fabricated in 90 nm SPRVT Low-K CMOS process. The measured results demonstrate that the proposed bootstrapped ring oscillator oscillates at 48MHz (771MHz) with a power consumption of at 0.63μW (87.6μw) and a phase noise of −93dBc/Hz (−88.5dBc/Hz) at a 1-MHz offset under a supply voltage of 0.2V (0.6V) V DD .


title: Community Detection in Temporal Networks

abstract: Many complex systems in nature, society and technologyfrom the online social networks to the internet from the nervous system to power gridscan be represented as a graph of vertices interconnected by edges. Small world network, Scale free network, Community detection are fundamental properties of complex networks. Community is a sub graph with densely connected nodes typically reveals topological relations, network structure, organizational and functional characteristics of the underlying network, e.g., friendships on Facebook, Followers of a VIP account on Twitter, and interaction with business professionals on LinkedIn. Online social networks are dynamic in nature, they evolve in size and space rapidly. In this paper we are detecting incremental disjoint communities using dyanamic multi label propagation algorithm. It tackles the temporal event changes, i.e., addition, deletion of an edge or vertex in a sub graph for every timestamp. The experimental results on Enron real network dataset shows that the proposed method is quite well in identifying communities.


title: User Authentication from Mouse Movement Data Using Multiple Classifiers

abstract: The biometric authentication is becoming very familiar due to its unique characteristics. This paper presents a user authentication system using mouse movement data. The mouse movement data are captured using an existing tool named Jitbit macro recorder. The acquired data are preprocessed and sampled into N blocks based on specific number of actions and stored in database. From each block twelve features are generated: Number of Points in the trajectory, Delay Time, Number of Delay, Number of Action, STDEV of Trajectory Length, Total Length of Trajectory, STDEV of Slope, STDEV of Slope to Slope Difference, Number of Curvatures, Curvature of Trajectory, Number of Changes in Horizontal Position and Number of Changes in Vertical Position. This system uses three separate classifiers: SVM, K-Nearest Neighbor and Naive Bayes to recognize the user. The system is trained and tested using a benchmark data. The experimental result shows that K-nearest Neighbor has the lowest error rate.


title: Image-based visual navigation for mobile robots

abstract: We introduce a new image-based visual navigation algorithm that allows the Cartesian velocity of a robot to be defined with respect to a set of visually observed features corresponding to previously unseen and unmapped world points. The technique is well suited to mobile robot tasks such as moving along a road or flying over the ground. We describe the algorithm in general form and present detailed simulation results for an aerial robot scenario using a spherical camera and a wide angle perspective camera, and present experimental results for a mobile ground robot.


title: Depth kernel descriptors for object recognition

abstract: Consumer depth cameras, such as the Microsoft Kinect, are capable of providing frames of dense depth values at real time. One fundamental question in utilizing depth cameras is how to best extract features from depth frames. Motivated by local descriptors on images, in particular kernel descriptors, we develop a set of kernel features on depth images that model size, 3D shape, and depth edges in a single framework. Through extensive experiments on object recognition, we show that (1) our local features capture different aspects of cues from a depth frame/view that complement one another; (2) our kernel features significantly outperform traditional 3D features (e.g. Spin images); and (3) we significantly improve the capabilities of depth and RGB-D (color+depth) recognition, achieving 10–15% improvement in accuracy over the state of the art.


title: EAST: An Efficient and Accurate Scene Text Detector

abstract: Previous approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging scenarios, even when equipped with deep neural network models, because the overall performance is determined by the interplay of multiple stages and components in the pipelines. In this work, we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes. The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images, eliminating unnecessary intermediate steps (e.g., candidate aggregation and word partitioning), with a single neural network. The simplicity of our pipeline allows concentrating efforts on designing loss functions and neural network architecture. Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500 demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR 2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps at 720p resolution.


title: A method for calibrating see-through head-mounted displays for AR

abstract: In order to have a working augmented reality (AR) system, the see-through system must be calibrated such that the internal models of objects match their physical counterparts. By match, we mean they should have the same position, orientation, and size information as well as any intrinsic parameters (such as focal lengths in the case of cameras) that their physical counterparts have. To this end, a procedure must be developed which estimates the parameters of these internal models. This calibration method must be both accurate and simple to use. This paper reports on our efforts to implement a calibration method for a see-through head-mounted display. We use a dynamic system in which a user interactively modifies the camera parameters until the image of a calibration object matches the image of a corresponding physical object. The calibration method is dynamic in the sense that we do not require the user's head to be immobilized.


title: I'll be there for you: Quantifying Attentiveness towards Mobile Messaging

abstract: Social norm has it that people are expected to respond to mobile phone messages quickly. We investigate how attentive people really are and how timely they actually check and triage new messages throughout the day. By collecting more than 55,000 messages from 42 mobile phone users over the course of two weeks, we were able to predict people's attentiveness through their mobile phone usage with close to 80% accuracy. We found that people were attentive to messages 12.1 hours a day, i.e. 84.8 hours per week, and provide statistical evidence how very short people's inattentiveness lasts: in 75% of the cases mobile phone users return to their attentive state within 5 minutes. In this paper, we present a comprehensive analysis of attentiveness throughout each hour of the day and show that intelligent notification delivery services, such as bounded deferral, can assume that inattentiveness will be rare and subside quickly.


title: Matrix and Tensor Decomposition in Recommender Systems

abstract: This turorial offers a rich blend of theory and practice regarding dimensionality reduction methods, to address the information overload problem in recommender systems. This problem affects our everyday experience while searching for knowledge on a topic. Naive Collaborative Filtering cannot deal with challenging issues such as scalability, noise, and sparsity. We can deal with all the aforementioned challenges by applying matrix and tensor decomposition methods. These methods have been proven to be the most accurate (i.e., Netflix prize) and efficient for handling big data. For each method (SVD, SVD++, timeSVD++, HOSVD, CUR, etc.) we will provide a detailed theoretical mathematical background and a step-by-step analysis, by using an integrated toy example, which runs throughout all parts of the tutorial, helping the audience to understand clearly the differences among factorisation methods.


title: A Compact UWB Antenna for On-Body Applications

abstract: A new compact planar ultrawideband (UWB) antenna designed for on-body communications is presented. The antenna is characterized in free space, on a homogeneous phantom modeling a human arm, and on a realistic high-resolution whole-body voxel model. In all configurations it demonstrates very satisfactory features for on-body propagation. The results are presented in terms of return loss, radiation pattern, efficiency, and E-field distribution. The antenna shows very good performance within the 3-11.2 GHz range, and therefore it might be used successfully for the 3.1-10.6 GHz IR-UWB systems. The simulation results for the return loss and radiation patterns are in good agreement with measurements. Finally, a time-domain analysis over the whole-body voxel model is performed for impulse radio applications, and transmission scenarios with several antennas placed on the body are analyzed and compared.


The Bifurcated E-Plane T-Junction and Its Application to Waveguide Diplexer Design


title: An obstacle signaling system for the blind

abstract: Many commercial navigation aids suffer from a number of design flaws, the most important of which are related to the human interface that conveys information to the user. Aids for the visually impaired are lightweight electronic devices that are either incorporated into a long cane, hand-held or worn by the client, warning of hazards ahead. Most aids use vibrating buttons or sound alerts to warn of upcoming obstacles, a method which is only capable of conveying very crude information regarding direction and proximity to the nearest object. Some of the more sophisticated devices use a complex audio interface in order to deliver more detailed information, but this often compromises the user's hearing, a critical impairment for a blind user. The author has produced an original design and working prototype solution which is a major first step in addressing some of these faults found in current production models for the blind.


title: Social media interventions for diet and exercise behaviours: a systematic review and meta-analysis of randomised controlled trials

abstract: Objectives To conduct a systematic review of randomised controlled trials (RCTs) examining the use of social media to promote healthy diet and exercise in the general population. Data sources MEDLINE, CENTRAL, ERIC, PubMed, CINAHL, Academic Search Complete, Alt Health Watch, Health Source, Communication and Mass Media Complete, Web of Knowledge and ProQuest Dissertation and Thesis (2000–2013). Study eligibility criteria RCTs of social media interventions promoting healthy diet and exercise behaviours in the general population were eligible. Interventions using social media, alone or as part of a complex intervention, were included. Study appraisal and synthesis Study quality was assessed using the Cochrane Risk of Bias Tool. We describe the studies according to the target populations, objectives and nature of interventions, outcomes examined, and results and conclusions. We extracted data on the primary and secondary outcomes examined in each study. Where the same outcome was assessed in at least three studies, we combined data in a meta-analysis. Results 22 studies were included. Participants were typically middle-aged Caucasian women of mid-to-high socioeconomic status. There were a variety of interventions, comparison groups and outcomes. All studies showed a decrease in programme usage throughout the intervention period. Overall, no significant differences were found for primary outcomes which varied across studies. Meta-analysis showed no significant differences in changes in physical activity (standardised mean difference (SMD) 0.13 (95% CI −0.04 to 0.30), 12 studies) and weight (SMD −0.00 (95% CI −0.19 to 0.19), 10 studies); however, pooled results from five studies showed a significant decrease in dietary fat consumption with social media (SMD −0.35 (95% CI −0.68 to −0.02)). Conclusions Social media may provide certain advantages for public health interventions; however, studies of social media interventions to date relating to healthy lifestyles tend to show low levels of participation and do not show significant differences between groups in key outcomes.


Trends in embedded systems Opportunities and challenges for System-on-Chip and Networked Embedded Systems technologies in industrial automation


title: Sensor fusion for occupancy detection and activity recognition using time-of-flight sensors

abstract: New technologies in lighting enable the design of illumination systems that autonomously meet the needs of occupants. In recent years, lighting design for both commercial and residential spaces has advanced beyond task performance, to a broader range of occupant needs including economic, energy and environmental constraints, architectural integration, human health, productivity, interpersonal communication, and aesthetic quality. Such systems require distributed sensing and control systems with sensory feedback to detect the lighting conditions in the space. Occupancy sensing and activity recognition are core components of this distributed sensor-based control system, and multisensor fusion of integrated sensors is viewed as a key attribute. This paper describes an expanded view of multisensor technologies, signal processing and pattern recognition algorithms that are being developed and evaluated for occupancy detection and activity recognition. This paper presents a multisensor testbed system that incorporates a sparse array of time-of-flight range sensors with pattern recognition algorithms for geometric form and motion detection of human occupants. Three levels of analysis are implemented: (1) Occupant detection and tracking, (2) Occupant pose classification (sitting, standing, and walking), (3) Occupant activity sequence recognition. These algorithms are dependent on statistical training of human pose and motion and implemented with a Bayesian formulation for detection, classification, and recognition. Evaluation of resulting performance in the testbed conference room demonstrates pose and activity recognition accuracy of greater than 97% for single occupants.


title: Determination of the complex permittivity of packaging materials at millimeter-wave frequencies

abstract: The focus of this paper is the determination of the complex permittivity of chip packaging materials at millimeter-wave frequencies. After a broad overview of existing measurement techniques, three methods will be presented that have been established for the dielectric property determination of substrate, as well as mold materials (encapsulants, under-fill, etc.) in the millimeter-wave frequency range. First, the open resonator used here will be briefly described. It allows accurate determination of the dielectric constant and loss of thin sheet substrate materials from below 20 GHz to above 100 GHz. Second, a filled waveguide method is explained in detail. The setup used here can determine the complex dielectric properties of mold materials from 70 to 100 GHz. Third, the method based on covered transmission lines will be described in detail. The used lines allow measurements from below 40 GHz to approximately 90 GHz. Verification of all three methods will be provided by inter-comparison and comparison to values from the literature. Additionally, results for several typical substrate and mold materials that are available for millimeter-wave packaging will be shown and discussed.


title: Cumulative trauma: The impact of child sexual abuse, adult sexual assault, and spouse abuse

abstract: The present study investigated the relationship between trauma symptoms and a history of child sexual abuse, adult sexual assault, and physical abuse by a partner as an adult. While there has been some research examining the correlation between individual victimization experiences and traumatic stress, the cumulative impact of multiple victimization experiences has not been addressed. Subjects were recruited from psychological clinics and community advocacy agencies. Additionally, a nonclinical undergraduate student sample was evaluated. The results of this study indicate not only that victimization and revictimization experiences are frequent, but also that the level of trauma specific symptoms are significantly related to the number of different types of reported victimization experiences. The research and clinical implications of these findings are discussed.


Integer Programming and Heuristic Approaches for a Multi-Stage Nurse Rostering Problem


title: Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis

abstract: We have developed a novel approach using source analysis for classifying motor imagery tasks. Two-equivalent-dipoles analysis was proposed to aid classification of motor imagery tasks for brain-computer interface (BCI) applications. By solving the electroencephalography (EEG) inverse problem of single trial data, it is found that the source analysis approach can aid classification of motor imagination of left- or right-hand movement without training. In four human subjects, an averaged accuracy of classification of 80% was achieved. The present study suggests the merits and feasibility of applying EEG inverse solutions to BCI applications from noninvasive EEG recordings.


title: Control scheme and networked control architecture for the Berkeley lower extremity exoskeleton (BLEEX)

abstract: The Berkeley lower extremity exoskeleton (BLEEX) is a load-carrying and energetically autonomous human exoskeleton that, in this first generation prototype, carries up to a 34 kg (75 Ib) payload for the pilot and allows the pilot to walk at up to 1.3 m/s (2.9 mph). This article focuses on the human-in-the-loop control scheme and the novel ring-based networked control architecture (ExoNET) that together enable BLEEX to support payload while safely moving in concert with the human pilot. The BLEEX sensitivity amplification control algorithm proposed here increases the closed loop system sensitivity to its wearer's forces and torques without any measurement from the wearer (such as force, position, or electromyogram signal). The tradeoffs between not having sensors to measure human variables, the need for dynamic model accuracy, and robustness to parameter uncertainty are described. ExoNET provides the physical network on which the BLEEX control algorithm runs. The ExoNET control network guarantees strict determinism, optimized data transfer for small data sizes, and flexibility in configuration. Its features and application on BLEEX are described


title: Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches

abstract: A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent component analysis (ICA). We estimated voxel-wise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal–parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust—yet frequently ignored—neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity.


title: NotifiVR: Exploring Interruptions and Notifications in Virtual Reality

abstract: The proliferation of high resolution and affordable virtual reality (VR) headsets is quickly making room-scale VR experiences available in our homes. Most VR experiences strive to achieve complete immersion by creating a disconnect from the real world. However, due to the lack of a standardized notification management system and minimal context awareness in VR, an immersed user may face certain situations such as missing an important phone call (digital scenario), tripping over wandering pets (physical scenario), or losing track of time (temporal scenario). In this paper, we present the results of 1) a survey across 61 VR users to understand common interruptions and scenarios that would benefit from some form of notifications; 2) a design exercise with VR professionals to explore possible notification methods; and 3) an empirical study on the noticeability and perception of 5 different VR interruption scenarios across 6 modality combinations (e.g., audio, visual, haptic, audio + haptic, visual + haptic, and audio + visual) implemented in Unity and presented using the HTC Vive headset. Finally, we combine key learnings from each of these steps along with participant feedback to present a set of observations and recommendations for notification design in VR.