title: Rare Congenital Coagulation Factor Deficiencies: Clinical Manifestations

abstract: The clinical symptoms of rare bleeding disorders (RBDs) range from mucosal to life-threatening haemorrhages or functionally restricting haemorrhages. Congenital coagulation factor (F) deficiencies, VII, FX, FXIII, fibrinogen deficiencies and dysfibrinogenaemias, represent 3‒5% of all inherited coagulation factor deficiencies. Due to this low prevalence and the variability of bleeding manifestations, the clinical features of these RBDs are not well characterised. For this reason we conducted a retrospective multicentre national survey using patient records to characterize clinical bleeding manifestations in patients with congenital isolated/combined coagulation factor deficiencies or with congenital abnormalities of fibrinogen. Fifteen departments (11 haematology and four paediatric departments) across Algeria took part in the study, and data from 234 patients were analyzed. The majority of patients (n=164; 70.1%) had isolated congenital coagulation factor deficiencies, the most common of which was FVII deficiency (n=101; 43.2%). Only 221 patients (9.0%) had combined congenital coagulation deficiencies, the most common of which was FVIII/V deficiency (n=13; 5.6%). Congenital fibrinogen abnormalities were reported in 49 patients (20.9%). Overall, there were 327 bleeding episodes in 163 patients. The majority of bleeding episodes (n=195, 59.6%) were in patients with an isolated factor deficiency, the highest incidence of which was recorded for FVII deficient patients (95 bleeds; mean 1.7 bleeds/patient). Our results support existing descriptions in the literature and may help to target resourcing and plan treatment strategies for those patients at most risk of bleeding.


title: Design of miniaturized ultra-wideband Quasi-Yagi antenna

abstract: A novel Quasi-Yagi antenna was proposed with ultra-wideband and miniaturization characteristics. The butterfly dipoles and parasitic branches are adopting to improve the bandwidth of the Quasi-Yagi antenna. Affections of antenna structure parameters on antenna was analyzed in this paper. After optimization, the operating frequency ranges from 3.3 to 10.2 GHz. Meanwhile, gain is more than 5.1 dB, and the maximum is 9.8 dB.


title: Improving Long-Horizon Forecasts with Expectation-Biased LSTM Networks

abstract: State-of-the-art forecasting methods using Recurrent Neural Net- works (RNN) based on Long-Short Term Memory (LSTM) cells have shown exceptional performance targeting short-horizon forecasts, e.g given a set of predictor features, forecast a target value for the next few time steps in the future. However, in many applica- tions, the performance of these methods decays as the forecasting horizon extends beyond these few time steps. This paper aims to explore the challenges of long-horizon forecasting using LSTM networks. Here, we illustrate the long-horizon forecasting problem in datasets from neuroscience and energy supply management. We then propose expectation-biasing, an approach motivated by the literature of Dynamic Belief Networks, as a solution to improve long-horizon forecasting using LSTMs. We propose two LSTM ar- chitectures along with two methods for expectation biasing that significantly outperforms standard practice.


title: Will robots know us better than we know ourselves

abstract: Abstract This paper aims to highlight some conceptual aspects on the impact of robotics on our concept of privacy. In those areas where robotics applications will invade the privacy of individuals as computers or mobile phones do today, the current idea of privacy will no longer suffice to ensure the right level of people’s protection. If we think to answer or stop the forthcoming controversies only relying on self-regulation of private parties, we will escape the real challenge: the next generation of robots does not affect solely persons and their individual rights, but the entire structure of society. This article assumes the robotics–privacy relationship as a clear illustration of how the technology–society nexus should be regulated in the future. We need approaches that are contextual–normativeand that should be politically addressed to the creation of a critical culture of technology.


title: Realistic Dynamic Facial Textures from a Single Image Using GANs

abstract: We present a novel method to realistically puppeteer and animate a face from a single RGB image using a source video sequence. We begin by fitting a multilinear PCA model to obtain the 3D geometry and a single texture of the target face. In order for the animation to be realistic, however, we need dynamic per-frame textures that capture subtle wrinkles and deformations corresponding to the animated facial expressions. This problem is highly undercon-strained, as dynamic textures cannot be obtained directly from a single image. Furthermore, if the target face has a closed mouth, it is not possible to obtain actual images of the mouth interior. To address this issue, we train a Deep Generative Network that can infer realistic per-frame texture deformations, including the mouth interior, of the target identity using the per-frame source textures and the single target texture. By retargeting the PCA expression geometry from the source, as well as using the newly inferred texture, we can both animate the face and perform video face replacement on the source video using the target appearance.


title: GaN laser driver switching 30 A in the sub-nanosecond range

abstract: A GaN-HEMT-based laser driver circuit is presented which can switch 30 A of current with a minimum pulse width of 580 ps. The pulse width can be varied electronically. Its maximum depends only on the value of the storage capacitor. This demonstrates the potential of GaN transistors also for high-speed high-current applications at low voltages. The driver is integrated together with the laser diode on a submount following a special scheme, which is essential to keep parasitic inductance low.


title: Simulation of stray currents on single track in Matlab Simulink

abstract: This article deals with the simulation of the line segment with a length of 3km and with origin of stray current in its vicinity. The simulation model is created using a line that is at the ends supply by sources with voltage of 3.3kV. These sources are realized by bridge rectifier with transformers that regulate a voltage level. On this line is simulated start and stop of DC motor, which represent a traction motor. During operating the motor electric current flows into the rail, which part of it flows into the ground and flows through the individual ground layers. Ground layer are inserted into subsystems in the simulation to better orientation in the entire simulation model. Individual results of these subsystems are shown in the graphs. Sizes of ground currents are mainly influenced by their own resistance and capacity of earth structures.


title: Multi-robot localization in a landmark-free environment using binaural sonar

abstract: It's interesting for a collaborative group of mobile robots to be able to identify and localize each other without referring to external beacons. In this context, we are able to develop a localizer model by combining an extended Kalman filter with an ultrasonic sensing model and synchronize them by a communication protocol among mobile agents. The approach we present in this paper consists of embedding on each agent traditional encoders in order to estimate their motion. Then, the EKF is used to correct the position estimate by a collective data fusion technique. The information required here consists of the relative pose between a pair of vehicles. It's an exteroceptive sensor, called binaural sonar, that allows to perform the observation. All along the localization process, these mobile agents share collected data and notify each other when new events procure. This paper also presents simulation results for this approach when applied to non-holonomic constraints mobile robots without using external tools or centralized computational units.


title: Desynchronization: Synthesis of Asynchronous Circuits From Synchronous Specifications

abstract: Asynchronous implementation techniques, which measure logic delays at runtime and activate registers accordingly, are inherently more robust than their synchronous counterparts, which estimate worst case delays at design time and constrain the clock cycle accordingly. Desynchronization is a new paradigm to automate the design of asynchronous circuits from synchronous specifications, thus, permitting widespread adoption of asynchronicity without requiring special design skills or tools. In this paper, different protocols for desynchronization are first studied, and their correctness is formally proven using techniques originally developed for distributed deployment of synchronous language specifications. A taxonomy of existing protocols for asynchronous latch controllers, covering, in particular, the four-phase handshake protocols devised in the literature for micropipelines, is also provided. A new controller that exhibits provably maximal concurrency is then proposed, and the performance of desynchronized circuits is analyzed with respect to the original synchronous optimized implementation. Finally, this paper proves the feasibility and effectiveness of the proposed approach by showing its application to a set of real designs, including a complete implementation of the DLX microprocessor architecture


title: Channel-Level Acceleration of Deep Face Representations

abstract: A major challenge in biometrics is performing the test at the client side, where hardware resources are often limited. Deep learning approaches pose a unique challenge: while such architectures dominate the field of face recognition with regard to accuracy, they require elaborate, multi-stage computations. Recently, there has been some work on compressing networks for the purpose of reducing run time and network size. However, it is not clear that these compression methods would work in deep face nets, which are, generally speaking, less redundant than the object recognition networks, i.e., they are already relatively lean. We propose two novel methods for compression: one based on eliminating lowly active channels and the other on coupling pruning with repeated use of already computed elements. Pruning of entire channels is an appealing idea, since it leads to direct saving in run time in almost every reasonable architecture.


title: Paraphrase and Textual Entailment Generation

abstract: One particular information can be conveyed by many different sentences. This variety concerns the choice of vocabulary and style as well as the level of detail (from laconism or succinctness to total verbosity). Although verbosity in written texts is considered bad style, generated verbosity can help natural language processing (NLP) systems to fill in the implicit knowledge.


title: Comprehensive Analyses and Comparison of 1 kW Isolated DC–DC Converters for Bidirectional EV Charging Systems

abstract: Isolated dc–dc converters with galvanic isolation are commonly used in electric vehicle (EV) battery chargers. These converters interface between a dc voltage link, which is usually the output of a power factor correction stage, and an energy storage unit. CLLC and dual active bridge (DAB) dc–dc converters can achieve high power density, high-energy efficiency, wide gain range, galvanic isolation, and bidirectional power flow, and therefore, have potential applications as dc–dc converters for bidirectional EV charging systems. In this paper, full-bridge CLLC, half-bridge CLLC, full-bridge DAB, and half-bridge DAB dc–dc converters are evaluated and compared for their suitability for EV chargers. All the converters are designed with optimal soft-switching features. The operating principles, design methodologies, and design considerations are presented. Prototypes of the converters with power rating of 1 kW are designed and developed. The prototypes interface a 500 V dc link and a 200–420 V load, which is common for EV applications. The performances of the circuits are analyzed and a comprehensive comparison is conducted.


title: Design and Development of Floor Cleaner Robot (Automatic and Manual)

abstract: Manual work is taken over the robot technology and many of the related robot appliances are being used extensively also. Here represents the technology that proposed the working of robot for Floor cleaning. This floor cleaner robot can work in any of two modes i.e. “Automatic and Manual”. All hardware and software operations are controlled by AT89S52 microcontroller. This robot can perform sweeping and mopping task. RF modules have been used for wireless communication between remote (manual mode) and robot and having range 50m. This robot is incorporated with IR sensor for obstacle detection and automatic water sprayer pump. Four motors are used, two for cleaning, one for water pump and one for wheels. Dual relay circuit used to drive the motors one for water pump and another for cleaner. In previous work, there was no automatic water sprayer used and works only in automatic mode. In the automatic mode robot control all the operations itself and change the lane in case of hurdle detection and moves back. In the manual mode, the keypad is used to perform the expected task and to operate robot. In manual mode, RF module has been used to transmit and receive the information between remote and robot and display the information related to the hurdle detection on LCD. The whole circuitry is connected with 12V battery.


title: A Neural Network Degradation Model for Computing and Updating Residual Life Distributions

abstract: The ability to accurately estimate the residual life of partially degraded components is arguably the most challenging problem in prognostic condition monitoring. This paper focuses on the development of a neural network-based degradation model that utilizes condition-based sensory signals to compute and continuously update residual life distributions of partially degraded components. Initial predicted failure times are estimated through trained neural networks using real-time sensory signals. These estimates are used to derive a prior failure time distribution for the component that is being monitored. Subsequent failure time estimates are then utilized to update the prior distributions using a Bayesian approach. The proposed methodology is tested using real world vibration-based degradation signals from rolling contact thrust bearings. The proposed methodology performed favorably when compared to other reliability-based and statistical-based benchmarks.


title: Toward Air Traffic Complexity Assessment in New Generation Air Traffic Management Systems

abstract: The characterization of complex air traffic situations is an important issue in air traffic management (ATM). Within the current ground-based ATM system, complexity metrics have been introduced with the goal of evaluating the difficulty experienced by air traffic controllers in guaranteeing the appropriate aircraft separation in a sector. The rapid increase in air travel demand calls for new generation ATM systems that can safely and efficiently handle higher levels of traffic. To this purpose, part of the responsibility for separation maintenance will be delegated to the aircraft, and trajectory management functions will be further automated and distributed. The evolution toward an autonomous aircraft framework envisages new tasks where assessing complexity may be valuable and requires a whole new perspective in the definition of suitable complexity metrics. This paper presents a critical analysis of the existing approaches for modeling and predicting air traffic complexity, examining their portability to autonomous ATM systems. Possible applications and related requirements will be discussed.


title: Biharmonic diffusion curve images from boundary elements

abstract: There is currently significant interest in freeform, curve-based authoring of graphic images. In particular, "diffusion curves" facilitate graphic image creation by allowing an image designer to specify naturalistic images by drawing curves and setting colour values along either side of those curves. Recently, extensions to diffusion curves based on the biharmonic equation have been proposed which provide smooth interpolation through specified colour values and allow image designers to specify colour gradient constraints at curves. We present a Boundary Element Method (BEM) for rendering diffusion curve images with smooth interpolation and gradient constraints, which generates a solved boundary element image representation. The diffusion curve image can be evaluated from the solved representation using a novel and efficient line-by-line approach. We also describe "curve-aware" upsampling, in which a full resolution diffusion curve image can be upsampled from a lower resolution image using formula evaluated orrections near curves. The BEM solved image representation is compact. It therefore offers advantages in scenarios where solved image representations are transmitted to devices for rendering and where PDE solving at the device is undesirable due to time or processing constraints.


title: A 30 nA, 6.6 ppm/°C, high PSRR subthreshold CMOS voltage reference

abstract: A voltage reference was developed using a 0.18 μm standard CMOS process technology, which is compatible with high power supply rejection ratio (PSRR) and low power consumption. The proposed reference circuit operating with all transistors biased in subthreshold region, which provide a reference voltage of 256 mV. The temperature coefficient (TC) was 5 ppm/°C at best and 6.6 ppm/°C on average, in a range from 0 to 140 °C. The line sensitivity was 163 ppm/V in a supply voltage range of 0.8 to 3.2 V, and the power supply rejection was 82 dB at 100 Hz. The current consumption is 30 nA at 140 °C. The chip area was 0.0042mm2.


title: Bootstrapping coreference resolution using word associations

abstract: In this paper, we present an unsupervised framework that bootstraps a complete coreference resolution (CoRe) system from word associations mined from a large unlabeled corpus. We show that word associations are useful for CoRe -- e.g., the strong association between Obama and President is an indicator of likely coreference. Association information has so far not been used in CoRe because it is sparse and difficult to learn from small labeled corpora. Since unlabeled text is readily available, our unsupervised approach addresses the sparseness problem. In a self-training framework, we train a decision tree on a corpus that is automatically labeled using word associations. We show that this unsupervised system has better CoRe performance than other learning approaches that do not use manually labeled data.


title: Using Wikipedia for Automatic Word Sense Disambiguation

abstract: This paper describes a method for generating sense-tagged data using Wikipedia as a source of sense annotations. Through word sense disambiguation experiments, the authors show that the Wikipedia-based sense annotations are reliable and can be used to construct accurate sense classifiers.


title: The Hybrid Aesthetic Functional (HAF) Appliance: A Less Visible Proposal for Functional Orthodontics

abstract: In modern orthodontics, aesthetics appear to have a decisive influence on orthodontic appliance preferences and acceptability. This paper reports the early application of a newly emerged functional device with enhanced aesthetics in a Class II treatment. Patient perspectives and technical considerations are discussed along with recommendations for further design development. It can be assumed that the use of thermoplastic material-based appliances may meet both the therapeutic and aesthetic demands of young age groups.


title: Coverage and capacity of millimeter-wave cellular networks

abstract: The millimeter-wave (mmWave) band offers the potential for high-bandwidth communication channels in cellular networks. It is not clear, however, whether both high data rates and coverage in terms of signal-to-noise-plus-interference ratio can be achieved in interference-limited mmWave cellular networks due to the differences in propagation conditions and antenna topologies. This article shows that dense mmWave networks can achieve both higher data rates and comparable coverage relative to conventional microwave networks. Sum rate gains can be achieved using more advanced beamforming techniques that allow multiuser transmission. The insights are derived using a new theoretical network model that incorporates key characteristics of mmWave networks.


title: Advances in cognitive radio networks: A survey

abstract: With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing demand for wireless radio spectrum. However, the fixed spectrum assignment policy becomes a bottleneck for more efficient spectrum utilization, under which a great portion of the licensed spectrum is severely under-utilized. The inefficient usage of the limited spectrum resources urges the spectrum regulatory bodies to review their policy and start to seek for innovative communication technology that can exploit the wireless spectrum in a more intelligent and flexible way. The concept of cognitive radio is proposed to address the issue of spectrum efficiency and has been receiving an increasing attention in recent years, since it equips wireless users the capability to optimally adapt their operating parameters according to the interactions with the surrounding radio environment. There have been many significant developments in the past few years on cognitive radios. This paper surveys recent advances in research related to cognitive radios. The fundamentals of cognitive radio technology, architecture of a cognitive radio network and its applications are first introduced. The existing works in spectrum sensing are reviewed, and important issues in dynamic spectrum allocation and sharing are investigated in detail.


title: Motion-based counter-measures to photo attacks in face recognition

abstract: Identity spoofing is a contender for high-security face recognition applications. With the advent of social media and globalized search, our face images and videos are wide-spread on the internet and can be potentially used to attack biometric systems without previous user consent. Yet, research to counter these threats is just on its infancy – we lack public standard databases, protocols to measure spoofing vulnerability and baseline methods to detect these attacks. The contributions of this work to the area are three-fold: firstly we introduce a publicly available PHOTO-ATTACK database with associated protocols to measure the effectiveness of counter-measures. Based on the data available, we conduct a study on current state-of-the-art spoofing detection algorithms based on motion analysis, showing they fail under the light of these new dataset. By last, we propose a new technique of counter-measure solely based on foreground/background motion correlation using Optical Flow that outperforms all other algorithms achieving nearly perfect scoring with an equal-error rate of 1.52% on the available test data. The source code leading to the reported results is made available for the replicability of findings in this article.


title: The Cyborg's Dilemma: Progressive Embodiment in Virtual Environments †

abstract: StudyW Academ Excellen Award Collab-U CMC Play E-Commerce Symposium Net Law InfoSpaces Usenet NetStudy VEs


title: Control system design and small signal analysis of APWM resonant inverter for induction heating

abstract: This paper proposes an asymmetrical pulse width modulation (APWM) with frequency tracking control of full bridge series resonant inverter for induction heating application. In this method, APWM is used as power regulation, and phased locked loop (PLL) is used to attain zero-voltage-switching (ZVS) over a wide load range. The complete closed loop control model is obtained using small signal analysis. The validity of the proposed control is verified by simulation results.


title: Young parents' views and experiences of interactions with health professionals

abstract: Background Although teenage pregnancy levels are declining, the North East of England still has the highest rate of young parenthood (age Aim This qualitative interview study aimed to explore young parents9 views and experiences of interactions with health professionals. Methods Young mothers and fathers ( n =10) were recruited from youth groups and Sure Start parenting classes in Newcastle upon Tyne during the period April–June 2013. They took part in one-to-one or small group semi-structured interviews regarding their experiences of interacting with health professionals about their own health and that of their child. The interviews were transcribed and analysed using thematic content analysis. Results Emergent themes included: beliefs about children9s health; proving oneself as a parent; and positives of parenting. All the participants distinguished between being a ‘first-time parent, not just a young parent’ and all, to varying degrees, challenged the authority and judgement of medical and nursing practitioners with regard to their children9s health. Conclusions The findings of this study highlight the need for health professionals to be particularly aware of the sensitivities arising from the power imbalances perceived by young parents of ill children. This can be achieved by following communication skills frameworks (e.g. the Calgary-Cambridge framework) that emphasise the importance of techniques such as active listening and building rapport.


Stem cells as delivery vehicles for regenerative medicine-challenges and perspectives


title: Evaluation of the Vegetation Coverage Resilience in Areas Damaged by the Wenchuan Earthquake Based on MODIS-EVI Data

abstract: The concept of resilience was integrated into post-earthquake ecological restoration assessments in 10 counties heavily impacted by the 2008 Wenchuan earthquake. Ecological resilience was defined as the time interval required for the vegetation coverage to recover to pre-earthquake levels in damaged areas. MODIS-EVI data from May to August in 2000 to 2016 were used to calculate the ecological resilience by fitting the curve of recovery rate (RR) versus time. The following conclusions were reached: (1) An area of 424.1 km² sustained vegetation damage. (2) The vegetation recovery was found to be linear based on the statistical analysis of the most common components of the damaged areas; consequently, linear fitting was used to estimate the resilience. (3) In terms of vegetation coverage, 44.2% of the damaged areas have already recovered. The vast majority of damaged areas are predicted to achieve vegetation recovery by 2022, but 5.3% of the damaged areas will not recover within this time period and have no resilience. (4) The management of damaged areas near roads, rivers and mining operations, especially at elevations of 2000-2500 m, slopes greater than 30°, and precipitation levels greater than 1200 mm, should be prioritized in the future. (5) The innovations of this study include the method used to extract earthquake-related vegetation damage and the prediction of vegetation succession based on resilience.


title: Calculation of Core and Stray Load Losses in Brushless Doubly Fed Induction Generators

abstract: The brushless doubly fed induction generator (BDFIG) has substantial benefits, which make it an attractive alternative as a wind generator. However, it suffers from lower efficiency and larger dimensions in comparison with the doubly fed induction generator. A major part of drawbacks arises from undesirable spatial harmonics of air-gap magnetic field. Calculation of core loss is an important issue in optimal design studies to improve the performance characteristics. The iron loss is higher and has a more complex nature in BDFIGs in contrast with conventional machines. Furthermore, additional losses cannot be ignored due to a high level of spatial harmonics distortion. This paper aims to formulate core loss and stray load loss (SLL) in BDFIGs based on the design data and experimental parameters provided by electrical steel manufacturers. In addition, an analytical procedure for calculating spatial harmonic components of the stator and rotor magnetic fields is presented. The acceptable accuracy of the theoretical equations is verified by comparing the calculated core loss and SLL of a D-180 prototype BDFIG with the measured data. The results show that SLL in BDFIGs is higher than the predicted per-unit values given by standards for induction machines, e.g., IEC 60034 ~ 2 and NEMA MG1.


title: Super-Resolution Reconstruction of High-Resolution Satellite ZY-3 TLC Images

abstract: Super-resolution (SR) image reconstruction is a technique used to recover a high-resolution image using the cumulative information provided by several low-resolution images. With the help of SR techniques, satellite remotely sensed images can be combined to achieve a higher-resolution image, which is especially useful for a two- or three-line camera satellite, e.g., the ZY-3 high-resolution Three Line Camera (TLC) satellite. In this paper, we introduce the application of the SR reconstruction method, including motion estimation and the robust super-resolution technique, to ZY-3 TLC images. The results show that SR reconstruction can significantly improve both the resolution and image quality of ZY-3 TLC images.


abstract: We propose a Topic Compositional Neural Language Model (TCNLM), a novel method designed to simultaneously capture both the global semantic meaning and the local word ordering structure in a document. The TCNLM learns the global semantic coherence of a document via a neural topic model, and the probability of each learned latent topic is further used to build a Mixture-of-Experts (MoE) language model, where each expert (corresponding to one topic) is a recurrent neural network (RNN) that accounts for learning the local structure of a word sequence. In order to train the MoE model efficiently, a matrix factorization method is applied, by extending each weight matrix of the RNN to be an ensemble of topic-dependent weight matrices. The degree to which each member of the ensemble is used is tied to the document-dependent probability of the corresponding topics. Experimental results on several corpora show that the proposed approach outperforms both a pure RNN-based model and other topic-guided language models. Further, our model yields sensible topics, and also has the capacity to generate meaningful sentences conditioned on given topics.


title: Camera Response Functions for Image Forensics: An Automatic Algorithm for Splicing Detection

abstract: We present a fully automatic method to detect doctored digital images. Our method is based on a rigorous consistency checking principle of physical characteristics among different arbitrarily shaped image regions. In this paper, we specifically study the camera response function (CRF), a fundamental property in cameras mapping input irradiance to output image intensity. A test image is first automatically segmented into distinct arbitrarily shaped regions. One CRF is estimated from each region using geometric invariants from locally planar irradiance points (LPIPs). To classify a boundary segment between two regions as authentic or spliced, CRF-based cross fitting and local image features are computed and fed to statistical classifiers. Such segment level scores are further fused to infer the image level authenticity. Tests on two data sets reach performance levels of 70% precision and 70% recall, showing promising potential for real-world applications. Moreover, we examine individual features and discover the key factor in splicing detection. Our experiments show that the anomaly introduced around splicing boundaries plays the major role in detecting splicing. Such finding is important for designing effective and efficient solutions to image splicing detection.


Socially-Aware Animated Intelligent Personal Assistant Agent


title: Designing a desirable smart bracelet for older adults

abstract: In this paper, we present the design process of a smart bracelet that aims at enhancing the life of elderly people. The bracelet acts as a personal assistant during the user's everyday life, monitoring the health status and alerting him or her about abnormal conditions, reminding medications and facilitating the everyday life in many outdoor and indoor activities.


C L ] 1 5 N ov 2 01 8 Survey of Computational Approaches to Diachronic Conceptual Change Detection


Treatment of leg cramps in patients with chronic kidney disease receiving hemodialysis.


title: Capturing Long-Tail Distributions of Object Subcategories

abstract: We argue that object subcategories follow a long-tail distribution: a few subcategories are common, while many are rare. We describe distributed algorithms for learning large- mixture models that capture long-tail distributions, which are hard to model with current approaches. We introduce a generalized notion of mixtures (or subcategories) that allow for examples to be shared across multiple subcategories. We optimize our models with a discriminative clustering algorithm that searches over mixtures in a distributed, "brute-force" fashion. We used our scalable system to train tens of thousands of deformable mixtures for VOC objects. We demonstrate significant performance improvements, particularly for object classes that are characterized by large appearance variation.


title: Small-signal modeling of pulse-width modulated switched-mode power converters

abstract: The state-space averaging method, associated with power converters, is discussed, Emphasis is on the motivation and objectives of modeling by equivalent circuits based on physical interpretation of state-space averaging. The discussion is limited to pulse-width-modulated DC-to-DC converters. >


title: Robust recommendation algorithm using an iterative group-based reputation

abstract: Collaborative filtering recommender systems have been widely applied in e-commerce and have brought huge economic benefits. Malicious users can deliberately inject attack profiles into a recommender system to affect the recommendation result. Detecting and defending against shilling attacks in recommender systems is very important. The existing algorithms, which are based on the latent factor model, have poor robustness and low recommendation accuracy under shilling attacks. Thus, we proposed a robust collaborative recommendation algorithm that is based on users' reputations, which are determined based on their grouping behaviours. We group users according to their rating histories and iteratively compute each user's reputation. This algorithm has two advantages: It has high recommendation accuracy and stronger robustness and does not destroy the user distribution of the original dataset. Experiments on the real datasets MovieLens 100K and MovieLens 1M show that the proposed robust algorithm outperforms previous robust algorithms in terms of both accuracy and robustness.


title: Find Who to Look at: Turning From Action to Saliency

abstract: The past decade has witnessed the use of highlevel features in saliency prediction for both videos and images. Unfortunately, the existing saliency prediction methods only handle high-level static features, such as face. In fact, high-level dynamic features (also called actions), such as speaking or head turning, are also extremely attractive to visual attention in videos. Thus, in this paper, we propose a data-driven method for learning to predict the saliency of multiple-face videos, by leveraging both static and dynamic features at high-level. Specifically, we introduce an eye-tracking database, collecting the fixations of 39 subjects viewing 65 multiple-face videos. Through analysis on our database, we find a set of high-level features that cause a face to receive extensive visual attention. These high-level features include the static features of face size, center-bias and head pose, as well as the dynamic features of speaking and head turning. Then, we present the techniques for extracting these high-level features. Afterwards, a novel model, namely multiple hidden Markov model (M-HMM), is developed in our method to enable the transition of saliency among faces. In our MHMM, the saliency transition takes into account both the state of saliency at previous frames and the observed high-level features at the current frame. The experimental results show that the proposed method is superior to other state-of-the-art methods in predicting visual attention on multiple-face videos. Finally, we shed light on a promising implementation of our saliency prediction method in locating the region-of-interest (ROI), for video conference compression with high efficiency video coding (HEVC).


title: Supporting conditional delegation in secure workflow management systems

abstract: Workflows model and control the execution of business processes in an organization. A workflow typically comprises of a set of coordinated activities, known as tasks. Typically, organizations establish a set of security policies, that regulate how the business process and resources should be managed. While a simple policy may specify which user (or role) can be assigned to execute a task, a complex policy may specify authorization constraints, such as separation of duties. Users may delegate the tasks assigned to them. Often such delegations are short-lived and come into play when certain conditions are satisfied. For example, a user may want to delegate his task of check approval only when going on vacation, when a check amount is less than a certain amount, or when his workload exceeds a certain limit.In this paper, we extend the notion of delegation to allow for such conditional delegation, where the delegation conditions can be based on time, workload and task attributes. When workflow systems entertain conditional delegation, different types of constraints come into play, which include authorization constraints, role activation constraints and workflow dependency requirements. We address the problem of assigning users to tasks in a consistent manner such that none of the constraints are violated.


title: VisualBackProp: efficient visualization of CNNs

abstract: This paper proposes a new method, that we call VisualBackProp, for visualizing which sets of pixels of the input image contribute most to the predictions made by the convolutional neural network (CNN). The method heavily hinges on exploring the intuition that the feature maps contain less and less irrelevant information to the prediction decision when moving deeper into the network. The technique we propose was developed as a debugging tool for CNN-based systems for steering self-driving cars and is therefore required to run in real-time, i.e. it was designed to require less computations than a forward propagation. This makes the presented visualization method a valuable debugging tool which can be easily used during both training and inference. We furthermore justify our approach with theoretical arguments and theoretically confirm that the proposed method identifies sets of input pixels, rather than individual pixels, that collaboratively contribute to the prediction. Our theoretical findings stand in agreement with the experimental results. The empirical evaluation shows the plausibility of the proposed approach on the road video data as well as in other applications and reveals that it compares favorably to the layer-wise relevance propagation approach, i.e. it obtains similar visualization results and simultaneously achieves order of magnitude speed-ups.


title: Predicting battery life from usage trajectory patterns

abstract: This paper addresses the task of predicting the battery capacity degradation ratio for a given usage pattern. This is an interesting pattern recognition task, where each usage pattern is represented as a trajectory in a feature space, and the prediction model captures the previous usage trajectory patterns. The main technical challenge here is how to build a good model from a limited number of training samples. To tackle this, we introduce a new smoothing technique in the trajectory space. The trajectory smoothing technique is shown to be equivalent of a novel regularization scheme for linear regression. Using real Li-ion battery data, we show that our approach outperforms existing methods.


title: Reframing Success and Failure of Information Systems: A Performative Perspective

abstract: The paper questions common assumptions in the dominant representational framings of information systems success and failure and proposes a performative perspective that conceives IS success and failure as relational effects performed by sociomaterial practices of IS project actor-networks of developers, managers, technologies, project documents, methodologies, and other actors. Drawing from a controversial case of a highly innovative information system in an insurance company--considered a success and failure at the same time-- the paper reveals the inherent indeterminacy of IS success and failure and describes the mechanisms by which success and failure become performed and thus determined by sociomaterial practices. This is explained by exposing ontological politics in the reconfiguration and decomposition of the IS project actor-network and the emergence of different agencies of assessment that performed both different IS realities and competing IS assessments. The analysis shows that the IS project and the implemented system as objects of assessment are not given and fixed, but are performed by the agencies of assessment together with the assessment outcomes of success and failure. The paper demonstrates that by reframing IS success and failure, the performative perspective provides some novel and surprising insights that have a potential to change conversations on IS assessments in both the IS literature and IS practice.


title: Recognition of Classical Arabic Poems

abstract: This work presents a novel method for recognizing and extracting classical Arabic poems found in textual sources. The method utilizes the basic classical Arabic poem features such as structure, rhyme, writing style, and word usage. The proposed method achieves a precision of 96.94% while keeping a high recall value at 92.24%. The method was also used to build a prototype search engine for classical Arabic poems.


title: Generalization Error Bounds for Extreme Multi-class Classification

abstract: Extreme multi-class classification is multi-class learning using an extremely large number of label classes. We show generalization error bounds with a mild dependency (up to logarithmic) on the number of classes, making them suitable for extreme classification. The bounds generally hold for empirical multi-class risk minimization algorithms using an arbitrary norm as regularizer. This setting comprises several prominent learning methods, including multinomial logistic regression, top-k multi-class SVM, and several classic multi-class SVMs. We show both data-dependent and data-independent bounds. Key to the data-dependent analysis is the introduction of the multi-class Gaussian complexity, which is able to preserve the coupling among classes. Furthermore, we show a data-independent bound that, choosing appropriate loss, can exhibit a tighter dependency on the number of classes than the data-dependent bound. We give a matching lower bound, showing that the result is tight, up to a constant factor. We apply the general results to several prominent multi-class learning machines, exhibiting a tighter dependency on the number of classes than the state of the art. For instance, for the multi-class SVM by Crammer and Singer (2002), we obtain a data-dependent bound with square root dependency (previously: linear) and a data-independent bound with logarithmic dependency (previously: square root).


title: Approaches to adversarial drift

abstract: In this position paper, we argue that to be of practical interest, a machine-learning based security system must engage with the human operators beyond feature engineering and instance labeling to address the challenge of drift in adversarial environments. We propose that designers of such systems broaden the classification goal into an explanatory goal, which would deepen the interaction with system's operators. To provide guidance, we advocate for an approach based on maintaining one classifier for each class of unwanted activity to be filtered. We also emphasize the necessity for the system to be responsive to the operators constant curation of the training set. We show how this paradigm provides a property we call isolation and how it relates to classical causative attacks. In order to demonstrate the effects of drift on a binary classification task, we also report on two experiments using a previously unpublished malware data set where each instance is timestamped according to when it was seen.


title: Color harmonization

abstract: Harmonic colors are sets of colors that are aesthetically pleasing in terms of human visual perception. In this paper, we present a method that enhances the harmony among the colors of a given photograph or of a general image, while remaining faithful, as much as possible, to the original colors. Given a color image, our method finds the best harmonic scheme for the image colors. It then allows a graceful shifting of hue values so as to fit the harmonic scheme while considering spatial coherence among colors of neighboring pixels using an optimization technique. The results demonstrate that our method is capable of automatically enhancing the color "look-and-feel" of an ordinary image. In particular, we show the results of harmonizing the background image to accommodate the colors of a foreground image, or the foreground with respect to the background, in a cut-and-paste setting. Our color harmonization technique proves to be useful in adjusting the colors of an image composed of several parts taken from different sources.


title: Full-Size High-Security ECC Implementation on MSP430 Microcontrollers

abstract: In the era of the Internet of Things, smart electronic devices facilitate processes in our everyday lives. Texas Instrument’s MSP430 microcontrollers target low-power applications, among which are wireless sensor, metering and medical applications. Those domains have in common that sensitive data is processed, which calls for strong security primitives to be implemented on those devices. Curve25519, which builds on a 255-bit prime field, has been proposed as an efficient, highly-secure elliptic-curve. While its high performance on powerful processors has been shown, the question remains, whether it is suitable for use in embedded devices. In this paper we present an implementation of Curve25519 for MSP430 microcontrollers. To combat timing attacks, we completely avoid conditional jumps and loads, thus making our software constant time. We give a comprehensive evaluation of different implementations of the modular multiplication and show which ones are favorable for different conditions. We further present implementation results of Curve25519, where our best implementation requires 9.1 million or 6.5 million cycles on MSP430Xs having a \(16\times 16\)-bit or a \(32\times 32\)-bit hardware multiplier respectively.


title: Trends in EEG-BCI for daily-life: Requirements for artifact removal

abstract: Abstract Since the discovery of the EEG principles by Berger in the 20’s, procedures for artifact removal have been essential in its pre-processing. In literature, diverse approaches based on signal processing, data mining, statistic models, and others compile information from multiple electrodes to build filters for artifact removal in the time, frequency or space domains. For almost one century, EEG acquisitions have required strict experimental conditions that included an isolated room, clinical acquisition systems, rigorous experimental protocols and very precise stimulation control. Under these steady experimental conditions, artifact removal techniques have not significantly evolved since then. However, in the last decade technological advances in brain-computer interfaces permit EEG acquisition by means of wireless, mobile, dry, wearable, and low-cost EEG headsets, with new potential daily-life applications, such as in entertainment or industry. New aspects not considered before, such as massive muscular and electrical artifacts, reduced number of electrodes, uncontrolled concomitant stimulus or the need for online processing are now essential. In this paper, we present a critical review of EEG artifact removal approaches, discuss their applicability to daily-life EEG-BCI applications, and give some directions and guidelines for upcoming research in this topic. Based on the results of the review, existing artifact removal techniques need further evolution to be applied in daily-life EEG-BCI. The use of multiple-step procedures is recommended, combining source decomposition with blind source separation and adaptive filtering, rather than using them separately. It is also recommendable to define and characterize most of artifacts evoked in daily-life EEG-BCI for a more effective removal.


title: Science in the cloud: lessons from three years of research projects on microsoft azure

abstract: Microsoft Research is now in its fourth year of awarding Windows Azure cloud resources to the academic community. As of April 2014, over 200 research projects have started. In this paper we review the results of this effort to date. We also characterize the computational paradigms that work well in public cloud environments and those that are usually disappointing. We also discuss many of the barriers to successfully using commercial cloud platforms in research and ways these problems can be overcome.


title: Caudal Septoplasty for Treatment of Septal Deviation

abstract: Objectives To describe our technique in the treatment of significant caudal septal deviation; to evaluate the effectiveness of our technique of caudal septoplasty in the treatment of caudal septal deviations. Design Retrospective review of cases taken from a database of more than 2000 patients who underwent rhinoplasty performed by 1 surgeon in a private facial plastic surgery practice. Results Medical charts were reviewed to determine the rate of preoperative nasal obstruction in 59 (95%) of 62 patients as well as nasal obstruction postoperatively 11 (17%) of 62 ( P Conclusions The caudal septoplasty technique is effective, relatively easy to perform, and shows long-term reliability in correcting caudal septal deviation. In properly selected patients, the technique is effective in improving cosmesis and nasal airflow.


title: Smart grid communication network capacity planning for power utilities

abstract: A reliable communications network is essential for the future smart grid. As power utilities move toward smart grid by implementing smart metering, it is important to plan the communication network with adequate capacity to accommodate future utility growth and new applications. This paper introduces a bottom-ups method for wide area communication network capacity planning. With this method data traffic for all foreseeable future major applications are identified and their hourly traffic profiles are estimated for both “Blue Sky Day” and “Storm Day” scenarios. Forecasts for the number of customers and devices in each region of the utility's service territory are used to derive regional data traffic profiles so the regional capacity requirement can be planned. The traffic is also categorized into different applications such as smart metering; grid monitoring and control; and advanced applications. This allows the utility to develop flexible strategies for building a common communication network based on smart grid implementation plans.


title: A Single-Phase Cascaded Multilevel Inverter Based on a New Basic Unit With Reduced Number of Power Switches

abstract: In this paper, a new single-phase cascaded multilevel inverter is proposed. This inverter is comprised of a series connection of the proposed basic unit and is able to only generate positive levels at the output. Therefore, an H-bridge is added to the proposed inverter. This inverter is called the developed cascaded multilevel inverter. In order to generate all voltage levels (even and odd) at the output, four different algorithms are proposed to determine the magnitude of dc voltage sources. Reduction in the number of power switches, driver circuits, and dc voltage sources is the advantage of the developed single-phase cascaded multilevel inverter. As a result, the installation space and cost of the inverter are reduced. These features are obtained by the comparison of the conventional cascaded multilevel inverters with the proposed cascaded topology. The ability of the proposed inverter to generate all voltage levels (even and odd) is reconfirmed by using the experimental results of a 15-level inverter.


title: Improved biclustering of microarray data demonstrated through systematic performance tests

abstract: A new algorithm is presented for fitting the plaid model, a biclustering method developed for clustering gene expression data. The approach is based on speedy individual differences clustering and uses binary least squares to update the cluster membership parameters, making use of the binary constraints on these parameters and simplifying the other parameter updates. The performance of both algorithms is tested on simulated data sets designed to imitate (normalised) gene expression data, covering a range of biclustering configurations. Empirical distributions for the components of these data sets, including non-systematic error, are derived from a real set of microarray data. A set of two-way quality measures is proposed, based on one-way measures commonly used in information retrieval, to evaluate the quality of a retrieved bicluster with respect to a target bicluster in terms of both genes and samples. By defining a one-to-one correspondence between target biclusters and retrieved biclusters, the performance of each algorithm can be assessed. The results show that, using appropriately selected starting criteria, the proposed algorithm out-performs the original plaid model algorithm across a range of data sets. Furthermore, through the rigorous assessment of the plaid model a benchmark for future evaluation of biclustering methods is established.


title: Text summarisation in progress: a literature review

abstract: This paper contains a large literature review in the research field of Text Summarisation (TS) based on Human Language Technologies (HLT). TS helps users manage the vast amount of information available, by condensing documents' content and extracting the most relevant facts or topics included in them. The rapid development of emerging technologies poses new challenges to this research field, which still need to be solved. Therefore, it is essential to analyse its progress over the years, and provide an overview of the past, present and future directions, highlighting the main advances achieved and outlining remaining limitations. With this purpose, several important aspects are addressed within the scope of this survey. On the one hand, the paper aims at giving a general perspective on the state-of-the-art, describing the main concepts, as well as different summarisation approaches, and relevant international forums. Furthermore, it is important to stress upon the fact that the birth of new requirements and scenarios has led to new types of summaries with specific purposes (e.g. sentiment-based summaries), and novel domains within which TS has proven to be also suitable for (e.g. blogs). In addition, TS is successfully combined with a number of intelligent systems based on HLT (e.g. information retrieval, question answering, and text classification). On the other hand, a deep study of the evaluation of summaries is also conducted in this paper, where the existing methodologies and systems are explained, as well as new research that has emerged concerning the automatic evaluation of summaries' quality. Finally, some thoughts about TS in general and its future will encourage the reader to think of novel approaches, applications and lines to conduct research in the next years. The analysis of these issues allows the reader to have a wide and useful background on the main important aspects of this research field.


title: What makes distributed practice effective?

abstract: The advantages provided to memory by the distribution of multiple practice or study opportunities are among the most powerful effects in memory research. In this paper, we critically review the class of theories that presume contextual or encoding variability as the sole basis for the advantages of distributed practice, and recommend an alternative approach based on the idea that some study events remind learners of other study events. Encoding variability theory encounters serious challenges in two important phenomena that we review here: superadditivity and nonmonotonicity. The bottleneck in such theories lies in the assumption that mnemonic benefits arise from the increasing independence, rather than interdependence, of study opportunities. The reminding model accounts for many basic results in the literature on distributed practice, readily handles data that are problematic for encoding variability theories, including superadditivity and nonmonotonicity, and provides a unified theoretical framework for understanding the effects of repetition and the effects of associative relationships on memory.


title: The Differential Relations of Parent and Peer Attachment to Adolescent Adjustment

abstract: Whether or not close emotional relationships with parents and peers serve similar functions for adolescent adjustment is an issue of increasing interest. The present study was designed to examine the relations between parent and peer attachment and adolescent adjustment. Eighty-nine adolescents (M age = 16.1 years, SD = 1.8 years) completed self-report measures of parent and peer attachment, sympathy, academic efficacy, aggression, anxiety, and depression. Adolescents were divided into four groups on the basis of their parent and peer attachment scores: those high on both, those low on both, those high on peer but low on parent attachment, and those high on parent but low on peer attachment. Discriminant function analyses revealed that the groups differed only along one dimension, suggesting that parent and peer attachment served similar functions in terms of the adjustment indices measured. Adolescents high on both peer and parent attachment were the best adjusted (i.e., least aggressive and depressed, most sympathetic) and those low on both were the least well adjusted. Furthermore, those high on peer but low on parent attachment were better adjusted than those high on parent but low on peer attachment, suggesting that peer attachment may be relatively more influential on adolescent adjustment than parent attachment.


title: Three-phase modular cascaded H-bridge multilevel inverter with individual MPPT for grid-connected photovoltaic systems

abstract: A three-phase modular cascaded H-bridge multilevel inverter for a grid-connected photovoltaic (PV) system is presented in this paper. To maximize the solar energy extraction of each PV string, an individual maximum power point tracking (MPPT) control scheme is applied, which allows the independent control of each dc-link voltage. PV mismatches may introduce unbalanced power supplied to the three-phase system. To solve this issue, a control scheme with modulation compensation is proposed. The three-phase modular cascaded multilevel inverter prototype has been built. Each H-bridge is connected to a 185 W solar panel. Simulation and experimental results are presented to validate the proposed ideas.


title: On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization

abstract: The problem of stochastic convex optimization with bandit feedback (in the learning community) or without knowledge of gradients (in the optimization community) has received much attention in recent years, in the form of algorithms and performance upper bounds. However, much less is known about the inherent complexity of these problems, and there are few lower bounds in the literature, especially for nonlinear functions. In this paper, we investigate the attainable error/regret in the bandit and derivative-free settings, as a function of the dimension d and the available number of queries T. We provide a precise characterization of the attainable performance for strongly-convex and smooth functions, which also imply a non-trivial lower bound for more general problems. Moreover, we prove that in both the bandit and derivative-free setting, the required number of queries must scale at least quadratically with the dimension. Finally, we show that on the natural class of quadratic functions, it is possible to obtain a "fast" O(1/T) error rate in terms of T, under mild assumptions, even without having access to gradients. To the best of our knowledge, this is the first such rate in a derivative-free stochastic setting, and holds despite previous results which seem to imply the contrary.


title: 3.6 GHz CMOS ring oscillator with low tune voltage sensitivity and temperature compensation

abstract: In this paper we present the design of a temperature compensated low-tune-voltage-sensitive CMOS ring oscillator in 40nm standard CMOS technology. The oscillator has an overall frequency range from 3.1 GHz to 3.6 GHz. The effect of temperature variations on the frequency span has been tuned out by an IPTAT (inversely proportional to absolute temperature) current reference. In this work, using a coarse-fine tuning mechanism lowers the tune range sensitivity of the oscillator, which is usually represented as KVCO. The achieved KVCO is around 490 MHz/V with 400 mV tune voltage sweep. The area and the power consumption of the ring oscillator are 0.0056 mm2 and 0.9 mW.


title: Topic extraction from news archive using TF*PDF algorithm

abstract: Since the Web became widespread, the amount of electronically available information online, especially news archives, has proliferated and threatens to become overwhelming. We propose an information system that will extract main topics in a news archive on a weekly basis. By obtaining a weekly report, a user can know what the main news events were in the past week.


title: Experimenting word embeddings in assisting legal review

abstract: As advanced technologies, such as data mining become part of the everyday workflow of document reviews in litigations, keyword-search still appears to serve as a cornerstone approach in responsive or privilege review. Keywords are conceptually easy to understand and help culling documents at the early stages of the review. But developing proper keywords to minimize the risk of under/over-inclusiveness can lead to complex strategies. To cope with the burden of designing search terms, we propose to use word embedding techniques in a dynamic manner. This paper describes a system leveraging semantic models in a smart review environment in order to support knowledge workers in eDiscovery.


title: An ear-worn continuous ballistocardiogram (BCG) sensor for cardiovascular monitoring

abstract: Traditionally, ballistocardiogram (BCG) has been measured using large and stationary devices. In this work, we demonstrate a portable and continuous BCG monitor that is wearable at the ear. The device has the form factor of a hearing aid and is wirelessly connected to a PC for data recording and analysis. With the ear as an anchoring point, the device uses a MEMS tri-axial accelerometer to measure BCG at the head. Morphological differences exist between head BCG and traditional BCG, but the principal peaks (J waves) and their vectors are preserved. The frequency of J waves corresponds to heart rate, and when used in conjunction with an electrocardiogram's (ECG) R wave, the timing of J waves yields the RJ interval. Results from our clinical study show linear correlation between the RJ interval and the heart's pre-ejection period during hemodynamic maneuvers, thus revealing important information about cardiac contractility and its regulation.


title: Locality Preserving Projections for Grassmann manifold

abstract: Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos. However, such learning algorithms particularly on high-dimensional Grassmann manifold always involve with significantly high computational cost, which seriously limits the applicability of learning on Grassmann manifold in more wide areas. In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Preserving Projections (LPP) criterion. LPP is a commonly used dimensionality reduction algorithm for vector-valued data, aiming to preserve local structure of data in the dimension-reduced space. The strategy is to construct a mapping from higher dimensional Grassmann manifold into the one in a relative low-dimensional with more discriminative capability. The proposed method can be optimized as a basic eigenvalue problem. The performance of our proposed method is assessed on several classification and clustering tasks and the experimental results show its clear advantages over other Grassmann based algorithms.


title: A Social Network-Based Recommender System (SNRS

abstract: Social influence plays an important role in product marketing. However, it has rarely been considered in traditional recommender systems. In this chapter, we present a new paradigm of recommender systems which can utilize information in social networks, including user preferences, item’s general acceptance, and influence from social friends. A probabilistic model is developed to make personalized recommendations from such information. We extract data from a real online social network, and our analysis of this large data set reveals that friends have a tendency to select the same items and give similar ratings. Experimental results on this data set show that our proposed system not only improves the prediction accuracy of recommender systems but also remedies the data sparsity and cold-start issues inherent in collaborative filtering. Furthermore, we propose to improve the performance of our system by applying semantic filtering of social networks and validate its improvement via a class project experiment. In this experiment we demonstrate how relevant friends can be selected for inference based on the semantics of friend relationships and finer-grained user ratings. Such technologies can be deployed by most content providers.


title: Social benefits of luxury brands as costly signals of wealth and status

abstract: Drawing from costly signaling theory, we predicted that luxury consumption enhances status and produces benefits in social interactions. Across seven experiments, displays of luxury — manipulated through brand labels on clothes — elicited different kinds of preferential treatment, which even resulted in financial benefits to people who engaged in conspicuous consumption. Furthermore, we tested preconditions in which the beneficial consequences of conspicuous consumption may arise and determined the proximate mechanisms underlying them. The present data suggest that luxury consumption can be a profitable social strategy because conspicuous displays of luxury qualify as a costly signaling trait that elicits status-dependent favorable treatment in human social interactions.


title: A New Abb Theorem In Banach Spaces

abstract: We give a new version of the Arrow, Barankin, and Blackwell theorem. Let X be a Banach space partially ordered through a closed, convex, and pointed cone C . We prove hat, if C is supposed to have a weakly compact base, the minimal points of a closed and onvex set S in X are in the strong closure of the minimum points of the linear continuous functionals which are strictly positive in C\{O}. We provide two proofs of this result; each of them, with only slight and canonical changes, furnishes as well the known ABB theorems in general normed spaces in a new unifying fashion. The first proof is based on the geometric property of Banach spaces expressed in the so-called drop theorem


title: Learning a Parametric Embedding by Preserving Local Structure Laurens van der Maaten

abstract: The paper presents a new unsupervised dimensionality reduction technique, called parametric t-SNE, that learns a parametric mapping between the high-dimensional data space and the low-dimensional latent space. Parametric t-SNE learns the parametric mapping in such a way that the local structure of the data is preserved as well as possible in the latent space. We evaluate the performance of parametric t-SNE in experiments on three datasets, in which we compare it to the performance of two other unsupervised parametric dimensionality reduction techniques. The results of experiments illustrate the strong performance of parametric t-SNE, in particular, in learning settings in which the dimensionality of the latent space is relatively low.


title: Low-cost robot arm with 3-DOF counterbalance mechanism

abstract: Low-cost but high performance robot arms are required for widespread use of service robots. Most robot arms use expensive motors and speed reducers to provide torques sufficient to support the robot mass and payload. If the gravitational torques due to the robot mass, which is usually much greater than the payload, can be compensated by some means, the robot would need much smaller torques, which can be delivered by cheap actuator modules. To this end, we propose a counterbalance mechanism which can completely counterbalance the gravitational torques due to the robot mass. Since most 6 DOF robots have 3 pitch joints, which are subject to gravitational torques due to the robot mass, we propose a 3 DOF counterbalance mechanism based on the double parallelogram mechanism, in which reference planes are provided to each joint for proper counterbalancing. A 5 DOF counterbalance robot arm was built to demonstrate the performance of the proposed mechanism. Simulation and experimental results showed that the proposed mechanism had effectively decreased the torque required to support the robot mass, thus allowing the prospective use of low-cost motors and speed reducers for high performance root arms.


title: Ensemble Robustness of Deep Learning Algorithms

abstract: The question why deep learning algorithms perform so well in practice has attracted increasing research interest. However, most of well-established approaches, such as hypothesis capacity, robustness or sparseness, have not provided complete explanations, due to the high complexity of the deep learning algorithms and their inherent randomness. In this work, we introduce a new approach~\textendash~ensemble robustness~\textendash~towards characterizing the generalization performance of generic deep learning algorithms. Ensemble robustness concerns robustness of the \emph{population} of the hypotheses that may be output by a learning algorithm. Through the lens of ensemble robustness, we reveal that a stochastic learning algorithm can generalize well as long as its sensitiveness to adversarial perturbation is bounded in average, or equivalently, the performance variance of the algorithm is small. Quantifying ensemble robustness of various deep learning algorithms may be difficult analytically. However, extensive simulations for seven common deep learning algorithms for different network architectures provide supporting evidence for our claims. Furthermore, our work explains the good performance of several published deep learning algorithms.


title: Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering

abstract: Clustering is among the most fundamental tasks in machine learning and artificial intelligence. In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE). Specifically, VaDE models the data generative procedure with a Gaussian Mixture Model (GMM) and a deep neural network (DNN): 1) the GMM picks a cluster; 2) from which a latent embedding is generated; 3) then the DNN decodes the latent embedding into an observable. Inference in VaDE is done in a variational way: a different DNN is used to encode observables to latent embeddings, so that the evidence lower bound (ELBO) can be optimized using the Stochastic Gradient Variational Bayes (SGVB) estimator and the reparameterization trick. Quantitative comparisons with strong baselines are included in this paper, and experimental results show that VaDE significantly outperforms the state-of-the-art clustering methods on 5 benchmarks from various modalities. Moreover, by VaDE's generative nature, we show its capability of generating highly realistic samples for any specified cluster, without using supervised information during training.


title: Procedures for Early-Stage Naval Ship Design Evaluation of Dynamic Stability: Influence of the Wave Crest

abstract: A method is presented to assess stability changes in waves in early-stage ship design. The method is practical: the calculations can be completed quickly and can be applied as soon as lines are available. The intended use of the described method is for preliminary analysis. If stability changes that result in large roll motion are indicated early in the design process, this permits planning and budgeting for direct assessments using numerical simulations and/or model experiments. The main use of the proposed method is for the justification for hull form shape modification or for necessary additional analysis to better quantify potentially increased stability risk. The method is based on the evaluation of changing stability in irregular seas and can be applied to any type of ship. To demonstrate the robustness of the method, results for 10 naval ship types are presented and discussed. The proposed method is shown to identify ships with known risk for large stability changes in waves.


title: Personalized Intelligent M-learning System for Supporting Effective English Learning

abstract: Since English has been an international language, how to enhance English levels of people by useful computer assisted learning forms or tools is a critical issue in non-English speaking countries because it definitely affects the overall competition ability of a country. With the rapid growth of wireless and mobile technologies, the mobile learning has been gradually considered as a novel and effective learning form because it inherits all the advantages of e-learning as well as breaks the limitations of learning time and space occurring in the traditional classroom learning. To provide an effective and flexible learning environment for English learning, this study adopts the advantages of the mobile learning to present a personalized intelligent mobile learning system (PIMS) which can appropriately recommend English news articles to learners based on the learners’ reading abilities evaluated by the proposed fuzzy Item Response Theory (FIRT). In addition, to promote the reading abilities of English news, the unknown or unfamiliar vocabularies of individual learner can also be automatically discovered and retrieved from the reading English news articles by the PIMS system according to the English vocabulary ability of individual learner for enhancing vocabulary learning. Currently, the PIMS system has been successfully implemented on the personal digital assistant (PDA) to provide personalized mobile learning for promoting the reading ability of English news. Experimental results indicated that the proposed system provides an efficient and effective mobile learning mechanism by adaptively recommending English news articles as well as enhancing unknown or unfamiliar vocabularies’ learning for individual learners.


title: Deep networks for robust visual recognition

abstract: Deep Belief Networks (DBNs) are hierarchical generative models which have been used successfully to model high dimensional visual data. However, they are not robust to common variations such as occlusion and random noise. We explore two strategies for improving the robustness of DBNs. First, we show that a DBN with sparse connections in the first layer is more robust to variations that are not in the training set. Second, we develop a probabilistic denoising algorithm to determine a subset of the hidden layer nodes to unclamp. We show that this can be applied to any feedforward network classifier with localized first layer connections. Recognition results after denoising are significantly better over the standard DBN implementations for various sources of noise.


title: Multiple Lane Detection Algorithm Based on Novel Dense Vanishing Point Estimation

abstract: The detection of multiple curved lane markings is still a challenge for advanced driver assistance systems today, due to interference such as road markings and shadows cast by roadside structures and vehicles. The vanishing point $\mathbf{V}_{p}$ contains the global information of the road image. Hence, $\mathbf{V}_{p}$ -based lane detection algorithms are quite insensitive to interference. When curved lanes are assumed, $\mathbf{V}_{p}$ shifts with respect to the rows of the image. In this paper, a $\mathbf{V}_{p}$ for each individual row of the image is estimated by first extracting a $\mathbf{V}_{py}$ (vertical position of the $\mathbf{V}_{p}$ ) for each individual row of the image from the v-disparity. Then, based on the estimated $\mathbf{V}_{py}$ 's, a 2-D $\mathbf{V}_{px}$ (horizontal position of the $\mathbf{V}_{p}$ ) accumulator is efficiently formed. Thus, by globally optimizing this 2-D $\mathbf{V}_{px}$ accumulator, globally optimum $\mathbf{V}_{p}$ s for the road image are extracted. Then, estimated $\mathbf{V}_{p}$ s are utilized for multiple curved lane marking detection on nonflat road surfaces. The resultant system achieves a detection rate of 99% in 1862 frames of six stereo vision test sequences.


title: Detecting DNS Tunnels Using Character Frequency Analysis

abstract: High-bandwidth covert channels pose significant risks to sensitive and proprietary information inside company networks. Domain Name System (DNS) tunnels provide a means to covertly infiltrate and exfiltrate large amounts of information passed network boundaries. This paper explores the possibility of detecting DNS tunnels by analyzing the unigram, bigram, and trigram character frequencies of domains in DNS queries and responses. It is empirically shown how domains follow Zipf's law in a similar pattern to natural languages, whereas tunneled traffic has more evenly distributed character frequencies. This approach allows tunnels to be detected across multiple domains, whereas previous methods typically concentrate on monitoring point to point systems. Anomalies are quickly discovered when tunneled traffic is compared to the character frequency fingerprint of legitimate domain traffic.


title: Periorbital and Intraorbital Studies of the Terminal Branches of the Ophthalmic Artery for Periorbital and Glabellar Filler Placements

abstract: Background ::: Filler injections for sunken upper eyelid correction and glabellar augmentation at the orbitoglabellar region need to be performed correctly. Precise knowledge of the emerging sites of all terminal branches of the ophthalmic artery is essential for these procedures to be conducted safely.


title: Uterine compression sutures, an update: review of efficacy, safety and complications of B-Lynch suture and other uterine compression techniques for postpartum haemorrhage

abstract: Since the advent of uterine compression suture by B-Lynch, numerous techniques have been advocated. We aim to discuss these various techniques with respect to their efficacy, safety, complications, complexity of the technique itself, future fertility and menstrual periods. Since no randomized controlled trials are available, the conclusions are based on weak data derived from observational studies and case series. A success rate of 91.7% has been reported for various uterine compression sutures. There are concerns regarding closure of uterine cavity and blood entrapment resulting in infection, pyometra and adhesions as the uterus will be transfixed from front to back in some of the suturing techniques. Long-term follow-up regarding fertility of patients who had uterine compression sutures is urgently needed. Data on menstrual periods after uterine compression sutures is limited. Our review is limited in nature due to lack of consistent data on many important outcomes and also preclinical variables.


title: Robust optimization -- methodology and applications

abstract: Robust Optimization (RO) is a modeling methodology, combined with computational tools, to process optimization problems in which the data are uncertain and is only known to belong to some uncertainty set. The paper surveys the main results of RO as applied to uncertain linear, conic quadratic and semidefinite programming. For these cases, computationally tractable robust counterparts of uncertain problems are explicitly obtained, or good approximations of these counterparts are proposed, making RO a useful tool for real-world applications. We discuss some of these applications, specifically: antenna design, truss topology design and stability analysis/synthesis in uncertain dynamic systems. We also describe a case study of 90 LPs from the NETLIB collection. The study reveals that the feasibility properties of the usual solutions of real world LPs can be severely affected by small perturbations of the data and that the RO methodology can be successfully used to overcome this phenomenon.


title: Bioinformatic tools for DNA/protein sequence analysis, functional assignment of genes and protein classification

abstract: Abstract. The development of efficient DNA sequencing methods has led to the achievement of the DNA sequence of entire genomes from (to date) 55 prokaryotes, 5 eukaryotic organisms and 10 eukaryotic chromosomes. Thus, an enormous amount of DNA sequence data is available and even more will be forthcoming in the near future. Analysis of this overwhelming amount of data requires bioinformatic tools in order to identify genes that encode functional proteins or RNA. This is an important task, considering that even in the well-studied Escherichia coli more than 30% of the identified open reading frames are hypothetical genes. Future challenges of genome sequence analysis will include the understanding of gene regulation and metabolic pathway reconstruction including DNA chip technology, which holds tremendous potential for biomedicine and the biotechnological production of valuable compounds. The overwhelming volume of information often confuses scientists.This review intends to provide a guide to choosing the most efficient way to analyze a new sequence or to collect information on a gene or protein of interest by applying current publicly available databases and Web services. Recently developed tools that allow functional assignment of genes, mainly based on sequence similarity of the deduced amino acid sequence, using the currently available and increasing biological databases will be discussed.


THE FORMATION OF FACEBOOK STICKINESS: THE PERSPECTIVES OF MEDIA RICHNESS THEORY, USE & GRATIFICATION THEORY AND INTIMACY


title: Fast Algorithms for Robust PCA via Gradient Descent

abstract: We consider the problem of Robust PCA in the the fully and partially observed settings. Without corruptions, this is the well-known matrix completion problem. From a statistical standpoint this problem has been recently well-studied, and conditions on when recovery is possible (how many observations do we need, how many corruptions can we tolerate) via polynomial-time algorithms is by now understood. This paper presents and analyzes a non-convex optimization approach that greatly reduces the computational complexity of the above problems, compared to the best available algorithms. In particular, in the fully observed case, with $r$ denoting rank and $d$ dimension, we reduce the complexity from $\mathcal{O}(r^2d^2\log(1/\varepsilon))$ to $\mathcal{O}(rd^2\log(1/\varepsilon))$ -- a big savings when the rank is big. For the partially observed case, we show the complexity of our algorithm is no more than $\mathcal{O}(r^4d \log d \log(1/\varepsilon))$. Not only is this the best-known run-time for a provable algorithm under partial observation, but in the setting where $r$ is small compared to $d$, it also allows for near-linear-in-$d$ run-time that can be exploited in the fully-observed case as well, by simply running our algorithm on a subset of the observations.


title: A Broadband Folded Printed Quadrifilar Helical Antenna Employing a Novel Compact Planar Feeding Circuit

abstract: A broadband printed quadrifilar helical antenna employing a novel compact feeding circuit is proposed in this paper. This antenna presents an excellent axial ratio over a wide beamwidth, with a 29% bandwidth. A specific feeding circuit based on an aperture-coupled transition and including two 90° surface mount hybrids has been designed to be integrated with the quadrifilar antenna. Over the bandwidth, the measured reflection coefficient of the antenna fed by the wideband compact circuit has been found to be equal to or lower than -12 dB and the maximum gain varies between 1.5 and 2.7 dBic from 1.18 to 1.58 GHz. The half-power beamwidth is 150°, with an axial ratio below 3 dB over this range. The compactness of the feeding circuit allows small element spacing in array arrangements.


title: Reduction of Plasma Cholesterol Levels and Induction of Hepatic LDL Receptor by Cerivastatin Sodium (CAS 143201-11-0, BAY w 6228), a New Inhibitor of 3-Hydroxy-3-methylglutaryl Coenzyme A Reductase, in Dogs

abstract: The effects of cerivastatin sodium (BAY w 6228), a new type of inhibitor of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, on plasma cholesterol concentrations and the induction of hepatic LDL receptors were investigated with beagle dogs and Hep G2 cells. Oral administration of cerivastatin (0.01, 0.03, and 0.1 mg/kg per day) for 3 weeks reduced plasma total and very low-density lipoprotein plus low-density lipoprotein (VLDL + LDL) cholesterol concentrations and increased hepatic LDL receptor binding activity in dogs. Scatchard plot analysis revealed a 1.9-fold increase in the maximum binding capacity of hepatic LDL receptors in cerivastatin-treated animals. Similar results were obtained by administration of pravastatin (1.0 and 5.0 mg/kg/day) for 3 weeks. Binding activity of the LDL receptor, as well as receptor mRNA and protein concentrations, were increased in a dose-dependent manner (0.01–1.0 μM) by exposure of Hep G2 cells to cerivastatin. The results suggest that cerivastatin reduces plasma cholesterol concentrations by increasing hepatic LDL receptor expression. The mechanism of lowering cholesterol concentration by cerivastatin was the same as with the other previously examined HMG-CoA reductase inhibitors, but the effects with cerivastatin were apparent at doses much lower than the effective doses of the other drugs. Cerivastatin, therefore, shows potential for clinical use as a potent and efficacious plasma cholesterol-lowering drug.


title: A real-time extension to the Android platform

abstract: Android belongs to the leading operating systems for mobile devices, e.g. smartphones or tablets. The availability of Android's source code under general public license allows interesting developments and useful modifications of the platform for third parties, like the integration of real-time support. This paper presents an extension of Android improving its real-time capabilities, without loss of original Android functionality and compatibility to existing applications. In our approach we apply the RT_PREEMPT patch to the Linux kernel, modify essential Android components like the Dalvik virtual machine and introduce a new real-time interface for Android developers. The resulting Android system supports applications with real-time requirements, which can be implemented in the same way as non-real-time applications.


title: Fast and accurate rectilinear steiner minimal tree algorithm for VLSI design

abstract: In this paper, we present a very fast and accurate rectilinear Steiner minimal tree (RSMT) algorithm called FLUTE. The algorithm is an extension of the wirelength estimation approach by fast lookup table [1]. The main contribution of this paper is a new net breaking technique which is much better than the one in [1]. A scheme is also presented to allow users to control the tradeoff between accuracy and runtime.FLUTE is optimal for nets up to degree 9 and is still very accurate for nets up to degree 100. So it is particularly suitable for VLSI applications in which most nets have a degree 30 or less. We show experimentally that over 18 industrial circuits in the ISPD98 benchmark suite, FLUTE with default accuracy is more accurate than the Batched 1-Steiner heuristic and is almost as fast as a very efficient implementation of Prim's rectilinear minimum spanning tree (RMST) algorithm. By adjusting the accuracy parameter, the error can be further reduced with only a small increase in runtime (e.g., 2.7x error reduction with 2.2x runtime increase).


title: Personality and Aggressive Behavior Under Provoking and Neutral Conditions: A Meta-Analytic Review

abstract: The authors conducted a comprehensive review to understand the relation between personality and aggressive behavior, under provoking and nonprovoking conditions. The qualitative review revealed that some personality variables influenced aggressive behavior under both neutral and provocation conditions, whereas others influenced aggressive behavior only under provocation. Studies that assessed personality variables and that directly measured aggressive behavior were included in the quantitative review. Analyses revealed that trait aggressiveness and trait irritability influenced aggressive behavior under both provoking and neutral conditions but that other personality variables (e.g., trait anger, Type A personality, dissipation-rumination) influenced aggressive behavior only under provoking conditions. The authors discuss possible relations between these patterns of aggressive behavior and the personality dimensions of Agreeableness and Neuroticism and consider implications for theories of aggression. Language: en


title: How does online social networking enhance life satisfaction? The relationships among online supportive interaction, affect, perceived social support, sense of community, and life satisfaction

abstract: The purpose of this study is to examine whether supportive interactions on social networking sites mediate the influence of SNS use and the number of SNS friends on perceived social support, affect, sense of community, and life satisfaction. Employing momentary sampling, the current study also looked at the relationship between supportive interaction and immediate affect after the interaction over a period of 5days. An analysis of 339 adult participants revealed a positive relationship between supportive interaction and positive affect after the interaction. A path model revealed positive associations among the number of SNS friends, supportive interactions, affect, perceived social support, sense of community, and life satisfaction. Implications for the research of online social networking and social support are discussed.


title: An Intelligent Interactive Voice Response System for Banking Domain

abstract: Interactive Voice Response (IVR) Systems have been developed by enterprises to provide better services to the customers by allowing an automated machine to interact with humans through the use of voice or Dual Tone Multi Frequency (DTMF) keypad tones. But the IVR systems being developed today do not provide ease of use to the customers as they are monotonous. The present IVR systems lack dynamism, have very less degree of failure recovery and use APIs that are system dependent. In this paper we are proposing an Intelligent Interactive Voice Response System (IIVR) that generates a context-based dialogue using semantic web service composition and Expert System. This approach provides quality service to the customer by reducing the conversation time and by providing maximum information needed by the user. We use Java Expert System Shell (JESS) Rule Engine for generating the dialogue based on the context and thus optimizing the request-reply dialogue process between the service provider and consumer of the IVR systems. Furthermore, we use Ontology-based knowledge representation and we extract the necessary information from the ontology using semantic web services to further refine the dialogue generated by the IVR system.


title: FEATURE SELECTION USING ADABOOST FOR FACE EXPRESSION RECOGNITION

abstract: Abstract : We propose a classification technique for face expression recognition using AdaBoost that learns by selecting the relevant global and local appearance features with the most discriminating information. Selectivity reduces the dimensionality of the feature space that in turn results in significant speed up during online classification. We compare our method with another leading margin-based classifier, the Support Vector Machines (SVM) and identify the advantages of using AdaBoost over SVM in this context. We use histograms of Gabor and Gaussian derivative responses as the appearance features. We apply our approach to the face expression recognition problem where local appearances play an important role. Finally, we show that though SVM performs equally well, AdaBoost feature selection provides a final hypothesis model that can easily be visualized and interpreted, which is lacking in the high dimensional support vectors of the SVM.


title: ACTION RESEARCH AND DESIGN SCIENCE RESEARCH – SEEMINGLY SIMILAR BUT DECISIVELY DISSIMILAR

abstract: Prior research has identified the similarity of Action Research (AR) and Design Science Research (DSR). This paper analyses AR and DSR from several perspectives, including paradigmatic assumptions of ontology, epistemology, methodology, and ethics, their research interests, and activities. We identify that often AR does not share the paradigmatic assumptions and the research interests of DSR, that some activities in DSR are always mutually exclusive from AR, and that there may be no, little, or significant (but not total) overlaps between AR and DSR. Thus we judge that AR and DSR are decisively dissimilar. We further identify several key problems with combining AR and DSR based on the ethical requirement of researchers to identify and manage risks to research stakeholders. Management of such risks is done by careful disclosure, identifying research limitations or by choosing alternative methods than AR for accomplishing DSR.


title: Logicblox, platform and language: A tutorial

abstract: The modern enterprise software stack--a collection of applications supporting bookkeeping, analytics, planning, and forecasting for enterprise data--is in danger of collapsing under its own weight. The task of building and maintaining enterprise software is tedious and laborious; applications are cumbersome for end-users; and adapting to new computing hardware and infrastructures is difficult. We believe that much of the complexity in today's architecture is accidental, rather than inherent. This tutorial provides an overview of the LogicBlox platform, a ambitious redesign of the enterprise software stack centered around a unified declarative programming model, based on an extended version of Datalog.


title: Series-fed planar dipole array antenna

abstract: A planar dipole array antenna operating around 2.4 GHz is presented. The array has five dipole elements that are excited in phase to reduce the elevation beamwidth for reducing undesired multipath signals and increase gain. The array is fed from the bottom with a single coaxial connector which sequentially excites all array elements in phase via a twin wire transmission line. The antenna is just 8mm wide and 1.27mm thick.


title: Rapid Synthesis of Massive Face Sets for Improved Face Recognition

abstract: Recent work demonstrated that computer graphics techniques can be used to improve face recognition performances by synthesizing multiple new views of faces available in existing face collections. By so doing, more images and more appearance variations are available for training, thereby improving the deep models trained on these images. Similar rendering techniques were also applied at test time to align faces in 3D and reduce appearance variations when comparing faces. These previous results, however, did not consider the computational cost of rendering: At training, rendering millions of face images can be prohibitive; at test time, rendering can quickly become a bottleneck, particularly when multiple images represent a subject. This paper builds on a number of observations which, under certain circumstances, allow rendering new 3D views of faces at a computational cost which is equivalent to simple 2D image warping. We demonstrate this by showing that the run-time of an optimized OpenGL rendering engine is slower than the simple Python implementation we designed for the same purpose. The proposed rendering is used in a face recognition pipeline and tested on the challenging IJB-A and Janus CS2 benchmarks. Our results show that our rendering is not only fast, but improves recognition accuracy.


title: Chat Speed OP PogChamp: Practices of Coherence in Massive Twitch Chat

abstract: Twitch.tv, a streaming platform known for video game content, has grown tremendously since its inception in 2011. We examine communication practices in Twitch chats for the popular game Hearthstone, comparing massive chats with at least 10,000 concurrent viewers and small chats with fewer than 2,000 concurrent viewers. Due to the large scale and fast pace of massive chats, communication patterns no longer follow models developed in previous studies of computer-mediated communication. Rather than what other studies have described as communication breakdowns and information overload, participants in massive chats communicate in what we call 'crowdspeak.'