title: Change Point Detection in Time Series Using Higher-Order Statistics: A Heuristic Approach

abstract: Changes in the level of a time series are usually attributed to an intervention that affects its temporal evolution. The resulting time series are referred to as interrupted time series and may be used to identify the events that caused the intervention and to quantify their impact. In the present paper, a heuristic method for level change detection in time series is presented. The method uses higher-order statistics, namely, the skewness and the kurtosis, and can identify both the existence of a change in the level of the time series and the time instance when it has happened. The technique is straightforwardly applicable to the detection of outliers in time series and promises to have several applications. The method is tested with both simulated and real-world data and is compared to other popular change detection techniques.


title: Optimal scheduling algorithm for real-time tasks in digital control systems based on GA

abstract: On the basis of analysis on the characteristic of the tasks in control system, the task model and subtask model are given. A scheduling algorithm for control system named half-preemptive EDF algorithm is proposed. The algorithm can guarantee the utility of processor and reduce the times of tasks that are preempted by other tasks. Because the performance of control system is influenced by sampling period and control delay and they affect each other, genetic algorithm is adopted to optimize the performance of control system in order to acquire optimal sampling period. Based on floating-point coding, elitist species and rank based selection are combined. The simulation results show that the algorithm presented in this paper can guarantee schedulability of tasks and improve the performance of control system.


title: Astor: Exploring the Design Space of Generate-and-Validate Program Repair beyond GenProg

abstract: Abstract This article contributes to defining the design space of program repair. Repair approaches can be loosely characterized according to the main design philosophy, in particular “generate- and-validate” and synthesis-based approaches. Each of those repair approaches is a point in the design space of program repair. Our goal is to facilitate the design, development and evaluation of repair approaches by providing a framework that: a) contains components commonly present in most approaches, b) provides built-in implementations of existing repair approaches. This paper presents a Java framework named Astor that focuses on the design space of generate-and-validate repair approaches. The key novelty of Astor is to provides explicit extension points to explore the design space of program repair. Thanks to those extension points, researchers can both reuse existing program repair components and implement new ones. Astor includes 6 unique implementations of repair approaches in Java, including GenProg for Java called jGenProg. Researchers have already defined new approaches over Astor. The implementations of program repair approaches built already available in Astor are capable of repairing, in total, 98 real bugs from 5 large Java programs. Astor code is publicly available on Github: https://github.com/SpoonLabs/astor .


title: A Ka-band waveguide-based traveling-wave spatial power divider/combiner

abstract: A Ka-band waveguide-based four-stage eight-way power divider/combiner is presented based on the traveling-wave technique and the spatial power combining technique. The dualprobe, which are symmetrically inserted in one broadside of the waveguide, double the power dividing ways without adding the insertion loss. The method of images is used to simplify the analysis and design process of the dual-probe structure. The power divider/combiner is designed, fabricated and measured to demonstrate its wide bandwidth and low insertion loss performance. From 30GHz to 40GHz, the measured return loss of the back-to-back structure is lower than −15dB and the insertion loss is less than 1.2dB.


title: Effective Outlier Detection based on Bayesian Network and Proximity

abstract: Outliers are objects that are significantly different from the others in the same dataset. They often contain insightful information for understanding the data and the data generation process. Traditional outlier detection methods can generally be divided into two categories: model-based and proximity-based approaches. A new type of model-based approach has recently been proposed to use the Bayesian network (BN) framework to discover more meaningful outliers with better interpretability. They yield very good detection result when anomalousness is mainly due to the violation of the dependency among variables. However, when anomalousness is caused by reasons other than dependency violation, BN-based methods produce very poor performance. To address this problem, we propose an ensemble outlier detection method that combines BN-based and proximity-based techniques to achieve more stable outlier detection results in different scenarios. To our best knowledge, the proposed method is the first to bring together the two major categories of outlier detection techniques. Comprehensive experiments have been done on both synthetic and real world datasets, and the results show that our method outperforms the baseline methods in most cases.


title: Effects of age and β-amyloid on cognitive changes in normal elderly people

abstract: Abstract Age-related decline is common in multiple cognitive domains. β-amyloid (Aβ) deposition, a pathological hallmark of Alzheimer's disease, is also associated with cognitive changes in many older people. In this study, we examined a wide range of cognitive function in order to differentiate the effect of age and Aβ on cognition during aging. Using positron emission tomography (PET) imaging with the radiotracer Pittsburgh Compound B (PIB), we classified normal older subjects as High PIB-Old and Low PIB-Old and applied sequential multivariate analyses (i.e., principal components analysis [PCA] and discriminant analysis) to obtain summary measures of cognitive tests encompassing multiple cognitive domains. Among 5 cognitive components, a significant age effect was observed in component scores of visual memory and executive functions, regardless of the level of Aβ. Discriminant scores (weighted scores of the 5 cognitive components) revealed a significant effect of both age and Aβ and were further associated with quantitative PIB counts. The results of the current study highlight both effects of age and Aβ on cognitive changes in normal elderly.


title: Language-independent and language-adaptive acoustic modeling for speech recognition

abstract: Abstract With the distribution of speech technology products all over the world, the portability to new target languages becomes a practical concern. As a consequence our research focuses on the question of how to port large vocabulary continuous speech recognition (LVCSR) systems in a fast and efficient way. More specifically we want to estimate acoustic models for a new target language using speech data from varied source languages, but only limited data from the target language. For this purpose, we introduce different methods for multilingual acoustic model combination and a polyphone decision tree specialization procedure. Recognition results using language-dependent, independent and language-adaptive acoustic models are presented and discussed in the framework of our GlobalPhone project which investigates LVCSR systems in 15 languages.


title: Crowdsourced time-sync video tagging using temporal and personalized topic modeling

abstract: Time-sync video tagging aims to automatically generate tags for each video shot. It can improve the user's experience in previewing a video's timeline structure compared to traditional schemes that tag an entire video clip. In this paper, we propose a new application which extracts time-sync video tags by automatically exploiting crowdsourced comments from video websites such as Nico Nico Douga, where videos are commented on by online crowd users in a time-sync manner. The challenge of the proposed application is that users with bias interact with one another frequently and bring noise into the data, while the comments are too sparse to compensate for the noise. Previous techniques are unable to handle this task well as they consider video semantics independently, which may overfit the sparse comments in each shot and thus fail to provide accurate modeling. To resolve these issues, we propose a novel temporal and personalized topic model that jointly considers temporal dependencies between video semantics, users' interaction in commenting, and users' preferences as prior knowledge. Our proposed model shares knowledge across video shots via users to enrich the short comments, and peels off user interaction and user bias to solve the noisy-comment problem. Log-likelihood analyses and user studies on large datasets show that the proposed model outperforms several state-of-the-art baselines in video tagging quality. Case studies also demonstrate our model's capability of extracting tags from the crowdsourced short and noisy comments.


title: Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling

abstract: It has become increasingly popular to obtain machine learning labels through commercial crowdsourcing services. The crowdsourcing workers or annotators are paid for each label they provide, but the task requester usually has only a limited amount of the budget. Since the data instances have different levels of labeling difficulty and the workers have different reliability for the labeling task, it is desirable to wisely allocate the budget among all the instances and workers such that the overall labeling quality is maximized. In this paper, we formulate the budget allocation problem as a Bayesian Markov decision process (MDP), which simultaneously conducts learning and decision making. The optimal allocation policy can be obtained by using the dynamic programming (DP) recurrence. However, DP quickly becomes computationally intractable when the size of the problem increases. To solve this challenge, we propose a computationally eficient approximate policy which is called optimistic knowledge gradient. Our method applies to both pull crowdsourcing marketplaces with homogeneous workers and push marketplaces with heterogeneous workers. It can also incorporate the contextual information of instances when they are available. The experiments on both simulated and real data show that our policy achieves a higher labeling quality than other existing policies at the same budget level.


title: A wearable face recognition system for individuals with visual impairments

abstract: This paper describes the iCare Interaction Assistant, an assistive device for helping the individuals who are visually impaired during social interactions. The research presented here addresses the problems encountered in implementing real-time face recognition algorithms on a wearable device. Face recognition is the initial step towards building a comprehensive social interaction assistant that will identify and interpret facial expressions, emotions and gestures. Experiments conducted for selecting a face recognition algorithm that works despite changes in facial pose and illumination angle are reported. Performance details of the face recognition algorithms tested on the device are presented along with the overall performance of the system. The specifics of the hardware components used in the wearable device are mentioned and the block diagram of the wearable system is explained in detail.


Key Generators Can Strengthen Block Ciphers


title: Visualizing Network Data

abstract: Networks are critical to modern society, and a thorough understanding of how they behave is crucial to their efficient operation. Fortunately, data on networks is plentiful; by visualizing this data, it is possible to greatly improve our understanding. Our focus is on visualizing the data associated with a network and not on simply visualizing the structure of the network itself. We begin with three static network displays; two of these use geographical relationships, while the third is a matrix arrangement that gives equal emphasis to all network links. Static displays can be swamped with large amounts of data; hence we introduce direct manipulation techniques that permit the graphs to continue to reveal relationships in the context of much more data. In effect, the static displays are parameterized so that interesting views may easily be discovered interactively. The software to carry out this network visualization is called SeeNet. >


title: Recovery of lead and copper from cyanide tailings

abstract: The flowsheet of the selective flotation of lead and the activation flotation of copper was adopted to recover lead and copper from cyanide tailings.Closed circuit test results show that the qualified lead concentrate with the recovery of 90.48% and the grade of 45.24% can be produced with lime as a stabilizer,sodium diethyl dithiocarbamate(N,N-diethyldithiocarbamate)and butylxanthate(butyl sodium xanthate)as combined collectors,and by using the flotation flowsheet of one roughing,two scavengings and two cleanings.The qualified copper concentrate with the recovery of 82.17% and the grade of 19.28% can be produced with NP(a combined reagent by inorganic copper salts and inorganic zinc salts)as an activator,organic depressant FM as a stabilizer,Z-200(O-isopropyl-N-ethylthiocarbamate)and butyl ammonium aerofloat(ammonium dibutyl dithiophosphate)as combined collectors,and by using the flotation flowsheet of one roughing,two scavengings and two cleanings.Gold and silver are also simultaneously enriched within the two kinds of concentrates.


Consistent Comic Colorization with Pixel-wise Background Classification


title: Game Theory Basics and Its Application in Cyber Security

abstract: The concept of game theory has in recent times has found application extensively in the area of security usually called security games. A game could be normal form or extensive form and are used to model the behaviour of players in a simple or complex contest for resources within a given scenario. Game theory finds application in various areas including finance, economics, politics, auction, sciences and cyber security. This work reviews the application of game theory in cyber security. A brief introduction to the concept of game theory is presented alongside a detailed review of research works carried out using the concept of game theory for cyber security.


title: Computers in Music Education

abstract: This paper provides a survey of the applications of computers in music teaching. The systems are classified by musical activity rather than by technical approach. The instructional strategies involved and the type of knowledge represented are highlighted and areas for future research are identified.


Nonstationary function optimization using genetic algorithm with dominance and diploidy


title: Phase retrieval algorithms: a comparison

abstract: Iterative algorithms for phase retrieval from intensity data are compared to gradient search methods. Both the problem of phase retrieval from two intensity measurements (in electron microscopy or wave front sensing) and the problem of phase retrieval from a single intensity measurement plus a non-negativity constraint (in astronomy) are considered, with emphasis on the latter. It is shown that both the error-reduction algorithm for the problem of a single intensity measurement and the Gerchberg-Saxton algorithm for the problem of two intensity measurements converge. The error-reduction algorithm is also shown to be closely related to the steepest-descent method. Other algorithms, including the input–output algorithm and the conjugate-gradient method, are shown to converge in practice much faster than the error-reduction algorithm. Examples are shown.


title: Techniques for Clutter Suppression in the Presence of Body Movements during the Detection of Respiratory Activity through UWB Radars

abstract: This paper focuses on the feasibility of tracking the chest wall movement of a human subject during respiration from the waveforms recorded using an impulse-radio (IR) ultra-wideband radar. The paper describes the signal processing to estimate sleep apnea detection and breathing rate. Some techniques to solve several problems in these types of measurements, such as the clutter suppression, body movement and body orientation detection are described. Clutter suppression is achieved using a moving averaging filter to dynamically estimate it. The artifacts caused by body movements are removed using a threshold method before analyzing the breathing signal. The motion is detected using the time delay that maximizes the received signal after a clutter removing algorithm is applied. The periods in which the standard deviations of the time delay exceed a threshold are considered macro-movements and they are neglected. The sleep apnea intervals are detected when the breathing signal is below a threshold. The breathing rate is determined from the robust spectrum estimation based on Lomb periodogram algorithm. On the other hand the breathing signal amplitude depends on the body orientation respect to the antennas, and this could be a problem. In this case, in order to maximize the signal-to-noise ratio, multiple sensors are proposed to ensure that the backscattered signal can be detected by at least one sensor, regardless of the direction the human subject is facing. The feasibility of the system is compared with signals recorded by a microphone.


title: Deterministic Construction of Compressed Sensing Matrices using BCH Codes

abstract: In this paper we introduce deterministic $m\times n$ RIP fulfilling $\pm 1$ matrices of order $k$ such that $\frac{\log m}{\log k}\approx \frac{\log(\log_2 n)}{\log(\log_2 k)}$. The columns of these matrices are binary BCH code vectors that their zeros are replaced with -1 (excluding the normalization factor). The samples obtained by these matrices can be easily converted to the original sparse signal; more precisely, for the noiseless samples, the simple Matching Pursuit technique, even with less than the common computational complexity, exactly reconstructs the sparse signal. In addition, using Devore's binary matrices, we expand the binary scheme to matrices with $\{0,1,-1\}$ elements.


title: Human Information Behavior

abstract: Introduction Until recently the computer science and information systems communities have equated 'information requirements' of users with the way users behave in relation to the systems available. In other words, investigations into information requirements were concerned almost entirely with how a user navigated a given system and what he or she could do with the data (rather than information) made available by information systems. This is now beginning to change as ethnographic methods are introduced into the requirements definition stage of systems design, and Beyer and Holtzblatt (1998) have shown the benefits. However, even when such methods are employed, the designers appear to be asking, "How is this person using the system?" rather than seeking to determine what the individual's (or the organization's) information needs may be and how information seeking behavior relates to other, task-oriented behavior. In fact, a concern with what information is needed has been the province not of information systems as a discipline, but of information science and, before that, librarianship. To these fields we can add consumer behavior research, marketing, psychology, health communication research, and a number of other disciplines that take the user as the focus of interest, rather than the system. The aim of this paper is to review some of this research and to point to findings that enable the system designer to put the design process in the wider context of the user in the organization. Some Definitions Some definitions are needed before we go further. In this paper, four terms are used: information behavior, information seeking behavior, information searching behavior and information use behavior. They are defined as follows: Information Behavior is the totality of human behavior in relation to sources and channels of information, including both active and passive information seeking, and information use. Thus, it includes face-to-face communication with others, as well as the passive reception of information as in, for example, watching TV advertisements, without any intention to act on the information given. Information Seeking Behavior is the purposive seeking for information as a consequence of a need to satisfy some goal. In the course of seeking, the individual may interact with manual information systems (such as a newspaper or a library), or with computer-based systems (such as the World Wide Web). Information Searching Behavior is the 'micro-level' of behavior employed by the searcher in interacting with information systems of all kinds. It consists of all the interactions with the system, whether at the level of human computer interaction (for example, use of the mouse and clicks on links) or at the intellectual level (for example, adopting a Boolean search strategy or determining the criteria for deciding which of two books selected from adjacent places on a library shelf is most useful), which will also involve mental acts, such as judging the relevance of data or information retrieved. Information Use Behavior consists of the physical and mental acts involved in incorporating the information found into the person's existing knowledge base. It may involve, therefore, physical acts such as marking sections in a text to note their importance or significance, as well as mental acts that involve, for example, comparison of new information with existing knowledge. In all of the above definitions data is subsumed under information, that is, data may or may not be information depending upon the state of understanding of the information user. A datum such as "hbar=h/2pi = 6.58*10 [conjunction] -25 GeV s = 1.05*10 [conjunction]-34 J s" does not inform me because I have no framework of understanding in which to incorporate the datum. …


title: A high-voltage output driver in a 2.5-V 0.25-/spl mu/m CMOS technology

abstract: The design of a high-voltage output driver in a digital 0.25-/spl mu/m 2.5-V technology is presented. The use of stacked devices with a self-biased cascode topology allows the driver to operate at three times the nominal supply voltage. Oxide stress and hot carrier degradation is minimized since the driver operates within the voltage limits imposed by the design rules of a mainstream CMOS technology. The proposed high-voltage architecture uses a switching output stage. The realized prototype delivers an output swing of 6.46 V to a 50-/spl Omega/ load with a 7.5-V supply and an input square wave of 10 MHz. A PWM signal with a dual-tone sinusoid at 70 kHz and 250 kHz results in an IM3 of -65 dB and an IM2 of -67 dB. The on-resistance is 5.9 /spl Omega/.


title: Building facade detection, segmentation, and parameter estimation for mobile robot localization and guidance

abstract: Building facade detection is an important problem in computer vision, with applications in mobile robotics and semantic scene understanding. In particular, mobile platform localization and guidance in urban environments can be enabled with an accurate segmentation of the various building facades in a scene. Toward that end, we present a system for segmenting and labeling an input image that for each pixel, seeks to answer the question “Is this pixel part of a building facade, and if so, which one?” The proposed method determines a set of candidate planes by sampling and clustering points from the image with Random Sample Consensus (RANSAC), using local normal estimates derived from Principal Component Analysis (PCA) to inform the planar model. The corresponding disparity map and a discriminative classification provide prior information for a two-layer Markov Random Field model. This MRF problem is solved via Graph Cuts to obtain a labeling of building facade pixels at the mid-level, and a segmentation of those pixels into particular planes at the high-level. The results indicate a strong improvement in the accuracy of the binary building detection problem over the discriminative classifier alone, and the planar surface estimates provide a good approximation to the ground truth planes.


title: Epigenetic regulation of neuronal dendrite and dendritic spine development

abstract: Dendrites and the dendritic spines of neurons play key roles in the connectivity of the brain and have been recognized as the locus of long-term synaptic plasticity, which is correlated with learning and memory. The development of dendrites and spines in the mammalian central nervous system is a complex process that requires specific molecular events over a period of time. It has been shown that specific molecules are needed not only at the spine's point of contact, but also at a distance, providing signals that initiate a cascade of events leading to synapse formation. The specific molecules that act to signal neuronal differentiation, dendritic morphology, and synaptogenesis are tightly regulated by genetic and epigenetic programs. It has been shown that the dendritic spine structure and distribution are altered in many diseases, including many forms of mental retardation (MR), and can also be potentiated by neuronal activities and an enriched environment. Because dendritic spine pathologies are found in many types of MR, it has been proposed that an inability to form normal spines leads to the cognitive and motor deficits that are characteristic of MR. Epigenetic mechanisms, including DNA methylation, chromatin remodeling, and the noncoding RNA-mediated process, have profound regulatory roles in mammalian gene expression. The study of epigenetics focuses on cellular effects that result in a heritable pattern of gene expression without changes to genomic encoding. Despite extensive efforts to understand the molecular regulation of dendrite and spine development, epigenetic mechanisms have only recently been considered. In this review, we will focus on epigenetic mechanisms that regulate the development and maturation of dendrites and spines. We will discuss how epigenetic alterations could result in spine abnormalities that lead to MR, such as is seen in fragile X and Rett syndromes. We will also discuss both general methodology and recent technological advances in the study of neuronal dendrites and spines.


title: 3D geographic scenes visualization based on WebGL

abstract: With the fast development of the Internet, the lightweight 3-dimension (3D) geographic scenes visualization system is expected to play more important roles in web-based GIS. Finding an effective solution to render 3D geospatial data has great significance in Cartography and Geographic Information Systems. The emergence of HTML5, as well as WebGL (Web Graphics Library), provides a new approach for 3D WebGIS. WebGL is a JavaScript API for rendering 3D graphics within compatible web browsers. Other plugins are no longer required for displaying 3D objects. WebGL is regarded as a part of new generation web standards and is completely integrated into most popular browsers. It allows GPU accelerated usage of physics and image processing. WebGL elements can be mixed with other HTML elements and composited with other parts of the page or page background. This paper discusses the design and implementation of a digital city roaming system based on WebGL technology. Users can make cartographic analysis, destination query, data query and map mark with the system in browsers. All 3D models in the scene are converted to glTF (GL Transmission Format), and can be used to load 3D scenes and models. For the complex scenes, this system only downloads the scene data within the current view using visibility culling techniques, then joins these data into the rendering queues. At the same time, the data request task is managed in queue by the management layer using prefetching strategy. On the client-side, a method is proposed to deal with occlusion culling based on the parameters of current scene, geometry shape and positional relation. The client-side removes the occluded model data from memory, while the remaining visible data is passed to the GPU rendering pipeline. Then the client-side renders the scene in browser according to the parameters of these glTF files. The experiment result shows that this system can smoothly render the city 3D scene in the browser without any plugins. The city roaming system based on WebGL shows a good interactive experience of 3D visualization for users. The paper provides a very effective and promising method for establishing 3D WebGIS realization.


title: Effect of increased fat availability on metabolism and exercise capacity.

abstract: Several procedures have been utilized to elevate plasma free fatty acid (FFA) concentration and increase fatty acid (FA) delivery to skeletal muscle during exercise. These include fasting, caffeine ingestion, L-carnitine supplementation, ingestion of medium-chain and long-chain triglyceride (LCT) solutions, and intravenous infusion of intralipid emulsions. Studies in which both untrained and well-trained subjects have ingested LCT solutions or received an infusion of intralipid (in combination with an injection of heparin) before exercise have reported significant reductions in whole-body carbohydrate oxidation and decreased muscle glycogen utilization during both moderate and intense dynamic exercise lasting 15-60 min. The effects of increased FA provision on rates of muscle glucose uptake during exercise are, however, equivocal. Despite substantial muscle glycogen sparing (15-48% compared with control), exercise capacity is not systematically improved in the face of increased FA availability.


title: Idiom, Syntax, and Advanced Theory of Mind Abilities in Children With Autism Spectrum Disorders

abstract: Purpose When researchers investigate figurative language abilities (including idioms) in children with autism spectrum disorder (ASD), syntax abilities may be more important than once considered. In addition, there are limitations to the overreliance on false-belief tasks to measure theory of mind (TOM) abilities. In the current study, the authors investigated idiom, syntax, and advanced TOM abilities in children with ASD compared to children with typical development (TD). Method Twenty-six children with ASD, ages 5 to 12 years, were compared to individuals in each of 2 control groups of children with TD: 1 matched on chronological age and nonverbal IQ, and 1 matched on syntax age-equivalence and raw scores. Idiom comprehension, syntax, vocabulary, and 2 measures of advanced TOM abilities were examined. Results Although children with ASD performed worse on idiom comprehension compared to the age-matched group with TD, they exhibited comparable idiom performance to the syntax-matched group with TD. Advance...


title: PRIMA: policy-reduced integrity measurement architecture

abstract: We propose an integrity measurement approach based on information flow integrity,which we call the Policy-Reduced Integrity Measurement Architecture (PRIMA).The recent availability of secure hardware has made it practical for a system to measure its own integrity, such that it can generate an integrity proof for remote parties. Various approaches have been proposed,but most simply measure the loaded code and static data to approximate runtime system integrity.We find that these approaches suffer from two problems: (1)the load-time measurements of code alone do not accurately reflect runtime behaviors,such as the use of untrusted network data,and (2) they are ineficient,requiring all measured entities to be known and fully trusted even if they have no impact on the target application.Classical integrity models are based on information flow,so we design the PRIMA approach to enable measurement of information flow integrity and prove that it achieves these goals. We prove how a remote party can verify useful information flow integrity properties using PRIMA. A PRIMA prototype has been built based on the open-source Linux Integrity Measurement Architecture (IMA)using SELinux policies to provide the information flow.


title: A comparison of platforms for implementing and running very large scale machine learning algorithms

abstract: We describe an extensive benchmark of platforms available to a user who wants to run a machine learning (ML) inference algorithm over a very large data set, but cannot find an existing implementation and thus must "roll her own" ML code. We have carefully chosen a set of five ML implementation tasks that involve learning relatively complex, hierarchical models. We completed those tasks on four different computational platforms, and using 70,000 hours of Amazon EC2 compute time, we carefully compared running times, tuning requirements, and ease-of-programming of each.


title: Comparison of Carboplatin and Doxorubicin‐Based Chemotherapy Protocols in 470 Dogs after Amputation for Treatment of Appendicular Osteosarcoma

abstract: BACKGROUND ::: Many chemotherapy protocols have been reported for treatment of canine appendicular osteosarcoma (OSA), but outcome comparisons in a single population are lacking. ::: ::: ::: OBJECTIVE ::: To evaluate the effects of protocol and dose intensity (DI) on treatment outcomes for carboplatin and doxorubicin-based chemotherapy protocols. ::: ::: ::: ANIMALS ::: Four hundred and seventy dogs with appendicular OSA. ::: ::: ::: METHODS ::: A retrospective cohort study was performed comprising consecutive dogs treated (1997-2012) with amputation followed by 1 of 5 chemotherapy protocols: carboplatin 300 mg/m(2) IV q21d for 4 or 6 cycles (CARBO6), doxorubicin 30 mg/m(2) IV q14d or q21d for 5 cycles, and alternating carboplatin 300 mg/m(2) IV and doxorubicin 30 mg/m(2) IV q21d for 3 cycles. Adverse events (AE) and DI were evaluated. Kaplan-Meier survival curves and Cox proportional hazards regression were used to compare disease-free interval (DFI) and survival time (ST) among protocols. ::: ::: ::: RESULTS ::: The overall median DFI and ST were 291 days and 284 days, respectively. A lower proportion of dogs prescribed CARBO6 experienced AEs compared to other protocols (48.4% versus 60.8-75.8%; P = .001). DI was not associated with development of metastases or death. After adjustment for baseline characteristics and prognostic factors, none of the protocols provided a significant reduction in risk of development of metastases or death. ::: ::: ::: CONCLUSIONS AND CLINICAL IMPORTANCE ::: Although choice of protocol did not result in significant differences in DFI or ST, the CARBO6 protocol resulted in a lower proportion of dogs experiencing AEs, which could be advantageous in maintaining high quality of life during treatment. DI was not a prognostic indicator in this study.


title: How design characteristics of robots determine evaluation and uncanny valley related responses

abstract: Categories of robots with distinct design characteristics were identified.Relationships between design and evaluation of robots are outlined.Contrasting the uncanny valley a cubic function did not fit our data best. Against the background of the uncanny valley hypothesis (Mori, 1970) and its conceptual shortcomings the present study aims at identifying design characteristics which determine the evaluation of robots. We conducted a web-based survey (N=151) with standardized pictures of 40 robots which were evaluated. A cluster analysis revealed six clusters of robots. We were able to identify different groups of robots which were rated significantly different on six dimensions. Interestingly, in two clusters robots were rated both low in human-likeness and low in "mechanical" indicating that in future research these adjectives should be used separately. The robots in those clusters with a high mean concerning "Threatening" are almost all bi-pedal robots. In contrast to the humanoid robots (cluster 5), the android robots (clusters 3 and 4) were rated higher on "Familiarity", and on "Likability" although all three clusters (3, 4, and 5) were rated as "Threatening" which contrasts the uncanny valley hypothesis. Moreover, the data in the present study could not be explained by a cubic function as would be suggested by the graph proposed by Mori, but rather by linear or quadratic relationships.


title: Two traditions of happiness research , not two distinct types of happiness

abstract: In an earlier paper (Kashdan, Biswas-Diener, & King, 2008), we outlined a critique of the distinction being made between eudaimonic and hedonic forms of happiness. That paper seems to have had the desired effect in stimulating discourse on this important subject as evidenced by a number of responses from our colleagues. In this paper, we address these responses collectively. In particular, we outline common intellectual ground with the responding authors as well as points of difference.


title: Application management services analytics

abstract: Enterprises often maintain many IT applications to support their business. Application Management Services (AMS) aim to maintain high levels of service quality and availability by restoring normal application service operations and minimizing negative business impact. In this paper, we present the AMS Analytics System for improving the productivity and quality of delivery for AMS practices. Issues regarding IT applications are formally referred as IT incidents or tickets, which are an important vehicle for measuring quality of AMS. IT incident ticket analytics, an important component of of the analytics system, measures workload variability, resource productivity and delivery performance using algorithms from statistics, queuing theory, data clustering and signal processing. The AMS Analytics System provides a standardized, integrated analytics platform supporting AMS delivery. It is built on a Web platform using a set of standard open stack software, enhanced with advanced analytics. Since its initial release, we have applied the AMS Analytics System to several dozens of real-world enterprise users, receiving very positive feedback.


title: Clinical profiles and outcomes of children admitted to the pediatric intensive care unit from the emergency department.

abstract: The aim of this study was to describe clinical profiles and outcomes of children admitted directly from the Emergency Room (ER) to the Pediatric Intensive Care Unit (PICU) of academic hospital. The medical records of all children (1 month to 16 years) admitted in PICU from ER, from January 2011 to December 2012 were reviewed. Of the 26,774 patients seen in the ER during the study period, 468 (1.7%) were admitted to the PICU which constituted about 41.5% (468/1127) of all the total PICU admissions. Sixty three percent (n=294) were under-five; males were 60.9% (285), 82.3% (385) were in medical category. Neurological and respiratory illnesses were the most common groups (> 50% of all ER admissions). Multi-organ dysfunction syndrome and co-morbidity were present in 25.2% (n=118) and 23.5% (n=110) respectively. The mean length of stay was 5 ± 3.7 hours. The case-fatality rate was 20.3% (n=95) as compared to the overall PICU mortality rate of 11.9% (n=135).


title: Calipso: physics-based image and video editing through CAD model proxies

abstract: We present Calipso, an interactive method for editing images and videos in a physically-coherent manner. Our main idea is to realize physics-based manipulations by running a full physics simulation on proxy geometries given by non-rigidly aligned CAD models. Running these simulations allows us to apply new, unseen forces to move or deform selected objects, change physical parameters such as mass or elasticity, or even add entire new objects that interact with the rest of the underlying scene. In our method the user makes edits directly in 3D; these edits are processed by the simulation and then transfered to the target 2D content using shape-to-image correspondences in a photo-realistic rendering process. To align the CAD models, we introduce an efficient CAD-to-image alignment procedure that jointly minimizes for rigid and non-rigid alignment while preserving the high-level structure of the input shape. Moreover, the user can choose to exploit image flow to estimate scene motion, producing coherent physical behavior with ambient dynamics. We demonstrate physics-based editing on a wide range of examples producing myriad physical behavior while preserving geometric and visual consistency.


title: Automatic chord recognition for music classification and retrieval

abstract: As one of the most important mid-level features of music, chord contains rich information of harmonic structure that is useful for music information retrieval. In this paper, we present a chord recognition system based on the N-gram model. The system is time-efficient, and its accuracy is comparable to existing systems. We further propose a new method to construct chord features for music emotion classification and evaluate its performance on commercial song recordings. Experimental results demonstrate the advantage of using chord features for music classification and retrieval.


title: XIRAF - XML-based indexing and querying for digital forensics

abstract: This paper describes a novel, XML-based approach towards managing and querying forensic traces extracted from digital evidence. This approach has been implemented in XIRAF, a prototype system for forensic analysis. XIRAF systematically applies forensic analysis tools to evidence files (e.g., hard disk images). Each tool produces structured XML annotations that can refer to regions (byte ranges) in an evidence file. XIRAF stores such annotations in an XML database, which allows us to query the annotations using a single, powerful query language (XQuery). XIRAF provides the forensic investigator with a rich query environment in which browsing, searching, and predefined query templates are all expressed in terms of XML database queries.


title: Handwritten Isolated Bangla Compound Character Recognition: a new benchmark using a novel deep learning approach

abstract: Abstract In this work, a novel deep learning technique for the recognition of handwritten Bangla isolated compound character is presented and a new benchmarkof recognition accuracy on the CMATERdb 3.1.3.3 dataset is reported. Greedy layer wise training of Deep Neural Network has helped to make significant strides in various pattern recognition problems. We employ layerwise training to Deep Convolutional Neural Networks (DCNN) in a supervised fashion and augment the training process with the RMSProp algorithm to achieve faster convergence. We compare results with those obtained from standard shallow learning methods with predefined features, as well as standard DCNNs. Supervised layerwise trained DCNNs are found to outperform standard shallow learning models such as Support Vector Machines as well as regular DCNNs of similar architecture by achieving error rate of 9.67% thereby setting a new benchmark on the CMATERdb 3.1.3.3 with recognition accuracy of 90.33%, representing an improvement of nearly 10%.


title: Convex Factorization Machines

abstract: Factorization machines are a generic framework which allows to mimic many factorization models simply by feature engineering. In this way, they combine the high predictive accuracy of factorization models with the flexibility of feature engineering. Unfortunately, factorization machines involve a non-convex optimization problem and are thus subject to bad local minima. In this paper, we propose a convex formulation of factorization machines based on the nuclear norm. Our formulation imposes fewer restrictions on the learned model and is thus more general than the original formulation. To solve the corresponding optimization problem, we present an efficient globally-convergent two-block coordinate descent algorithm. Empirically, we demonstrate that our approach achieves comparable or better predictive accuracy than the original factorization machines on 4 recommendation tasks and scales to datasets with 10 million samples.


title: ADMIT- A Five Dimensional Approach towards Standardization of Network and Computer Attack Taxonomies

abstract: This paper proposed a five dimensional taxonomy ADMIT which captures five major classifiers to characterize the nature of attacks. These are classification by attack vector, classification by defense, classification by method, classification by impact and classification by attack target. The classification structure of proposed taxonomy described the nature of attacks thoroughly. The administrator can use the proposed taxonomy to locate strategies that are appropriate for securing their system against vulnerabilities that can be exploited. Use of ADMIT taxonomy in network defense strategies can improve the overall level of security.


title: Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design

abstract: In the future smart grid, both users and power companies can potentially benefit from the economical and environmental advantages of smart pricing methods to more effectively reflect the fluctuations of the wholesale price into the customer side. In addition, smart pricing can be used to seek social benefits and to implement social objectives. To achieve social objectives, the utility company may need to collect various information about users and their energy consumption behavior, which can be challenging. In this paper, we propose an efficient pricing method to tackle this problem. We assume that each user is equipped with an energy consumption controller (ECC) as part of its smart meter. All smart meters are connected to not only the power grid but also a communication infrastructure. This allows two-way communication among smart meters and the utility company. We analytically model each user's preferences and energy consumption patterns in form of a utility function. Based on this model, we propose a Vickrey-Clarke-Groves (VCG) mechanism which aims to maximize the social welfare, i.e., the aggregate utility functions of all users minus the total energy cost. Our design requires that each user provides some information about its energy demand. In return, the energy provider will determine each user's electricity bill payment. Finally, we verify some important properties of our proposed VCG mechanism for demand side management such as efficiency, user truthfulness, and nonnegative transfer. Simulation results confirm that the proposed pricing method can benefit both users and utility companies.


title: Place-Its: A Study of Location-Based Reminders on Mobile Phones

abstract: Context-awareness can improve the usefulness of automated reminders. However, context-aware reminder applications have yet to be evaluated throughout a person's daily life. Mobile phones provide a potentially convenient and truly ubiquitous platform for the detection of personal context such as location, as well as the delivery of reminders. We designed Place-Its, a location-based reminder application that runs on mobile phones, to study people using location-aware reminders throughout their daily lives. We describe the de-sign of Place-Its and a two-week exploratory user study. The study reveals that location-based reminders are useful, in large part because people use location in nuanced ways.


title: Detection-based object labeling in 3D scenes

abstract: We propose a view-based approach for labeling objects in 3D scenes reconstructed from RGB-D (color+depth) videos. We utilize sliding window detectors trained from object views to assign class probabilities to pixels in every RGB-D frame. These probabilities are projected into the reconstructed 3D scene and integrated using a voxel representation. We perform efficient inference on a Markov Random Field over the voxels, combining cues from view-based detection and 3D shape, to label the scene. Our detection-based approach produces accurate scene labeling on the RGB-D Scenes Dataset and improves the robustness of object detection.


title: Bugs as deviant behavior: a general approach to inferring errors in systems code

abstract: A major obstacle to finding program errors in a real system is knowing what correctness rules the system must obey. These rules are often undocumented or specified in an ad hoc manner. This paper demonstrates techniques that automatically extract such checking information from the source code itself, rather than the programmer, thereby avoiding the need for a priori knowledge of system rules.The cornerstone of our approach is inferring programmer "beliefs" that we then cross-check for contradictions. Beliefs are facts implied by code: a dereference of a pointer, p, implies a belief that p is non-null, a call to "unlock(1)" implies that 1 was locked, etc. For beliefs we know the programmer must hold, such as the pointer dereference above, we immediately flag contradictions as errors. For beliefs that the programmer may hold, we can assume these beliefs hold and use a statistical analysis to rank the resulting errors from most to least likely. For example, a call to "spin_lock" followed once by a call to "spin_unlock" implies that the programmer may have paired these calls by coincidence. If the pairing happens 999 out of 1000 times, though, then it is probably a valid belief and the sole deviation a probable error. The key feature of this approach is that it requires no a priori knowledge of truth: if two beliefs contradict, we know that one is an error without knowing what the correct belief is.Conceptually, our checkers extract beliefs by tailoring rule "templates" to a system --- for example, finding all functions that fit the rule template "a must be paired with b." We have developed six checkers that follow this conceptual framework. They find hundreds of bugs in real systems such as Linux and OpenBSD. From our experience, they give a dramatic reduction in the manual effort needed to check a large system. Compared to our previous work [9], these template checkers find ten to one hundred times more rule instances and derive properties we found impractical to specify manually.


title: MaskRNN: Instance Level Video Object Segmentation

abstract: Instance level video object segmentation is an important technique for video editing and compression. To capture the temporal coherence, in this paper, we develop MaskRNN, a recurrent neural net approach which fuses in each frame the output of two deep nets for each object instance - a binary segmentation net providing a mask and a localization net providing a bounding box. Due to the recurrent component and the localization component, our method is able to take advantage of long-term temporal structures of the video data as well as rejecting outliers. We validate the proposed algorithm on three challenging benchmark datasets, the DAVIS-2016 dataset, the DAVIS-2017 dataset, and the Segtrack v2 dataset, achieving state-of-the-art performance on all of them.


title: SoK: Cryptographically Protected Database Search

abstract: Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly, systems are offered by academia, start-ups, and established companies. However, there is no best protected search system or set of techniques. Design of such systems is a balancing act between security, functionality, performance, and usability. This challenge is made more difficult by ongoing database specialization, as some users will want the functionality of SQL, NoSQL, or NewSQL databases. This database evolution will continue, and the protected search community should be able to quickly provide functionality consistent with newly invented databases. At the same time, the community must accurately and clearly characterize the tradeoffs between different approaches. To address these challenges, we provide the following contributions:1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms.2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in functionality.3) An analysis of attacks against protected search for different base queries.4) A roadmap and tools for transforming a protected search system into a protected database, including an open-source performance evaluation platform and initial user opinions of protected search.


title: Traumatic and trauma-related amputations: Part II: Upper extremity and future directions.

abstract: Trauma is the most common reason for amputation of the upper extremity. The morphologic and functional distinctions between the upper and lower extremities render the surgical techniques and decision-making different in many key respects. Acceptance of the prosthesis and the outcomes are improved by performing a transradial rather than a more proximal amputation. Substantial efforts, including free tissue transfers when necessary, should be made to salvage the elbow. Careful management of the peripheral nerves is critical to minimize painful neuroma formation while preserving options for possible future utilization in targeted muscle reinnervation and use of a myoelectric prosthesis. Rapid developments with targeted muscle reinnervation, myoelectric prostheses, and composite tissue allotransplantation may dramatically alter surgical treatment algorithms in the near future for patients with severe upper-extremity trauma.


title: BeeKeeper: A Blockchain-Based IoT System With Secure Storage and Homomorphic Computation

abstract: Currently, Internet of Things (IoT) and blockchain technologies are experiencing exponential growth in academia and industry. Generally, IoT is a centralized system whose security and performance mainly rely on centralized servers. Therefore, users have to trust the centralized servers; in addition, it is difficult to coordinate external computing resources to improve the performance of IoT. Fortunately, the blockchain may provide this decentralization, high credibility and high security. Consequently, blockchain-based IoT may become a reasonable choice for the design of a decentralized IoT system. In this paper, we propose a novel blockchain-based threshold IoT service system: BeeKeeper. In the BeeKeeper system, servers can process a user’s data by performing homomorphic computations on the data without learning anything from them. Furthermore, any node can become a leader’s server if the node and the leader desire so. In this way, BeeKeeper’s performance can continually increase by attracting external computing resources to join in it. Moreover, malicious nodes can be scrutinized. In addition, BeeKeeper is fault tolerant since a user’s BeeKeeper protocol may work smoothly as long as a threshold number of its servers are active and honest. Finally, we deploy BeeKeeper on the Ethereum blockchain and give the corresponding performance evaluation. In our experiments, servers can generate their response with about 107 ms. Moreover, the performance of BeeKeeper mainly depends on the blockchain platform. For instance, the response time is about 22.5 s since the block interval of Ethereum blockchain is about 15 s. In fact, if we use some other blockchain with short block interval, the response time may be obviously short.


title: How to model fake news

abstract: Over the past three years it has become evident that fake news is a danger to democracy. However, until now there has been no clear understanding of how to define fake news, much less how to model it. This paper addresses both these issues. A definition of fake news is given, and two approaches for the modelling of fake news and its impact in elections and referendums are introduced. The first approach, based on the idea of a representative voter, is shown to be suitable to obtain a qualitative understanding of phenomena associated with fake news at a macroscopic level. The second approach, based on the idea of an election microstructure, describes the collective behaviour of the electorate by modelling the preferences of individual voters. It is shown through a simulation study that the mere knowledge that pieces of fake news may be in circulation goes a long way towards mitigating the impact of fake news.


title: Parle: parallelizing stochastic gradient descent

abstract: We propose a new algorithm called Parle for parallel training of deep networks that converges 2-4x faster than a data-parallel implementation of SGD, while achieving significantly improved error rates that are nearly state-of-the-art on several benchmarks including CIFAR-10 and CIFAR-100, without introducing any additional hyper-parameters. We exploit the phenomenon of flat minima that has been shown to lead to improved generalization error for deep networks. Parle requires very infrequent communication with the parameter server and instead performs more computation on each client, which makes it well-suited to both single-machine, multi-GPU settings and distributed implementations.


title: Distributing the Kalman Filter for Large-Scale Systems

abstract: This paper derives a \emph{distributed} Kalman filter to estimate a sparsely connected, large-scale, $n-$dimensional, dynamical system monitored by a network of $N$ sensors. Local Kalman filters are implemented on the ($n_l-$dimensional, where $n_l\ll n$) sub-systems that are obtained after spatially decomposing the large-scale system. The resulting sub-systems overlap, which along with an assimilation procedure on the local Kalman filters, preserve an $L$th order Gauss-Markovian structure of the centralized error processes. The information loss due to the $L$th order Gauss-Markovian approximation is controllable as it can be characterized by a divergence that decreases as $L\uparrow$. The order of the approximation, $L$, leads to a lower bound on the dimension of the sub-systems, hence, providing a criterion for sub-system selection. The assimilation procedure is carried out on the local error covariances with a distributed iterate collapse inversion (DICI) algorithm that we introduce. The DICI algorithm computes the (approximated) centralized Riccati and Lyapunov equations iteratively with only local communication and low-order computation. We fuse the observations that are common among the local Kalman filters using bipartite fusion graphs and consensus averaging algorithms. The proposed algorithm achieves full distribution of the Kalman filter that is coherent with the centralized Kalman filter with an $L$th order Gaussian-Markovian structure on the centralized error processes. Nowhere storage, communication, or computation of $n-$dimensional vectors and matrices is needed; only $n_l \ll n$ dimensional vectors and matrices are communicated or used in the computation at the sensors.


title: Real time adaptive nonlinear model inversion control of a twin rotor MIMO system using neural networks

abstract: This paper investigates the development and experimental implementation of an adaptive dynamic nonlinear model inversion control law for a Twin Rotor MIMO System (TRMS) using artificial neural networks. The TRMS is a highly nonlinear aerodynamic test rig with complex cross-coupled dynamics and therefore represents the control challenges of modern air vehicles. A highly nonlinear 1DOF mathematical model of the TRMS is considered in this study and a nonlinear inverse model is developed for the pitch channel of the system. An adaptive neural network element is integrated thereafter with the feedback control system to compensate for model inversion errors. The proposed on-line learning algorithm updates the weights and biases of the neural network using the error between the set-point and the real output. The real-time response of the method shows a satisfactory tracking performance in the presence of inversion errors caused by model uncertainty. The approach is therefore deemed to be suitable to apply real-time to other nonlinear systems with necessary modifications.


title: Automatic gait recognition based on statistical shape analysis

abstract: Gait recognition has recently gained significant attention from computer vision researchers. This interest is strongly motivated by the need for automated person identification systems at a distance in visual surveillance and monitoring applications. The paper proposes a simple and efficient automatic gait recognition algorithm using statistical shape analysis. For each image sequence, an improved background subtraction procedure is used to extract moving silhouettes of a walking figure from the background. Temporal changes of the detected silhouettes are then represented as an associated sequence of complex vector configurations in a common coordinate frame, and are further analyzed using the Procrustes shape analysis method to obtain mean shape as gait signature. Supervised pattern classification techniques, based on the full Procrustes distance measure, are adopted for recognition. This method does not directly analyze the dynamics of gait, but implicitly uses the action of walking to capture the structural characteristics of gait, especially the shape cues of body biometrics. The algorithm is tested on a database consisting of 240 sequences from 20 different subjects walking at 3 viewing angles in an outdoor environment. Experimental results are included to demonstrate the encouraging performance of the proposed algorithm.


title: Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing

abstract: Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms


title: Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds

abstract: This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning, and features combining the point clouds of both epochs to identify the change. All these features are merged in the points and then training samples are acquired to create the model for supervised classification, which is then applied to the whole study area. The final results reach an overall accuracy of over 90% for both epochs of eight classes: lost tree, new tree, lost building, new building, changed ground, unchanged building, unchanged tree, and unchanged ground.


title: A Review of Lower Extremity Assistive Robotic Exoskeletons in Rehabilitation Therapy

abstract: The rapid advancement of robotics technology in recent years has pushed the development of a distinctive field of robotic applications, namely robotic exoskeletons. Because of the aging population, more people are suffering from neurological disorders such as stroke, central nervous system disorder, and spinal cord injury. As manual therapy seems to be physically demanding for both the patient and therapist, robotic exoskeletons have been developed to increase the efficiency of rehabilitation therapy. Robotic exoskeletons are capable of providing more intensive patient training, better quantitative feedback, and improved functional outcomes for patients compared to manual therapy. This review emphasizes treadmill-based and over-ground exoskeletons for rehabilitation. Analyses of their mechanical designs, actuation systems, and integrated control strategies are given priority because the interactions between these components are crucial for the optimal performance of the rehabilitation robot. The review also discusses the limitations of current exoskeletons and technical challenges faced in exoskeleton development. A general perspective of the future development of more effective robot exoskeletons, specifically real-time biological synergy-based exoskeletons, could help promote brain plasticity among neurologically impaired patients and allow them to regain normal walking ability.


title: Is pain relief after vaginal mesh and/or sling removal durable long term?

abstract: INTRODUCTION AND HYPOTHESIS ::: This study was to review our experience of pain relief durability in women who experienced initial pain resolution after vaginal mesh and/or sling removal (VMSR). ::: ::: ::: METHODS ::: A retrospective chart review of consecutive, nonneurogenic women who underwent VMSR for pain and reported persistent pain relief at the 6-month postoperative follow-up visit were assessed. Pre- and postoperative data collected were self-reported chief complaints, physical examination, other medical conditions associated with chronic pain, use of pain medications, Urogenital Distress Inventory-6 (in which question 6 specifically addresses pain), and the Numerical Pain Rating Scale (NPRS). Success was defined by continued resolution of pain assessed by score of 0 on NPRS (0 none to 10 terrible), (0-1 on the Urogenital Distress Inventory-6 (UDI-6) question 6, subjective report of pain-free status, and/or no chronic use of pain medications other than those prescribed for non-mesh-related pain. ::: ::: ::: RESULTS ::: Between 2006 and 2015, 125 of 356 women met study criteria. Mean follow-up after VMSR was 3.5 (range 0.5-10) years. Among 25 women who did not meet success criteria by questionnaire answers or subjective report, 21 had causes unrelated to their original mesh/sling placement and were pain free after they were addressed. Four women experienced delayed mesh-related pain return at 28, 46, 47, and 54 months, respectively; two required mesh removal surgery. Involvement in lawsuits and chronic pain-related medical conditions did not affect the durability of pain relief. ::: ::: ::: CONCLUSIONS ::: At a mean follow-up of 3.5 years, the original pain relief noted after VMSR was durably maintained.


title: Ultrasonic Damage Detection of Impacted Long and Short Fibre Composite Specimens

abstract: Composite materials structures are particularly susceptible to the damages induced by low-velocity impacts that may result in Barely Visible Impact Damages (BVIDs), which can hardly be identified through visual inspection. These damages are particularly dangerous, since they can critically reduce the mechanical properties of the impacted structures. In this work, the damage induced in impacted long and short fibre composite specimens has been experimentally evaluated by means of Non-Destructive Technique (NDT) inspections. The damages size and location have been evaluated by means of ultrasonic testing to assess the influence of fibres aspect ratio (long and short fibres), fibres material (carbon and glass), volume fraction, and impact energy for low velocity impacts on composite specimens. Considerations about the failure mechanisms arising as a consequence of the impact event and their interactions have been finally introduced.


title: An implemented theory of mind to improve human-robot shared plans execution

abstract: When a robot has to execute a shared plan with a human, a number of unexpected situations and contingencies can happen due, essentially, to human initiative. For instance, a temporary absence or inattention of the human can entail a partial, and potentially not sufficient, knowledge about the current situation. To ensure a successful and fluent execution of the shared plan the robot might need to detect such situations and be able to provide the information to its human partner about what he missed without being annoying or intrusive. To do so, we have developed a framework which allows a robot to estimate the other agents mental states not only about the environment but also about the state of goals, plans and actions and to take them into account when executing human-robot shared plans.


title: Rogue-Access-Point Detection: Challenges, Solutions, and Future Directions

abstract: Rogue devices are an increasingly dangerous reality in the insider threat problem domain. Industry, government, and academia need to be aware of this problem and promote state-of-the-art detection methods.


title: Gender stereotypes stem from the distribution of women and men into social roles.

abstract: According to stereotypic beliefs about the sexes, women are more communal (selfless and concerned with others) and less agentic (self-assertive and motivated to master) than men. These beliefs were hypothesized to stem from perceivers' observations of women and men in differing social roles: (a) Women are more likely than men to hold positions of lower status and authority, and (b) women are more likely than men to be homemakers and are less likely to be employed in the paid work force. Experiments 1 and 2 failed to support the hypothesis that observed sex differences in status underlie belief in female communal qualities and male agentic qualities. Experiment 3 supported the hypothesis that observed sex differences in distribution into homemaker and employee occupational roles account for these beliefs. In this experiment, subjects perceived the average woman and man stereotypically. Female and male homemakers were perceived as high in communion and low in agency. Female and male employees were perceived as low in communion and high in agency, although female employees were perceived as even more agentic than their male counterparts. Experiments 4 and 5 examined perceptions that might account for the belief that employed women are especially agentic: (a) A double burden of employment plus family responsibilities did not account for this belief, and (b) freedom of choice about being employed accounted for it reasonably well.


title: A FPGA-based wearable ultrasound device for monitoring obstructive sleep apnea syndrome

abstract: Obstructive sleep apnea (OSA), a breathing disorder characterized by repetitive collapse of the pharyngeal airway during sleep, can cause intermittent hypoxemia and frequent arousal. Many studies have demonstrated that the deformation of tongue base plays an important role in OSA. Currently, several medical imaging modalities have been used to record the airway changes, such as cine MRI and ultrafast CT. However, most of them are difficult to be used widely in clinical diagnosis for continuous recording the deformation of tongue in real-time, particularly during sleeping. In order to overcome these problems, a wearable ultrasonic device was developed for real-time monitor of the dynamic change of the tongue base in this study. The device includes a custom-designed 3.5-MHz ultrasonic array transducer, an ultrasound pulser/receiver, high-speed analog-to-digital converter, high speed digital I/O, and a FPGA board. The ultrasound pulse repetition frequency and pulse center frequency were controlled by the programmable FPGA board. The anthropometric measurement was carried out for verifying the system performance. Results demonstrated that the dynamic variation of tongue base can be clearly distinguished by using this wearable ultrasound.


title: Self-control relies on glucose as a limited energy source: Willpower is more than a metaphor

abstract: The present work suggests that self-control relies on glucose as a limited energy source. Laboratory tests of self-control (i.e., the Stroop task, thought suppression, emotion regulation, attention control) and of social behaviors (i.e., helping behavior, coping with thoughts of death, stifling prejudice during an interracial interaction) showed that (a) acts of self-control reduced blood glucose levels, (b) low levels of blood glucose after an initial self-control task predicted poor performance on a subsequent self-control task, and (c) initial acts of self-control impaired performance on subsequent self-control tasks, but consuming a glucose drink eliminated these impairments. Self-control requires a certain amount of glucose to operate unimpaired. A single act of self-control causes glucose to drop below optimal levels, thereby impairing subsequent attempts at self-control.


title: Cognitive, performance, and systems issues for augmented reality applications in manufacturing and maintenance

abstract: This paper presents cognitive studies and analyses relating to how augmented reality (AR) interacts with human abilities in order to benefit manufacturing and maintenance tasks. A specific set of applications is described in detail, as well as a prototype system and the software library that it is built upon. An integrated view of information flow to support AR is also presented, along with a proposal for an AR media language (ARML) that could provide interoperability between various AR implementations.


title: Lessons from non-canonical splicing

abstract: Advances in transcriptomics and analysis have identified thousands of previously unknown non-canonical splicing events. In this Review, the authors discuss the mechanisms and functions of these events and their roles in a variety of diseases. They explain how non-canonical splicing mechanisms can be targeted or exploited for therapeutic strategies.


title: TANDEM-bottleneck feature combination using hierarchical Deep Neural Networks

abstract: To improve speech recognition performance, a combination between TANDEM and bottleneck Deep Neural Networks (DNN) is investigated. In particular, exploiting a feature combination performed by means of a multi-stream hierarchical processing, we show a performance improvement by combining the same input features processed by different neural networks. The experiments are based on the spontaneous telephone recordings of the Cantonese IARPA Babel corpus using both standard MFCCs and Gabor as input features.


title: Squeezing Deep Learning into Mobile and Embedded Devices

abstract: This department provides an overview the progress the authors have made to the emerging area of embedded and mobile forms of on-device deep learning. Their work addresses two core technical questions. First, how should deep learning principles and algorithms be applied to sensor inference problems that are central to this class of computing? Second, what is required for current and future deep learning innovations to be efficiently integrated into a variety of mobile resource-constrained systems? Toward answering such questions, the authors describe phone, watch, and embedded prototypes that can locally run large-scale deep networks processing audio, images, and inertial sensor data. These prototypes are enabled with a variety of algorithmic and system-level innovations that vastly reduce conventional inference-time overhead of deep models.


title: Steganalysis of LSB matching in grayscale images

abstract: We consider the problem of detecting spatial domain least significant bit (LSB) matching steganography in grayscale images, which has proved much harder than for its counterpart, LSB replacement. We use the histogram characteristic function (HCF), introduced by Harmsen for the detection of steganography in color images but ineffective on grayscale images. Two novel ways of applying the HCF are introduced: calibrating the output using a downsampled image and computing the adjacency histogram instead of the usual histogram. Extensive experimental results show that the new detectors are reliable, vastly more so than those previously known.


title: Augmented Reality Fashion Apparel Simulation using a Magic Mirror

abstract: Digital technology innovations have led to significant changes in everyday life, made possible by the widespread use of computers and continuous developments in information technology (IT). Based on the utilization of systems applying 3D(three-dimensional) technology, as well as virtual and augmented reality techniques, IT has become the basis for a new fashion industry model, featuring consumer-centered service and production methods. Because of rising wages and production costs, the fashion industry’s international market power has been significantly weakened in recent years. To overcome this situation, new markets must be established by building a new knowledge and technology-intensive fashion industry. Development of virtual clothing simulation software, which has played an important role in the fashion industry’s IT-based digitalization, has led to continuous technological improvements for systems that can virtually adapt existing 2D(two-dimensional) design work to 3D design work. Such adaptions have greatly influenced the fashion industry by increasing profits. Both here and abroad, studies have been conducted to support the development of consumercentered, high value-added clothing and fashion products by employing digital technology. This study proposes a system that uses a depth camera to capture the figure of a user standing in front of a large display screen. The display can show fashion concepts and various outfits to the user, coordinated to his or her body. Thus, a “magic mirror” effect is produced. Magic mirror-based fashion apparel simulation can support total fashion coordination for accessories and outfits automatically, and does not require computer or fashion expertise. This system can provide convenience for users by assuming the role of a professional fashion coordinator giving an appearance presentation. It can also be widely used to support a customized method for clothes shopping.


OpenFace: A general-purpose face recognition library with mobile applications


title: CAMA: contact-aware matrix assembly with unified collision handling for GPU-based cloth simulation

abstract: We present a novel GPU-based approach to robustly and efficiently simulate high-resolution and complexly layered cloth. The key component of our formulation is a parallelized matrix assembly algorithm that can quickly build a large and sparse matrix in a compressed format and accurately solve linear systems on GPUs. We also present a fast and integrated solution for parallel collision handling, including collision detection and response computations, which utilizes spatio-temporal coherence. We combine these algorithms as part of a new cloth simulation pipeline that incorporates contact forces into implicit time integration for collision avoidance. The entire pipeline is implemented on GPUs, and we evaluate its performance on complex benchmarks consisting of 100 -- 300K triangles. In practice, our system takes a few seconds to simulate one frame of a complex cloth scene, which represents significant speedups over prior CPU and GPU-based cloth simulation systems.


title: Food Recommendation System Using Sequential Pattern Mining

abstract: Sequential pattern mining is an important subfield in data mining. In this paper, we introduce a sequential pattern mining-based food recommendation system. The purchase history of users is analyzed to find their sequential patterns using SPADE algorithm [5]. These patterns are then used to predict the next possible purchase. One of these patterns will be shown as a special offer with discount. The proposed approach is experimented on real transaction data. It demonstrate that the proposed system effectively improves the efficiency for mining sequential patterns, increases the user-relevance of the identified sequential patterns, and most importantly, generates significantly more accurate next-items recommendation for the target users.


title: Ultrawideband PIFA With a Capacitive Feed for Penta-Band Folder-Type Mobile Phone Antenna

abstract: An ultrawideband planar inverted-F antenna (PIFA) with a capacitive feed in the folder-type or clamshell mobile phone for penta-band operation is presented. The antenna is placed at the hinge position between the main and upper ground planes of the folder-type mobile phone. With the use of the capacitive feed, a very wide operating bandwidth (3:1 VSWR or 6-dB return loss) of about 1.5 GHz (from 820 to 2345 MHz) is obtained, although the antenna is with a small radiating plate of size 8 times 40 mm2 and a low profile of 7.8 mm above the top edge of the main ground plane. The very wide bandwidth covers GSM850/900/1800/1900/UMTS penta-band operation for the mobile phone in the open (talk) state. In addition, when in the closed (standby) state, although the groundplane condition greatly differs from that in the open state, the impedance matching over the operating bands can still be better than 6:1 VSWR (3-dB return loss), acceptable for standby operation in the practical applications.


title: Selective isolation of the electron or hole in photocatalysis: ZnO–TiO2 and TiO2–ZnO core–shell structured heterojunction nanofibers via electrospinning and atomic layer deposition

abstract: Heterojunctions are a well-studied material combination in photocatalysis studies, the majority of which aim to improve the efficacy of the catalysts. Developing novel catalysts begs the question of which photo-generated charge carrier is more efficient in the process of catalysis and the associated mechanism. To address this issue we have fabricated core–shell heterojunction (CSHJ) nanofibers from ZnO and TiO2 in two combinations where only the ‘shell’ part of the heterojunction is exposed to the environment to participate in the photocatalysis. Core and shell structures were fabricated via electrospinning and atomic layer deposition, respectively which were then subjected to calcination. These CSHJs were characterized and studied for photocatalytic activity (PCA). These two combinations expose electrons or holes selectively to the environment. Under suitable illumination of the ZnO–TiO2 CSHJ, e/h pairs are created mainly in TiO2 and the electrons take part in catalysis (i.e. reduce the organic dye) at the conduction band or oxygen vacancy sites of the ‘shell’, while holes migrate to the core of the structure. Conversely, holes take part in catalysis and electrons diffuse to the core in the case of a TiO2–ZnO CSHJ. The results further revealed that the TiO2–ZnO CSHJ shows ∼1.6 times faster PCA when compared to the ZnO–TiO2 CSHJ because of efficient hole capture by oxygen vacancies, and the lower mobility of holes.


title: Occurrence of coring after needle insertion through a rubber stopper: study with prednisolone acetate

abstract: OBJECTIVES ::: To evaluate the occurrence of coring after needle insertion through the rubber stopper of prednisolone acetate vials. ::: ::: ::: METHODS ::: Two-hundred vials of prednisolone acetate were randomly distributed to two radiologists. Prednisolone acetate was drawn up through the rubber bung of the vials with an 18-gauge cutting bevelled needle and aspirated with a 5-ml syringe. The presence of coring was noted visually. We systematically put each core in a syringe refilled with 3 ml prednisolone acetate, and injected the medication through a 20-gauge spine needle. Computed tomography was performed to measure the size of each coring. ::: ::: ::: RESULTS ::: Coring occurred in 21 out of 200 samples (10.5 %), and was visually detected in the syringe filled up with prednisolone in 11 of the 21 cases. Ten more occult cores were detected only after the syringes and needles were taken apart and rinsed. The core size ranged from 0.6 to 1.1 mm, and 1 of the 21 (4.7 %) cores was ejected through the 20-gauge needle. ::: ::: ::: CONCLUSION ::: Coring can occur after the insertion of a needle through the rubber stopper of a vial of prednisolone acetate, and the resultant core can then be aspirated into the syringe.


title: T2API: synthesizing API code usage templates from English texts with statistical translation

abstract: In this work, we develop T2API, a statistical machine translation-based tool that takes a given English description of a programming task as a query, and synthesizes the API usage template for the task by learning from training data. T2API works in two steps. First, it derives the API elements relevant to the task described in the input by statistically learning from a StackOverflow corpus of text descriptions and corresponding code. To infer those API elements, it also considers the context of the words in the textual input and the context of API elements that often go together in the corpus. The inferred API elements with their relevance scores are ensembled into an API usage by our novel API usage synthesis algorithm that learns the API usages from a large code corpus via a graph-based language model. Importantly, T2API is capable of generating new API usages from smaller, previously-seen usages.


title: A method to compute distance between two categorical values of same attribute in unsupervised learning for categorical data set

abstract: Computation of similarity between categorical data objects in unsupervised learning is an important data mining problem. We propose a method to compute distance between two attribute values of same attribute for unsupervised learning. This approach is based on the fact that similarity of two attribute values is dependent on their relationship with other attributes. Computational cost of this method is linear with respect to number of data objects in data set. To see the effectiveness of our proposed distance measure, we use proposed distance measure with K-mode clustering algorithm to cluster various categorical data sets. Significant improvement in clustering accuracy is observed as compared to clustering results obtained using traditional K-mode clustering algorithm.


title: Review Article: Multimodal interaction: A review

abstract: People naturally interact with the world multimodally, through both parallel and sequential use of multiple perceptual modalities. Multimodal human-computer interaction has sought for decades to endow computers with similar capabilities, in order to provide more natural, powerful, and compelling interactive experiences. With the rapid advance in non-desktop computing generated by powerful mobile devices and affordable sensors in recent years, multimodal research that leverages speech, touch, vision, and gesture is on the rise. This paper provides a brief and personal review of some of the key aspects and issues in multimodal interaction, touching on the history, opportunities, and challenges of the area, especially in the area of multimodal integration. We review the question of early vs. late integration and find inspiration in recent evidence in biological sensory integration. Finally, we list challenges that lie ahead for research in multimodal human-computer interaction.


title: Self-perceived attractiveness influences human female preferences for sexual dimorphism and symmetry in male faces

abstract: Exaggerated sexual dimorphism and symmetry in human faces have both been linked to potential 'good-gene' benefits and have also been found to influence the attractiveness of male faces. The current study explores how female self-rated attractiveness influences male face preference in females using faces manipulated with computer graphics. The study demonstrates that there is a relatively increased preference for masculinity and an increased preference for symmetry for women who regard themselves as attractive. This finding may reflect a condition-dependent mating strategy analogous to behaviours found in other species. The absence of a preference for proposed markers of good genes may be adaptive in women of low mate value to avoid the costs of decreased parental investment from the owners of such characteristics.


title: THz imaging and wireless communication using nanotransistor based detectors: From basic physics to first real world applications

abstract: In this paper, an overview of main results concerning THz detection by nanometer field effect transistors (FETs) and heterojunction based transistors (HBTs) is presented. Different GaInAs/InP and GaN/AlGaN nanometer field effect transistors based detectors are presented. We present also first Silicon C-MOS transistors based integrated circuits for wireless communication in sub-THz range. Special attention is given to transistors based focal plane arrays. We show, how these arrays, together with in purpose developed diffractive 3D printed optics lead to construction of the demonstrators of safety and industrial quality control scanners.


Information propagation in interaction networks


title: Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes

abstract: Viewpoint independent recognition of free-form objects and their segmentation in the presence of clutter and occlusions is a challenging task. We present a novel 3D model-based algorithm which performs this task automatically and efficiently. A 3D model of an object is automatically constructed offline from its multiple unordered range images (views). These views are converted into multidimensional table representations (which we refer to as tensors). Correspondences are automatically established between these views by simultaneously matching the tensors of a view with those of the remaining views using a hash table-based voting scheme. This results in a graph of relative transformations used to register the views before they are integrated into a seamless 3D model. These models and their tensor representations constitute the model library. During online recognition, a tensor from the scene is simultaneously matched with those in the library by casting votes. Similarity measures are calculated for the model tensors which receive the most votes. The model with the highest similarity is transformed to the scene and, if it aligns accurately with an object in the scene, that object is declared as recognized and is segmented. This process is repeated until the scene is completely segmented. Experiments were performed on real and synthetic data comprised of 55 models and 610 scenes and an overall recognition rate of 95 percent was achieved. Comparison with the spin images revealed that our algorithm is superior in terms of recognition rate and efficiency


title: Classification of Cotton Leaf Spot Disease Using Support Vector Machine

abstract: In order to obtain more value added products, a product quality control is essentially required Many studies show that quality of agriculture products may be reduced from many causes. One of the most important factors of such quality plant diseases. Consequently, minimizing plant diseases allows substantially improving quality of the product Suitable diagnosis of crop disease in the field is very critical for the increased production. Foliar is the major important fungal disease of cotton and occurs in all growing Indian cotton regions. In this paper I express Technological Strategies uses mobile captured symptoms of Cotton Leaf Spot images and categorize the diseases using support vector machine. The classifier is being trained to achieve intelligent farming, including early detection of disease in the groves, selective fungicide application, etc. This proposed work is based on Segmentation techniques in which, the captured images are processed for enrichment first. Then texture and color Feature extraction techniques are used to extract features such as boundary, shape, color and texture for the disease spots to recognize diseases.


title: Deep Dense Multi-level feature for partial high-resolution fingerprint matching

abstract: Fingerprint sensors on mobile devices commonly have limited area, which results in partial fingerprints. Optical sensor can capture fingerprints at very high resolution (2000ppi) with abundant details like pores, incipients, etc. It is quite crucial to develop effective partial-to-partial high-resolution fingerprint matching algorithms. Existing fingerprint matching methods are mainly minutiae-based, with fusion of different levels of features. Their accuracy degrades significantly in our application due to minutiae insufficiency and detection error. In this paper, we propose a novel representation for partial high-resolution fingerprint, named Deep Dense Multi-level feature (DDM). We train a deep convolutional neural network that can extract discriminative features inside any local fingerprint block with certain size. We find that not only minutiae but most local blocks contain sufficient features. Moreover, we analyze DDM and find that it contains multi-level information. When utilizing DDM for partial-to-partial matching, we first extract features block by block through a fully convolutional network, next match the two sets of features pairwise exhaustively, and then select the bi-directional best matches to compute matching score. Experiments indicate that our method outperforms several state-of-the-art approaches.


title: Accelerating Encryption Algorithms Using Parallelism

abstract: The importance of protecting the information has increased rapidly during the last decades and as a consequence so did the need for cryptographic algorithms. We want to increase the speed of these methods that protect our data as much as we can. In this paper , we used parallelism for encryption algorithms and I/O operations to emphasize the benefit of it, by implementing and improving two cryptographic modes such as Cipher Block Chaining (CBC ) and Interleaved CBC (ICBC ) for Advanced Encryption Algorithm (AES ). The result of this paper is the remarkable speedup in encryption and decryption time of cryptographic systems when using parallel paradigms (OpenCL, Cuda, OpenMP) and the comparison of the serial versus parallel implementation.


title: Cost Efficiency Optimization of 5G Wireless Backhaul Networks

abstract: To cater for the demands of future fifth generation (5G) ultra-dense small cell networks, the wireless backhaul network is an attractive solution for the urban deployment of 5G wireless networks. Optimization of 5G wireless backhaul networks is a key issue. In this paper we propose a two-scale optimization solution to maximize the cost efficiency of 5G wireless backhaul networks. Specifically, the number and positions of gateways are optimized in the long time scale of 5G wireless backhaul networks. The wireless backhaul routings are optimized in the short time scale of 5G wireless backhaul networks. Numerical results show the cost efficiency of proposed optimization algorithm is better than the cost efficiency of conventional and most widely used shortest path (SP) algorithm in 5G wireless backhaul networks.


title: Deep image mining for diabetic retinopathy screening

abstract: Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert knowledge about the target pathologies. However, deep learning algorithms, including the popular ConvNets, are black boxes: little is known about the local patterns analyzed by ConvNets to make a decision at the image level. A solution is proposed in this paper to create heatmaps showing which pixels in images play a role in the image-level predictions. In other words, a ConvNet trained for image-level classification can be used to detect lesions as well. A generalization of the backpropagation method is proposed in order to train ConvNets that produce high-quality heatmaps. The proposed solution is applied to diabetic retinopathy (DR) screening in a dataset of almost 90,000 fundus photographs from the 2015 Kaggle Diabetic Retinopathy competition and a private dataset of almost 110,000 photographs (e-ophtha). For the task of detecting referable DR, very good detection performance was achieved: Az=0.954 in Kaggle's dataset and Az=0.949 in e-ophtha. Performance was also evaluated at the image level and at the lesion level in the DiaretDB1 dataset, where four types of lesions are manually segmented: microaneurysms, hemorrhages, exudates and cotton-wool spots. For the task of detecting images containing these four lesion types, the proposed detector, which was trained to detect referable DR, outperforms recent algorithms trained to detect those lesions specifically, with pixel-level supervision. At the lesion level, the proposed detector outperforms heatmap generation algorithms for ConvNets. This detector is part of the Messidor® system for mobile eye pathology screening. Because it does not rely on expert knowledge or manual segmentation for detecting relevant patterns, the proposed solution is a promising image mining tool, which has the potential to discover new biomarkers in images.


title: Localized forgery detection in hyperspectral document images

abstract: Hyperspectral imaging is emerging as a promising technology to discover patterns that are otherwise hard to identify with regular cameras. Recent research has shown the potential of hyperspectral image analysis to automatically distinguish visually similar inks. However, a major limitation of prior work is that automatic distinction only works when the number of inks to be distinguished is known a priori and their relative proportions in the inspected image are roughly equal. This research work aims at addressing these two problems. We show how anomaly detection combined with unsupervised clustering can be used to handle cases where the proportions of pixels belonging to the two inks are highly unbalanced. We have performed experiments on the publicly available UWA Hyperspectral Documents dataset. Our results show that INFLO anomaly detection algorithm is able to best distinguish inks for highly unbalanced ink proportions.