title: A region based active contour method for x-ray lung segmentation using prior shape and low level features

abstract: In this work, a level set energy for segmenting the lungs from digital Posterior-Anterior (PA) chest x-ray images is presented. The primary challenge in using active contours for lung segmentation is local minima due to shading effects and presence of strong edges due to the rib cage and clavicle. We have used the availability of good contrast at the lung boundaries to extract a multi-scale set of edge/corner feature points and drive our active contour model using these features. We found these features when supplemented with a simple region based data term and a shape term based on the average lung shape, able to handle the above local minima issues. The algorithm was tested on 1130 clinical images, giving promising results.


title: Automatic Contract Insertion with CCBot

abstract: Existing static analysis tools require significant programmer effort. On large code bases, static analysis tools produce thousands of warnings. It is unrealistic to expect users to review such a massive list and to manually make changes for each warning. To address this issue we propose CCBot (short for C ode C ontracts Bot ), a new tool that applies the results of static analysis to existing code through automatic code transformation. Specifically, CCBot instruments the code with method preconditions, postconditions, and object invariants which detect faults at runtime or statically using a static contract checker. The only configuration the programmer needs to perform is to give CCBot the file paths to code she wants instrumented. This allows the programmer to adopt contract-based static analysis with little effort. CCBot's instrumented version of the code is guaranteed to compile if the original code did. This guarantee means the programmer can deploy or test the instrumented code immediately without additional manual effort. The inserted contracts can detect common errors such as null pointer dereferences and out-of-bounds array accesses. CCBot is a robust large-scale tool with an open-source C# implementation. We have tested it on real world projects with tens of thousands of lines of code. We discuss several projects as case studies, highlighting undiscovered bugs found by CCBot, including 22 new contracts that were accepted by the project authors.


title: Multi-Object Tracking Through Simultaneous Long Occlusions and Split-Merge Conditions

abstract: A fundamental requirement for effective automated analysis of object behavior and interactions in video is that each object must be consistently identified over time. This is difficult when the objects are often occluded for long periods: nearly all tracking algorithms will terminate a track with loss of identity on a long gap. The problem is further confounded by objects in close proximity, tracking failures due to shadows, etc. Recently, some work has been done to address these issues using higher level reasoning, by linking tracks from multiple objects over long gaps. However, these efforts have assumed a one-to-one correspondence between tracks on either side of the gap. This is often not true in real scenarios of interest, where the objects are closely spaced and dynamically occlude each other, causing trackers to merge objects into single tracks. In this paper, we show how to efficiently handle splitting and merging during track linking. Moreover, we show that we can maintain the identities of objects that merge together and subsequently split. This enables the identity of objects to be maintained throughout long sequences with difficult conditions. We demonstrate our approach on a highly challenging, oblique-view video sequence of dense traffic of a highway interchange. We successfully track the large majority of the hundreds of moving vehicles in the scene, many in close proximity, through long occlusions and shadows.


title: Cognitive Tutors: Lessons Learned

abstract: This paper review the 10-year history of tutor development based on the ACT theory (Anderson, 1983,1993). We developed production system models in ACT ofhow students solved problems in LISP, geometry, and algebra. Computer tutors were developed around these cognitive models. Construction ofthese tutors was guided by a set of eight principles loosely based on the ACT theory. Early evaluations of these tutors usually but not always showed significant achievement gains. Best-case evaluations showed that students could achieve at least the same level of proficiency as conventional instruction in one third the time. Empirical studies showed that students were learning skills in production-rule units and that the best tutorial interaction style was one in which the tutor provides immediate feedback, consisting of short and directed error messages. The tutors appear to work better if they present themselves to students as nonhuman tools to assist learning rather than as emulations of human tutors. Students working with these tutors display transfer to other environments to the degree that they can map the tutor environment into the test environment. These experiences have coalesced into a new system for developing and deploying tutors. This system involves first selecting a problem-solving interface, then constructing a curriculum under the guidance of a domain expert, then designing a cognitive model for solving problems in that environment, then building instruction around the productions in that model, and finally deploying the tutor in the classroom. New tutors are being built in this system to achieve the NCTM standards for high school mathematics in an urban setting. (http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA312246)


title: Mirror Mirror: An On-Body T-shirt Design System

abstract: Virtual fitting rooms equipped with magic mirrors let people evaluate fashion items without actually putting them on. The mirrors superimpose virtual clothes on the user's reflection. We contribute the Mirror Mirror system, which not only supports mixing and matching of existing fashion items, but also lets users design new items in front of the mirror and export designs to fabric printers. While much of the related work deals with interactive cloth simulation on live user data, we focus on collaborative design activities and explore various ways of designing on the body with a mirror.


Antibiotics for preschool wheeze


title: Transform Coding Techniques in HEVC

abstract: High Efficiency Video Coding (HEVC) is the most recent jointly developed video coding standard of ITU-T Visual Coding Experts Group (VCEG) and ISO/IEC Moving Picture Experts Group (MPEG). Although its basic architecture is built along the conventional hybrid block-based approach of combining prediction with transform coding, HEVC includes a number of coding tools with greatly enhanced coding-efficiency capabilities relative to those of prior video coding standards. Among these tools are new transform coding techniques that include the support for dyadically increasing transform block sizes ranging from 4 × 4 to 32 × 32, the partitioning of residual blocks into variable block-size transforms by using a quadtree-based partitioning dubbed as residual quadtree (RQT) as well as some properly designed entropy coding techniques for quantized transform coefficients of variable transform block sizes. In this paper, we describe these HEVC techniques for transform coding with a particular focus on the RQT structure and the entropy coding stage and demonstrate their benefit in terms of improved coding efficiency by experimental results.


title: Acetazolamide pre-treatment before ascending to high altitudes: when to start?

abstract: Hypoxia is the main responsible factor initiating the symptoms of acute mountain sickness (AMS) in susceptible individuals. Measures that improve oxygenation and/or hasten acclimatization like pre-treatment with acetazolamide will prevent the development of AMS. We hypothesized that pre-treatment with acetazolamide the day before arrival at high altitude would elicit improved oxygenation compared to placebo not until the second day of high-altitude exposure. Fifteen study participants were randomly assigned in a double blind fashion to receive placebo or acetazolamide (2 × 125 mg) before (10 hours and 1 hour) exposure to high altitude (Monte Rosa plateau, 3480 m). Beside AMS scoring, heart rate, minute ventilation, and blood gas analyses were performed during rest and submaximal exercise at low altitude and on day 1, 2 and 3 at high altitude. From low altitude to day 1 at high altitude changes of pH (7.41 ± 0.01 vs. 7.48 ± 0.04) and HCO3 (24.0 ± 0.46 vs. 24.6 ± 2.6 mmol/L) within the placebo group differed significantly from those within the acetazolamide group (7.41 ± 0.01 vs. 7.41 ± 0.02; 23.6 ± 0.38 vs. 20.7 ± 1.8 mmol/L) (P < 0.05). AMS incidence tended to be lower with acetazolamide (P < 0.1). From low altitude to day 2 at high altitude changes of paO2 within the placebo group (75.3 ± 5.4 vs. 40.5 ± 3.4 mmHg) differed significantly from those within the acetazolamide group (76.5 ± 4.5 vs. 48.2 ± 4.9 mmHg) (P < 0.05). In conclusion, pre-treatment with low-dose acetazolamide on the day before ascending to high altitude tended to reduce AMS incidence on the first day at high altitude but improved oxygen availability to tissues not until the second day of exposure. Therefore, it is suggested that the beginning of pre-treatment with low-dose acetazolamide at least two days before arrival at high altitude, in contrast to usual recommendations, would be of greater beneficial effect on AMS development.


Discourse Trees Are Good Indicators of Importance in Text


title: State of the Art in Pulse-Repetitive Generation of GW-Level Superradiative Pulses

abstract: The superradiative regime of relativistic BWO allows achieving not only extremely high microwave power but under some conditions also the very stable parameters (peak power, central frequency and RF phase relatively voltage rise time) from one pulse to another. The moderately dispersive spread of group velocities in the frequency band of short microwave pulse might plays the important role for the peak power hold. The results of experiments on pulse-periodic generation of X-band (9.7 GHz) short pulses are presented. Microwave pulses with peak power up to 2 GW and 0.7 ns duration were produced with the use of magnetic field in the slow wave structure as high as 2.2 T. The repetition rate was 100 p.p.s. for the batches with duration up to 2 sec. The cathode voltage was of 370 kV and beam current was 4 kA. Conditions of stable output power value were found when the root mean square deviation of detector signals was in two times less than rms of accelerating voltage pulses. Because of the rise time of cathode voltage should be optimal, the ferrite-loaded section of coaxial transmission line was used. This method of voltage pulse sharpening could provide the reduction of rise time from 2.5 to 0.6 ns at the level 300-400 kV.


title: Using Machine Learning to Break Visual Human Interaction Proofs ( HIPs )

abstract: Machine learning is often used to automatically solve human tasks. In this paper, we look for tasks where machine learning algorithms are not as good as humans with the hope of gaining insight into their current limitations. We studied various Human Interactive Proofs (HIPs) on the market, because they are systems designed to tell computers and humans apart by posing challenges presumably too hard for computers. We found that most HIPs are pure recognition tasks which can easily be broken using machine learning. The harder HIPs use a combination of segmentation and recognition tasks. From this observation, we found that building segmentation tasks is the most effective way to confuse machine learning algorithms. This has enabled us to build effective HIPs (which we deployed in MSN Passport), as well as design challenging segmentation tasks for machine learning algorithms.


title: Network Interactions and Performance of a Multifunction IEC 61850 Process Bus

abstract: New substation technology, such as non-conventional instrument transformers,and a need to reduce design and construction costs, are driving the adoption of ::: Ethernet based digital process bus networks for high voltage substations. ::: Protection and control applications can share a process bus, making more efficient use of the network infrastructure. This paper classifies and defines ::: performance requirements for the protocols used in a process bus on the basis of application. These include GOOSE, SNMP and IEC 61850-9-2 sampled values. A method, based on the Multiple Spanning Tree Protocol (MSTP) and virtual local area networks, is presented that separates management and monitoring traffic from the rest of the process bus. A quantitative investigation of the ::: interaction between various protocols used in a process bus is described. These tests also validate the effectiveness of the MSTP based traffic segregation method. While this paper focusses on a substation automation network, the ::: results are applicable to other real-time industrial networks that implement multiple protocols. High volume sampled value data and time-critical circuit breaker tripping commands do not interact on a full duplex switched Ethernet network, even under very high network load conditions. This enables an efficient digital network to replace a large number of conventional analog ::: connections between control rooms and high voltage switchyards.


title: Hierarchical Neural Networks for Image Interpretation

abstract: I. Theory.- Neurobiological Background.- Related Work.- Neural Abstraction Pyramid Architecture.- Unsupervised Learning.- Supervised Learning.- II. Applications.- Recognition of Meter Values.- Binarization of Matrix Codes.- Learning Iterative Image Reconstruction.- Face Localization.- Summary and Conclusions.


title: Only Aggressive Elephants are Fast Elephants

abstract: Yellow elephants are slow. A major reason is that they consume their inputs entirely before responding to an elephant rider's orders. Some clever riders have trained their yellow elephants to only consume parts of the inputs before responding. However, the teaching time to make an elephant do that is high. So high that the teaching lessons often do not pay off. We take a different approach. We make elephants aggressive; only this will make them very fast. ::: ::: We propose HAIL (Hadoop Aggressive Indexing Library), an enhancement of HDFS and Hadoop MapReduce that dramatically improves runtimes of several classes of MapReduce jobs. HAIL changes the upload pipeline of HDFS in order to create different clustered indexes on each data block replica. An interesting feature of HAIL is that we typically create a win-win situation: we improve both data upload to HDFS and the runtime of the actual Hadoop MapReduce job. In terms of data upload, HAIL improves over HDFS by up to 60% with the default replication factor of three. In terms of query execution, we demonstrate that HAIL runs up to 68x faster than Hadoop. In our experiments, we use six clusters including physical and EC2 clusters of up to 100 nodes. A series of scalability experiments also demonstrates the superiority of HAIL.


title: Efficient bounds for the stable set, vertex cover and set packing problems

abstract: Abstract In this paper we describe a collection of efficient algorithms that deliver approximate solution to the weighted stable set, vertex cover and set packing problems. All algorithms guarantee bounds on the ratio of the heuristic solution to the optimal solution.


Random Forests In Language Modeling


Performance Comparison of AODV and DSR routing protocols in MANET : A study


title: Zero-Shot Learning via Joint Latent Similarity Embedding

abstract: Zero-shot recognition (ZSR) deals with the problem of predicting class labels for target domain instances based on source domain side information (e.g. attributes) of unseen classes. We formulate ZSR as a binary prediction problem. Our resulting classifier is class-independent. It takes an arbitrary pair of source and target domain instances as input and predicts whether or not they come from the same class, i.e. whether there is a match. We model the posterior probability of a match since it is a sufficient statistic and propose a latent probabilistic model in this context. We develop a joint discriminative learning framework based on dictionary learning to jointly learn the parameters of our model for both domains, which ultimately leads to our class-independent classifier. Many of the existing embedding methods can be viewed as special cases of our probabilistic model. On ZSR our method shows 4.90% improvement over the state-of-the-art in accuracy averaged across four benchmark datasets. We also adapt ZSR method for zero-shot retrieval and show 22.45% improvement accordingly in mean average precision (mAP).


title: A Two Phase Clustering Method for Intelligent Customer Segmentation

abstract: Customer Segmentation is an increasingly significant issue in today’s competitive commercial area. Many literatures have reviewed the application of data mining technology in customer segmentation, and achieved sound effectives. But in the most cases, it is performed using customer data from a special point of view, rather than from systematical method considering all stages of CRM. This paper, with the aid of data mining tools, constructs a new customer segmentation method based on RFM, demographic and LTV data. The new customer segmentation method consists of two phases. Firstly, with K-means clustering, customers are clustered into different segments regarding their RFM. Secondly, using demographic data, each cluster again is partitioned into new clusters. Finally, using LTV, a profile for customer is created. The method has been applied to a dataset from Iranian bank, which resulted in some useful management measures and suggestions.


title: Predicting Legislative Roll Calls from Text

abstract: We develop several predictive models linking legislative sentiment to legislative text. Our models, which draw on ideas from ideal point estimation and topic models, predict voting patterns based on the contents of bills and infer the political leanings of legislators. With supervised topics, we provide an exploratory window into how the language of the law is correlated with political support. We also derive approximate posterior inference algorithms based on variational methods. Across 12 years of legislative data, we predict specific voting patterns with high accuracy.


title: Looking forward to looking. backward: The misprediction of regret

abstract: Decisions are powerfully affected by anticipated regret, and people anticipate feeling more regret when they lose by a narrow margin than when they lose by a wide margin. But research suggests that people are remarkably good at avoiding self-blame, and hence they may be better at avoiding regret than they realize. Four studies measured people's anticipations and experiences of regret and self-blame. In Study 1, students overestimated how much more regret they would feel when they “nearly won” than when they “clearly lost” a contest. In Studies 2, 3a, and 3b, subway riders overestimated how much more regret and self-blame they would feel if they “nearly caught” their trains than if they “clearly missed” their trains. These results suggest that people are less susceptible to regret than they imagine, and that decision makers who pay to avoid future regrets may be buying emotional insurance that they do not actually need.


title: Empirical Principles and an Industrial Case Study in Retrieving Equivalent Requirements via Natural Language Processing Techniques

abstract: Though very important in software engineering, linking artifacts of the same type (clone detection) or different types (traceability recovery) is extremely tedious, error-prone, and effort-intensive. Past research focused on supporting analysts with techniques based on Natural Language Processing (NLP) to identify candidate links. Because many NLP techniques exist and their performance varies according to context, it is crucial to define and use reliable evaluation procedures. The aim of this paper is to propose a set of seven principles for evaluating the performance of NLP techniques in identifying equivalent requirements. In this paper, we conjecture, and verify, that NLP techniques perform on a given dataset according to both ability and the odds of identifying equivalent requirements correctly. For instance, when the odds of identifying equivalent requirements are very high, then it is reasonable to expect that NLP techniques will result in good performance. Our key idea is to measure this random factor of the specific dataset(s) in use and then adjust the observed performance accordingly. To support the application of the principles we report their practical application to a case study that evaluates the performance of a large number of NLP techniques for identifying equivalent requirements in the context of an Italian company in the defense and aerospace domain. The current application context is the evaluation of NLP techniques to identify equivalent requirements. However, most of the proposed principles seem applicable to evaluating any estimation technique aimed at supporting a binary decision (e.g., equivalent/nonequivalent), with the estimate in the range [0,1] (e.g., the similarity provided by the NLP), when the dataset(s) is used as a benchmark (i.e., testbed), independently of the type of estimator (i.e., requirements text) and of the estimation method (e.g., NLP).


title: Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas.

abstract: BACKGROUND ::: Diffuse low-grade and intermediate-grade gliomas (which together make up the lower-grade gliomas, World Health Organization grades II and III) have highly variable clinical behavior that is not adequately predicted on the basis of histologic class. Some are indolent; others quickly progress to glioblastoma. The uncertainty is compounded by interobserver variability in histologic diagnosis. Mutations in IDH, TP53, and ATRX and codeletion of chromosome arms 1p and 19q (1p/19q codeletion) have been implicated as clinically relevant markers of lower-grade gliomas. ::: ::: ::: METHODS ::: We performed genomewide analyses of 293 lower-grade gliomas from adults, incorporating exome sequence, DNA copy number, DNA methylation, messenger RNA expression, microRNA expression, and targeted protein expression. These data were integrated and tested for correlation with clinical outcomes. ::: ::: ::: RESULTS ::: Unsupervised clustering of mutations and data from RNA, DNA-copy-number, and DNA-methylation platforms uncovered concordant classification of three robust, nonoverlapping, prognostically significant subtypes of lower-grade glioma that were captured more accurately by IDH, 1p/19q, and TP53 status than by histologic class. Patients who had lower-grade gliomas with an IDH mutation and 1p/19q codeletion had the most favorable clinical outcomes. Their gliomas harbored mutations in CIC, FUBP1, NOTCH1, and the TERT promoter. Nearly all lower-grade gliomas with IDH mutations and no 1p/19q codeletion had mutations in TP53 (94%) and ATRX inactivation (86%). The large majority of lower-grade gliomas without an IDH mutation had genomic aberrations and clinical behavior strikingly similar to those found in primary glioblastoma. ::: ::: ::: CONCLUSIONS ::: The integration of genomewide data from multiple platforms delineated three molecular classes of lower-grade gliomas that were more concordant with IDH, 1p/19q, and TP53 status than with histologic class. Lower-grade gliomas with an IDH mutation either had 1p/19q codeletion or carried a TP53 mutation. Most lower-grade gliomas without an IDH mutation were molecularly and clinically similar to glioblastoma. (Funded by the National Institutes of Health.).


title: Software-defined wireless network architectures for the Internet-of-Things

abstract: The Internet-of-Things (IoT) envisions a world where billions of everyday objects and mobile devices communicate using a large number of interconnected wired and wireless networks. Maximizing the utilization of this paradigm requires fine-grained QoS support for differentiated application requirements, context-aware semantic information retrieval, and quick and easy deployment of resources, among many other objectives. These objectives can only be achieved if components of the IoT can be dynamically managed end-to-end across heterogeneous objects, transmission technologies, and networking architectures. Software-defined Networking (SDN) is a new paradigm that provides powerful tools for addressing some of these challenges. Using a software-based control plane, SDNs introduce significant flexibility for resource management and adaptation of network functions. In this article, we study some promising solutions for the IoT based on SDN architectures. Particularly, we analyze the application of SDN in managing resources of different types of networks such as Wireless Sensor Networks (WSN) and mobile networks, the utilization of SDN for information-centric networking, and how SDN can leverage Sensing-as-a-Service (SaaS) as a key cloud application in the IoT.


title: Beyond Security and Privacy Perception: An Approach to Biometric Authentication Perception Change

abstract: The aim of the paper is to shed light on the factors affecting perception shifts in biometrics authentication. This study explores trust relationships in the adoption of biometrics using the valence framework to understand and explain the individual’s evaluation of risk concerning biometrics. Hypotheses are developed to suggest that individuals’ intention to use biometrics is influenced by trust in the vendor. An experiment to test the hypotheses is described. Expected contributions, limitations, and possibilities for future research are noted.


title: When sex is more than just sex : Attachment orientations, sexual experience, and relationship quality

abstract: The authors explored the contribution of individual differences in attachment orientations to the experience of sexual intercourse and its association with relationship quality. In Study 1, 500 participants completed self-report scales of attachment orientations and sexual experience. The findings indicated that whereas attachment anxiety was associated with an ambivalent construal of sexual experience, attachment avoidance was associated with more aversive sexual feelings and cognitions. In Study 2, 41 couples reported on their attachment orientations and provided daily diary measures of sexual experiences and relationship interactions for a period of 42 days. Results showed that attachment anxiety amplified the effects of positive and negative sexual experiences on relationship interactions. In contrast, attachment avoidance inhibited the positive relational effect of having sex and the detrimental relational effects of negative sexual interactions. The authors discuss the possibility that attachment orientations are associated with different sex-related strategies and goals within romantic relationships.


title: A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization

abstract: We present a general approach for collaborative filtering (CF) using spectral regularization to learn linear operators from "users" to the "objects" they rate. Recent low-rank type matrix completion approaches to CF are shown to be special cases. However, unlike existing regularization based CF methods, our approach can be used to also incorporate information such as attributes of the users or the objects -- a limitation of existing regularization based CF methods. We then provide novel representer theorems that we use to develop new estimation methods. We provide learning algorithms based on low-rank decompositions, and test them on a standard CF dataset. The experiments indicate the advantages of generalizing the existing regularization based CF methods to incorporate related information about users and objects. Finally, we show that certain multi-task learning methods can be also seen as special cases of our proposed approach.


title: Joint Attention in Autonomous Driving (JAAD)

abstract: In this paper we present a novel dataset for a critical aspect of autonomous driving, the joint attention that must occur between drivers and of pedestrians, cyclists or other drivers. This dataset is produced with the intention of demonstrating the behavioral variability of traffic participants. We also show how visual complexity of the behaviors and scene understanding is affected by various factors such as different weather conditions, geographical locations, traffic and demographics of the people involved. The ground truth data conveys information regarding the location of participants (bounding boxes), the physical conditions (e.g. lighting and speed) and the behavior of the parties involved.


Self-Driving: From General Purpose to Specialized DBMSs


title: A Streaming Motion Magnification Core for Smart Image Sensors

abstract: This brief proposes a modified Eulerian Video Magnification (EVM) algorithm and a hardware implementation of a motion magnification core for smart image sensors. Compared to the original EVM algorithm, we perform the pixel-wise temporal bandpass filtering only once rather than multiple times on all scale layers, to reduce the memory and multiplier requirement for hardware implementation. A pixel stream processing architecture with pipelined blocks is proposed for the magnification core, enabling it to readily fit common image sensing components with streaming pixel output, while achieving higher performance with lower system cost. We implemented an FPGA-based prototype that is able to process up to 90M pixels per second and magnify subtle motion. The motion magnification results are comparable to the original algorithm running on PC.


title: Deep Sparse Coding for Non–Intrusive Load Monitoring

abstract: Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This is a single channel (the only channel being the smart-meter) blind source (different electrical appliances) separation problem. The traditional way to address this is via stochastic finite state machines (e.g., factorial hidden Markov model). In recent times, dictionary learning-based approaches have shown promise in addressing the disaggregation problem. The usual technique is to learn a dictionary for every device and use the learned dictionaries as basis for blind source separation during disaggregation. Prior studies in this area are shallow learning techniques, i.e., they learn a single layer of dictionary for every device. In this paper, we propose a deep learning approach—instead of learning one level of dictionary, we learn multiple layers of dictionaries for each device. These multi-level dictionaries are used as a basis for source separation during disaggregation. Results on two benchmark datasets and one actual implementation show that our method outperforms state-of-the-art techniques.


title: PALLOC: DRAM bank-aware memory allocator for performance isolation on multicore platforms

abstract: DRAM consists of multiple resources called banks that can be accessed in parallel and independently maintain state information. In Commercial Off-The-Shelf (COTS) multicore platforms, banks are typically shared among all cores, even though programs running on the cores do not share memory space. In this situation, memory performance is highly unpredictable due to contention in the shared banks.


title: Performing an APT Investigation: Using People-Process-Technology-Strategy Model in Digital Triage Forensics

abstract: Taiwan has become the frontline in an emerging cyberspace battle. Cyber attacks from different countries are constantly reported during past decades. The incident of Advanced Persistent Threat (APT) is analyzed from the golden triangle components (people, process and technology) to ensure the application of digital forensics. This study presents a novel People-Process-Technology-Strategy (PPTS) model by implementing a triage investigative step to identify evidence dynamics in digital data and essential information in auditing logs. The result of this study is expected to improve APT investigation. The investigation scenario of this proposed methodology is illustrated by applying to some APT incidents in Taiwan.


title: Urban expansion monitoring and driving forces analysis: A case study of Jiangsu Province, China

abstract: Urban expansion and land use change was studied in Jiangsu Province with RS and GIS technique, and driving forces was analyzed. Cities studied in this paper include 13 prefecture cities and 51 county cities. Remote sensing images include Landsat MSS, TM and ETM with time series from 1979 to 2000.


title: CheckMate: Exploring a Tangible Augmented Reality Interface for Remote Interaction

abstract: The digitalized world comes with increasing Internet capabilities, allowing to connect persons over distance easier than ever before. Video conferencing and similar online applications create great benefits bringing people who physically cannot spend as much time as they want virtually together. However, such remote experiences can also tend to lose the feeling of traditional experiences. People lack direct visual presence and no haptic feedback is available. In this paper, we tackle this problem by introducing our system called CheckMate. We combine Augmented Reality and capacitive 3D printed objects that can be sensed on an interactive surface to enable remote interaction while providing the same tangible experience as in co-located scenarios. As a proof-of-concept, we implemented a sample application based on the traditional chess game.


title: Weighted-Graph-Coloring-Based Pilot Decontamination for Multicell Massive MIMO Systems

abstract: A multicell massive multiple-input multiple-output (MIMO) system, which utilizes a large number of base-station antennas to simultaneously serve a set of users, suffers from pilot contamination (PC) due to unavoidable reuse of pilots in adjacent cells. In this paper, a weighted-graph-coloring-based pilot decontamination (WGC-PD) scheme is proposed to mitigate PC for multicell massive MIMO systems. Specifically, based on limited cooperation among cells, an edge-weighted interference graph (EWIG) is first constructed to depict the potential PC relationship among users, whereby two users in different cells are connected by a weighted edge, indicating the strength of potential PC when they reuse the same pilot. Then, inspired by classical graph coloring algorithms, we develop the WGC-PD scheme by denoting each color as a pilot and each vertex as a user in the EWIG, which is able to mitigate PC by assigning different pilots to connected users with a large weight in a greedy way with insufficient pilot resource. Compared with exhaustive search among numerous pilot assignment solutions, the proposed WGC-PD scheme is able to mitigate PC with significantly reduced complexity, which is verified by numerical results.


title: Buried Object Detection from B-Scan Ground Penetrating Radar Data Using Faster-RCNN

abstract: In this paper, we adapt the Faster-RCNN framework for the detection of underground buried objects (i.e. hyperbola reflections) in B-scan ground penetrating radar (GPR) images. Due to the lack of real data for training, we propose to incorporate more simulated radargrams generated from different configurations using the gprMax toolbox. Our designed CNN is first pre-trained on the grayscale Cifar-l0 database. Then, the Faster-RCNN framework based on the pre-trained CNN is trained and fine-tuned on both real and simulated GPR data. Preliminary detection results show that the proposed technique can provide significant improvements compared to classical computer vision methods and hence becomes quite promising to deal with this kind of specific GPR data even with few training samples.


title: Developing a concept-level knowledge base for sentiment analysis in Singlish

abstract: In this paper, we present Singlish sentiment lexicon, a concept-level knowledge base for sentiment analysis that associates multiword expressions to a set of emotion labels and a polarity value. Unlike many other sentiment analysis resources, this lexicon is not built by manually labeling pieces of knowledge coming from general NLP resources such as WordNet or DBPedia. Instead, it is automatically constructed by applying graph-mining and multi-dimensional scaling techniques on the affective common-sense knowledge collected from three different sources. This knowledge is represented redundantly at three levels: semantic network, matrix, and vector space. Subsequently, the concepts are labeled by emotions and polarity through the ensemble application of spreading activation, neural networks and an emotion categorization model.


title: 3D printing of a pavlova

abstract: 3D food printing is emerging as a high-resolution computer-aided-design and additive-manufacturing approach for producing food products. The benefits offered by 3D food printing include custom design and production of visually appealing foods, making of foods for people with special needs (e.g., people with swallowing difficulties), reduction in design and fabrication time and cost, and decrease in dependency on skilled personnel. This paper presents three-dimensional (3D) printing of a pavlova with chocolate garnish. Since the pavlova mixture mainly consists of eggwhite foam, its 3D printing is a challenging exercise. The materials and equipment used to 3D print the pavlova and the chocolate garnish are described. The preparation of meringue, design of pavlova, printing of pavlova, baking of pavlova, design of chocolate garnish, printing of chocolate garnish, and assembly of the pavlova are explained. The obtained results are presented and discussed, and the new opportunities that 3D food printing can offer are highlighted.


title: Decision Support based on Bio-PEPA Modeling and Decision Tree Induction: A New Approach, Applied to a Tuberculosis Case Study

abstract: The problem of selecting determinant features generating appropriate model structure is a challenge in epidemiological modelling. Disease spread is highly complex, and experts develop their understanding of its dynamic over years. There is an increasing variety and volume of epidemiological data which adds to the potential confusion. The authors propose here to make use of that data to better understand disease systems. Decision tree techniques have been extensively used to extract pertinent information and improve decision making. In this paper, the authors propose an innovative structured approach combining decision tree induction with Bio-PEPA computational modelling, and illustrate the approach through application to tuberculosis. By using decision tree induction, the enhanced Bio-PEPA model shows considerable improvement over the initial model with regard to the simulated results matching observed data. The key finding is that the developer expresses a realistic predictive model using relevant features, thus considering this approach as decision support, empowers the epidemiologist in his policy decision making.


title: Physiological demands of competitive basketball

abstract: The aim of this study was to assess physiological demands of competitive basketball by measuring oxygen consumption (VO2) and other variables during practice games. Each of 12 players (20.4 � 1.1 years) was monitored in a 20-min practice game, which was conducted in the same way as actual games with the presence of referees and coaches. VO2 was measured by a portable system during the game and blood lactate concentration (LA) was measured in brief breaks. Subjects were also videotaped for time-motion analysis. Female and male players demonstrated respective VO2 of 33.4 � 4.0 and 36.9 � 2.6 mL/kg/min and LA of 3.2 � 0.9 and 4.2 � 1.3 mmol/L in the practice games (P40.05). They spent 34.1% of play time running and jumping, 56.8% walking, and 9.0% standing. Pre-obtained VO2max was correlated to VO2 during play (r 5 0.673) and to percent of duration for running and jumping (r 5 0.935 and 0.962 for females and males, respectively). This study demonstrated a greater oxygen uptake for competitive basketball than that estimated based on a previous compendium. The correlation between aerobic capacity and activity level suggests the potential benefit of aerobic conditioning in basketball.


title: Detecting camera based traitor and fraudulent apps on smartphone

abstract: In recent trends, android smartphones are rapidly in use by a number of users because of its user friendly and open source architecture. There are millions of android applications are available in the open market place. But we cannot trust most of the apps, which may steal the user privacy content or any confidential data. However, some works have studied mobile phones, multimedia securities and also android permissions. In this paper, to focus on the security issues related to camera based attacks on smartphones. The fraudulent application and its traitor can be detected by using the defense system that detects the attacks. By use of the defense system the users can be saved before getting into the vulnerability. To evaluate the defense system we have proposed a camera based fraudulent application for feasibility study and also we analyzed the performance of the system with the fraudulent applications from the open android market.


title: A new bio-inspired optimisation algorithm: Bird Swarm Algorithm

abstract: A new bio-inspired algorithm, namely Bird Swarm Algorithm (BSA), is proposed for solving optimisation applications. BSA is based on the swarm intelligence extracted from the social behaviours and social interactions in bird swarms. Birds mainly have three kinds of behaviours: foraging behaviour, vigilance behaviour and flight behaviour. Birds may forage for food and escape from the predators by the social interactions to obtain a high chance of survival. By modelling these social behaviours, social interactions and the related swarm intelligence, four search strategies associated with five simplified rules are formulated in BSA. Simulations and comparisons based on eighteen benchmark problems demonstrate the effectiveness, superiority and stability of BSA. Some proposals for future research about BSA are also discussed.


title: End-to-End Transport Security in the IP-Based Internet of Things

abstract: The IP-based Internet of Things refers to the interconnection of smart devices in a Low-power and Lossy Network (LLN) with the Internet by means of protocols such as 6LoWPAN or CoAP. The mechanisms to protect the LLN from attacks from the Internet and provisioning of an end-to-end (E2E) secure connection are key requirements for functionalities ranging from network access to software updates. Interconnecting such resource constrained devices with high-performance machines requires new security mechanisms that cannot be covered by already known solutions. This paper describes attacks at transport layer against the LLN launched from the Internet. It also introduces approaches to ensure E2E security between two devices located in homogeneous networks using either HTTP/TLS or CoAP/DTLS by proposing a mapping between TLS and DTLS.


title: A Review of Co-saliency Detection Technique: Fundamentals, Applications, and Challenges

abstract: Co-saliency detection is a newly emerging and rapidly growing research area in computer vision community. As a novel branch of visual saliency, co-saliency detection refers to the discovery of common and salient foregrounds from two or more relevant images, and can be widely used in many computer vision tasks. The existing co-saliency detection algorithms mainly consist of three components: extracting effective features to represent the image regions, exploring the informative cues or factors to characterize co-saliency, and designing effective computational frameworks to formulate co-saliency. Although numerous methods have been developed, the literature is still lacking a deep review and evaluation of co-saliency detection techniques. In this paper, we aim at providing a comprehensive review of the fundamentals, challenges, and applications of co-saliency detection. Specifically, we provide an overview of some related computer vision works, review the history of co-saliency detection, summarize and categorize the major algorithms in this research area, discuss some open issues in this area, present the potential applications of co-saliency detection, and finally point out some unsolved challenges and promising future works. We expect this review to be beneficial to both fresh and senior researchers in this field, and give insights to researchers in other related areas regarding the utility of co-saliency detection algorithms.


title: Ghosting-free multi-exposure image fusion in gradient domain

abstract: This paper presents an algorithm to produce ghosting-free High Dynamic Range (HDR) image by fusing set of multiple exposed images in gradient domain. Recently proposed Gradient domain based exposure fusion method provides high quality result but the scope of which is limited to static camera without foreground object motion. The presence of moving objects/hand shake produces a set of misaligned images. The result of gradient domain approach on misaligned images suffers from ghosting artifacts. In order to produce better HDR image without image registration, we propose to create an aligned image set from input image set by photometric calibration. The gradient of aligned image set is then used to reconstruct the fused final image. The proposed algorithm tested on several publicly available dynamic image sets shows that resultant HDR image is ghosting-free and well exposed. Additionally, the proposed method is fast and thus can be used in consumer appliances such as mobile phones, portable devices with digital cameras.


title: Pattern Discovery from Stock Time Series Using Self-Organizing Maps †

abstract: † This work was supported by the RGC CERG project PolyU 5065/98E and the Departmental Grant H-ZJ84 ‡ Corresponding author ABSTRACT Pattern discovery from time series is of fundamental importance. Particularly when the domain expert derived patterns do not exist or are not complete, an algorithm to discover specific patterns or shapes automatically from the time series data is necessary. Such an algorithm is noteworthy in that it does not assume prior knowledge of the number of interesting structures, nor does it require an exhaustive explanation of the patterns being described. In this paper, a clustering approach is proposed for pattern discovery from time series. In view of its popularity and superior clustering performance, the self-organizing map (SOM) was adopted for pattern discovery in temporal data sequences. It is a special type of clustering algorithm that imposes a topological structure on the data. To prepare for the SOM algorithm, data sequences are segmented from the numerical time series using a continuous sliding window. Similar temporal patterns are then grouped together using SOM into clusters, which may subsequently be used to represent different structures of the data or temporal patterns. Attempts have been made to tackle the problem of representing patterns in a multi-resolution manner. With the increase in the number of data points in the patterns (the length of patterns), the time needed for the discovery process increases exponentially. To address this problem, we propose to compress the input patterns by a perceptually important point (PIP) identification algorithm. The idea is to replace the original data segment by its PIP’s so that the dimensionality of the input pattern can be reduced. Encouraging results are observed and reported for the application of the proposed methods to the time series collected from the Hong Kong stock market.


Abstract Neural Network Applications in Stock Market Predictions- A Methodology Analysis


title: Towards an orientation enhanced astar algorithm for robotic navigation

abstract: This paper presents an algorithm capable of generating smooth, feasible paths for an any-shape non-holonomic mobile robot, taking into account orientation restrictions, with the aim of navigating close to obstacles. Our contribution consists in an extension of the A∗ algorithm in a cell decomposition, where besides its position, the orientation of the platform is also considered when searching for a path. This is achieved by constructing 16 layers of orientations and only visiting neighbor layers when searching for the lowest cost. To simplify collision checking, the robot's footprint is used to inflate obstacles, yet, to allow the robot to find paths close to obstacles, the actual footprint of the robot must used. By discretizing the orientation space into layers and computing an oriented footprint for each layer, the actual footprint of the robot is used, increasing the configuration space without becoming computationally expensive. The path planning algorithm was developed under the EU-funded project CARLoS1 and was implemented in a stud welding robot simulated within a naval industry environment, validating our approach.


title: Linking Localisation and Language Resources

abstract: Industrial localisation is changing from the periodic translation of large bodies of content to a long-tail of small, heterogeneous translations processed in an agile and demand-driven manner. Software localisation and crowd-source translation already practice continuous fine-grained distribution of translation work. This requires close integration and round-trip interoperability between content creation and localisation processes, while at the same time recording the provenance of translated content to maximise it reuse in future translation tasks, and, increasingly, in training Statistical Machine Translation (SMT) engines. This work adopts a Linked Data approach to integrating the content translation round-trip process with the logging of process quality assurance provenance. This integration supports a pull-based interoperability model that supports continuous synchronising of content and process meta-data between the generating organisation and any number of language service providers or translators. We present a platform architecture for sharing, searching and interlinking of Linked Localisation and Language Data (termed L3Data) on the web. This is accomplished using a semantic schema for L3Data that is compatible with existing localisation data exchange standards and can be used to support the round-trip sharing of language resources. The paper describes our approach to development of L3Data schema and data management processes, web-based tools and data sharing infrastructure that use it. An initial proof of concept prototype is presented which implements a web application that segments and machine translates content for crowd-sourced post-editing and rating.


title: Design and Implementation of a Three-Phase Buck-Type Third Harmonic Current Injection PFC Rectifier SR

abstract: In this paper, a three-phase buck-type unity power factor rectifier is designed for high-power electric vehicle battery charging mains interfaces. The characteristics of the converter, named the Swiss Rectifier (SR), including the principle of operation, modulation strategy, suitable control structure, and dimensioning equations are described in detail. Exemplarily, a 7.5 kW hardware prototype is then designed based on the derived analytical expressions and the feasibility of the SR concept is demonstrated by the means of experimental measurements. Finally, the SR is compared with a conventional six-switch buck-type ac–dc power conversion. According to the results, the SR is the topology of choice for a buck-type PFC.


title: Accelerating the nonuniform Fast Fourier Transform

abstract: The nonequispaced Fourier transform arises in a variety of application areas, from medical imaging to radio astronomy to the numerical solution of partial differential equations. In a typical problem, one is given an irregular sampling of N data in the frequency domain and one is interested in reconstructing the corresponding function in the physical domain. When the sampling is uniform, the fast Fourier transform (FFT) allows this calculation to be computed in O(N log N ) operations rather than O(N 2 ) operations. Unfortunately, when the sampling is nonuniform, the FFT does not apply. Over the last few years, a number of algorithms have been developed to overcome this limitation and are often referred to as nonuniform FFTs (NUFFTs). These rely on a mixture of interpolation and the judicious use of the FFT on an oversampled grid (A. Dutt and V. Rokhlin, SIAM J. Sci. Comput., 14 (1993), pp. 1368-1383). In this paper, we observe that one of the standard interpolation or "gridding" schemes, based on Gaussians, can be accelerated by a significant factor without precomputation and storage of the interpolation weights. This is of particular value in two- and three- dimensional settings, saving either 10 d N in storage in d dimensions or a factor of about 5-10 in CPUtime (independent of dimension).


title: Extended Dot Cluster Marker for High-speed 3D Tracking in Dynamic Projection Mapping

abstract: The technique of Projection Mapping, which is useful for merging real-world geometry with an augmented appearance, is a promising core technology for augmented reality (AR). In recent years, dynamically changing environments, mainly a consequence of the growing demand for interactive user experiences, have contributed to a new style of AR applications. However, performance levels of current systems for realizing 3D effects, in terms of the tracking speed and projection ability, are insufficient to meet these demands. In this paper, we present a high-speed, occlusion-robust marker-based 3D tracking method achieved by only using a monocular monochrome image. The objective of our research is to develop an automatic marker design method for any 3D shape and an effective framework for stabilizing tracking at high throughput by extending the latest promising work based on a deformable dot cluster marker [46]. Furthermore, this tracking method was used in combination with a high-speed projector, both of which can achieve high throughput and low latency, on the order of milliseconds. This enabled us to realize a high-quality computational display capable of representing the material appearance of a dynamically moving target. The demonstration showed that the effect of a dynamically changing appearance with nearly imperceptible latency drastically enriches the sense of immersion in the recognition of augmented materials with the naked eye.


title: Content-Aware Display Adaptation and Interactive Editing for Stereoscopic Images

abstract: We propose a content-aware stereoscopic image display adaptation method which simultaneously resizes a binocular image to the target resolution and adapts its depth to the comfort zone of the display while preserving the perceived shapes of prominent objects. This method does not require depth information or dense correspondences. Given the specification of the target display and a sparse set of correspondences, our method efficiently deforms the input stereoscopic images for display adaptation by solving a least-squares energy minimization problem. This can be used to adjust stereoscopic images to fit displays with different real estates, aspect ratios and comfort zones. In addition, with slight modifications to the energy function, our method allows users to interactively adjust the sizes, locations and depths of the selected objects, giving users aesthetic control for depth perception. User studies show that the method is effective at editing depth and reducing occurrences of diplopia and distortions.


title: PipeDream: Fast and Efficient Pipeline Parallel DNN Training

abstract: PipeDream is a Deep Neural Network(DNN) training system for GPUs that parallelizes computation by pipelining execution across multiple machines. Its pipeline parallel computing model avoids the slowdowns faced by data-parallel training when large models and/or limited network bandwidth induce high communication-to-computation ratios. PipeDream reduces communication by up to 95% for large DNNs relative to data-parallel training, and allows perfect overlap of communication and computation. PipeDream keeps all available GPUs productive by systematically partitioning DNN layers among them to balance work and minimize communication, versions model parameters for backward pass correctness, and schedules the forward and backward passes of different inputs in round-robin fashion to optimize"time to target accuracy". Experiments with five different DNNs on two different clusters show that PipeDream is up to 5x faster in time-to-accuracy compared to data-parallel training.


title: Interpretable Partitioned Embedding for Customized Multi-item Fashion Outfit Composition

abstract: Intelligent fashion outfit composition becomes more and more popular in these years. Some deep learning based approaches reveal competitive composition recently. However, the uninterpretable characteristic makes such deep learning based approach cannot meet the designers, businesses and consumers' urge to comprehend the importance of different attributes in an outfit composition. To realize interpretable and customized multi-item fashion outfit compositions, we propose a partitioned embedding network to learn interpretable embeddings from clothing items. The network consists of two vital components: attribute partition module and partition adversarial module. In the attribute partition module, multiple attribute labels are adopted to ensure that different parts of the overall embedding correspond to different attributes. In the partition adversarial module, adversarial operations are adopted to achieve the independence of different parts. With the interpretable and partitioned embedding, we then construct an outfit composition graph and an attribute matching map. Extensive experiments demonstrate that 1) the partitioned embedding have unmingled parts which corresponding to different attributes and 2) outfits recommended by our model are more desirable in comparison with the existing methods.


title: Representation Learning on Graphs with Jumping Knowledge Networks

abstract: Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose a strategy to overcome those. In particular, the range of "neighboring" nodes that a node's representation draws from strongly depends on the graph structure, analogous to the spread of a random walk. To adapt to local neighborhood properties and tasks, we explore an architecture -- jumping knowledge (JK) networks -- that flexibly leverages, for each node, different neighborhood ranges to enable better structure-aware representation. In a number of experiments on social, bioinformatics and citation networks, we demonstrate that our model achieves state-of-the-art performance. Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.


title: Design and Implementation of SecPod, A Framework for Virtualization-Based Security Systems

abstract: The OS kernel is critical to the security of a computer system. Many systems have been proposed to improve its security. A fundamental weakness of those systems is that page tables, the data structures that control the memory protection, are not isolated from the vulnerable kernel, and thus subject to tampering. To address that, researchers have relied on virtualization for reliable kernel memory protection. Unfortunately, such memory protection requires to monitor every update to the guest's page tables. This fundamentally conflicts with the recent advances in the hardware virtualization support. In this paper, we present the design and implementation of SecPod, a practical and extensible framework for virtualization-based security systems that can provide both strong isolation and the compatibility with modern hardware. SecPod has two key techniques: paging delegation delegates and audits the kernel's paging operations to a secure space; execution trapping intercepts the (compromised) kernel's attempts to subvert SecPod by misusing privileged instructions. We have implemented a prototype of SecPod based on KVM. Our experiments show that SecPod is both effective and efficient.


title: Feature learning with deep scattering for urban sound analysis

abstract: In this paper we evaluate the scattering transform as an alternative signal representation to the mel-spectrogram in the context of unsupervised feature learning for urban sound classification. We show that we can obtain comparable (or better) performance using the scattering transform whilst reducing both the amount of training data required for feature learning and the size of the learned codebook by an order of magnitude. In both cases the improvement is attributed to the local phase invariance of the representation. We also observe improved classification of sources in the background of the auditory scene, a result that provides further support for the importance of temporal modulation in sound segregation.


title: Simulation as an engine of physical scene understanding

abstract: In a glance, we can perceive whether a stack of dishes will topple, a branch will support a child’s weight, a grocery bag is poorly packed and liable to tear or crush its contents, or a tool is firmly attached to a table or free to be lifted. Such rapid physical inferences are central to how people interact with the world and with each other, yet their computational underpinnings are poorly understood. We propose a model based on an “intuitive physics engine,” a cognitive mechanism similar to computer engines that simulate rich physics in video games and graphics, but that uses approximate, probabilistic simulations to make robust and fast inferences in complex natural scenes where crucial information is unobserved. This single model fits data from five distinct psychophysical tasks, captures several illusions and biases, and explains core aspects of human mental models and common-sense reasoning that are instrumental to how humans understand their everyday world.


title: DAWS: Exploiting Crossbar Characteristics for Improving Write Performance of High Density Resistive Memory

abstract: Resistive random access memory (RRAM) is promising to be used as high density storage-class memory by employing crossbar structure. However, the wire resistance in crossbar array causes the IR drop problem, which makes nonuniformity of write latency throughout the array. In large crossbar array, the write latency differs greatly even in the same row. Since the write latency of a region is determined by its slowest write-unit, the conventional group-by-row region partition and addressing scheme is suboptimal for improving the overall performance of RRAM. In this work, we present DAWS, a novel RRAM architecture that exploits intrinsic features of crossbar structure. We first build a circuit model to analyze the voltage distribution and write latency distribution in a crossbar array. Then we propose a voltage bias scheme to optimize write latency via minimizing the IR drop path. We further present block diagonal partition to narrow the variance of write latency within each region, thus the write latency of each region is reduced. Moreover, we provide block diagonal addressing to make the write latency monotonically increase with the physical address, which is in favor of address mapping and memory allocation. We also design diagonal writing and diagonal swapping to overlap SET and RESET operations by applying a particular voltage bias pattern that can exploit row level parallelism, thus the number of write operations is halved. The experimental results show that DAWS can reduce memory access latency by 24.0% and improve system performance by 29.7% over an aggressive baseline.


title: Adaptive Knee Joint Exoskeleton Based on Biological Geometries

abstract: This paper presents a relatively complete analytical model of a knee joint interacting with a two-link exoskeleton for investigating the effects of different exoskeleton designs on the internal joint forces/torque in the knee. The closed kinematic chain formed by the leg and exoskeleton has a significant effect on the joint forces/torque in the knee. A bio-joint model is used to capture this effect by relaxing a commonly made assumption that approximates a knee joint as a perfect engineering pin-joint in designing an exoskeleton. Based on the knowledge of a knee-joint kinematics, an adaptive knee-joint exoskeleton has been designed to eliminate negative effects associated with the closed leg-exoskeleton kinematic chain on a human knee. For experimental validation, the flexion motion of an artificial human knee is investigated comparing the performances of five exoskeleton designs against the case with no exoskeleton. Analytical results that estimate internal forces/torque using the kinematic and dynamic models (based on the properties of a knee joint) agree well with data obtained experimentally. This investigation illustrates the applications of the analytical model for designing an adaptive exoskeleton that minimizes internal joint forces due to a knee-exoskeleton interaction.


title: Using Genetic Algorithm to Optimize Artificial Neural Network: A Case Study on Earthquake Prediction

abstract: By integrating the global searching advantage of Genetic Algorithm (GA) and the local searching ability of BP Artificial Neural Network (BP ANN), this paper proposes a new model of BP ANN based on GA (called GA-BP ANN). Firstly, it applies GA to optimize the initial interconnecting weights and thresholds of BP ANN. Then, it utilizes the BP algorithm to train the neural network more accurately. This method can speed up the convergence and avoid local minimum of BP ANN. The experiments of earthquake prediction with general BP ANN and optimized GA-BP ANN are respectively conducted as a case study. The results show that the BP ANN optimized with GA can not only get better network configurations, but also improve the efficiency, precision and stability of earthquake prediction.


title: Normative Social Influence in Persuasive Technology: Intensity versus Effectiveness

abstract: It has been established that normative social influence can be used effectively in persuasive technology. However, it is unknown whether the application of more social pressure makes it more effective. To test this hypothesis, a quantitative experiment was conducted on the online social network Facebook. Although evidence to support the hypothesis was found, it cannot be concluded from this experiment that more intense persuasion is more effective, when utilizing normative social influence in persuasive technology.


title: Expressing Emotions with the Social Robot Probo

abstract: Probo is a huggable animal-like robot, designed to act as a social interface. It will be used as a platform to study human robot interaction (HRI) while employing human-like social cues and communication modalities. The robot has a fully actuated head, with 20 degrees of freedom, capable of showing facial expressions and making eye-contact. The basic facial expressions are represented as a vector in the 2-dimensional emotion space based on Russel’s circomplex model of affect (Posner et al. in Dev. Psychopathol. 17(03):715–734, 2005). The recognition of the underlying emotions based on the robot’s facial expressions were tested in different user studies and compared with similar robotic projects. This paper describes the concepts of the robot Probo and the ability to express emotional states.


title: Robust and Efficient Robotic Mapping

abstract: Mobile robots are dependent upon a model of the environment for many of their basic functions. Locally accurate maps are critical to collision avoidance, while large-scale maps (accurate both metrically and topologically) are necessary for efficient route planning. Solutions to these problems have immediate and important applications to autonomous vehicles, precision surveying, and domestic robots. ::: Building accurate maps can be cast as an optimization problem: find the map that is most probable given the set of observations of the environment. However, the problem rapidly becomes difficult when dealing with large maps or large numbers of observations. Sensor noise and non-linearities make the problem even more difficult—especially when using inexpensive (and therefore preferable) sensors. ::: This thesis describes an optimization algorithm that can rapidly estimate the maximum likelihood map given a set of observations. The algorithm, which iteratively reduces map error by considering a single observation at a time, scales well to large environments with many observations. The approach is particularly robust to noise and non-linearities, quickly escaping local minima that trap current methods. Both batch and online versions of the algorithm are described. ::: In order to build a map, however, a robot must first be able to recognize places that it has previously seen. Limitations in sensor processing algorithms, coupled with environmental ambiguity, make this difficult. Incorrect place recognitions can rapidly lead to divergence of the map. This thesis describes a place recognition algorithm that can robustly handle ambiguous data. ::: We evaluate these algorithms on a number of challenging datasets and provide quantitative comparisons to other state-of-the-art methods, illustrating the advantages of our methods. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)


title: Evolutionary Generation of Whole Test Suites

abstract: Recent advances in software testing allow automatic derivation of tests that reach almost any desired point in the source code. There is, however, a fundamental problem with the general idea of targeting one distinct test coverage goal at a time: Coverage goals are neither independent of each other, nor is test generation for any particular coverage goal guaranteed to succeed. We present EvoSuite, a search-based approach that optimizes whole test suites towards satisfying a coverage criterion, rather than generating distinct test cases directed towards distinct coverage goals. Evaluated on five open source libraries and an industrial case study, we show that EvoSuite achieves up to 18 times the coverage of a traditional approach targeting single branches, with up to 44% smaller test suites.


An analysis of the causes of increased error rates in children²s speech recognition.


THE UBISENSE SMART SPACE PLATFORM


title: Pneumatic Artificial Muscles : actuators for robotics and automation

abstract: This article is intended as an introduction to and an overview of Pneumatic Artificial Muscles (PAMs). These are pneumatic actuators made mainly of a flexible and inflatable membrane. First, their concept and way of operation are explained. Next, the properties of these actuators are given, the most important of which are the compliant behavior and extremely low weight. A classification and review is following this section. Typical applications are dealt with in the last but one section and, finally, some concluding remarks are made.


title: Salicylic Acid Is Involved in the Basal Resistance of Tomato Plants to Citrus Exocortis Viroid and Tomato Spotted Wilt Virus

abstract: Tomato plants expressing the NahG transgene, which prevents accumulation of endogenous salicylic acid (SA), were used to study the importance of the SA signalling pathway in basal defence against Citrus Exocortis Viroid (CEVd) or Tomato Spotted Wilt Virus (TSWV). The lack of SA accumulation in the CEVd- or TSWV-infected NahG tomato plants led to an early and dramatic disease phenotype, as compared to that observed in the corresponding parental Money Maker. Addition of acibenzolar-S-methyl, a benzothiadiazole (BTH), which activates the systemic acquired resistance pathway downstream of SA signalling, improves resistance of NahG tomato plants to CEVd and TSWV. CEVd and TSWV inoculation induced the accumulation of the hydroxycinnamic amides p-coumaroyltyramine, feruloyltyramine, caffeoylputrescine, and feruloylputrescine, and the defence related proteins PR1 and P23 in NahG plants earlier and with more intensity than in Money Maker plants, indicating that SA is not essential for the induction of these plant defence metabolites and proteins. In addition, NahG plants produced very high levels of ethylene upon CEVd or TSWV infection when compared with infected Money Maker plants, indicating that the absence of SA produced additional effects on other metabolic pathways. This is the first report to show that SA is an important component of basal resistance of tomato plants to both CEVd and TSWV, indicating that SA-dependent defence mechanisms play a key role in limiting the severity of symptoms in CEVd- and TSWV-infected NahG tomato plants.


title: Spiral-generated modular FFT algorithms

abstract: This paper presents an extension of the Spiral system to automatically generate and optimize FFT algorithms for the discrete Fourier transform over finite fields. The generated code is intended to support modular algorithms for multivariate polynomial computations in the modpn library used by Maple. The resulting code provides an order of magnitude speedup over the original implementations in the modpn library, and the Spiral system provides the ability to automatically tune the FFT code to different computing platforms.


title: ARTICLE NO. OB972679 Coping with Uncertainty: A Naturalistic Decision-Making Analysis

abstract: Abstract This paper is concerned with three questions: How do decision makers conceptualize uncertainty? How do decision makers cope with uncertainty? Are there systematic relationships between different conceptualizations of uncertainty and different methods of coping? To answer these questions we analyzed 102 self-reports of decision-making under uncertainty with an inclusive method of classifying conceptualizations of uncertainty and coping mechanisms developed from the decision-making literature. The results showed that decision makers distinguished among three types of uncertainty: inadequate understanding, incomplete information, and undifferentiated alternatives. To these they applied five strategies of coping: reducing uncertainty, assumption-based reasoning, weighing pros and cons of competing alternatives, suppressing uncertainty, and forestalling. Inadequate understanding was primarily managed by reduction, incomplete information was primarily managed by assumption-based reasoning, and conflict among alternatives was primarily managed by weighing pros and cons. Based on these results and findings from previous studies of naturalistic decision-making we hypothesized a R.A.W.F.S. (Reduction, Assumption-based reasoning, Weighing pros and cons, Suppression, and Hedging) heuristic, which describes the strategies that decision makers apply to different types of uncertainty in naturalistic settings.


title: Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms

abstract: A novel hybrid learning algorithm based on a genetic algorithm to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on genetic algorithms (SOFNNGA), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. A new adding method based on geometric growing criterion and the epsiv-completeness of fuzzy rules is first used to generate the initial structure. Then a hybrid algorithm based on genetic algorithms, backpropagation, and recursive least squares estimation is used to adjust all parameters including the number of fuzzy rules. This has two steps: First, the linear parameter matrix is adjusted, and second, the centers and widths of all membership functions are modified. The GA is introduced to identify the least important neurons, i.e., the least important fuzzy rules. Simulations are presented to illustrate the performance of the proposed algorithm


title: Scan-Along Polygonal Approximation for Data Compression of Electrocardiograms

abstract: Three fast and efficient "scan-along" algorithms for compressing digitized electrocardiographic data are described. These algorithms are "scan-along" in the sense that they produce the compressed data in real time as the electrocardiogram is generated. The algorithms are based on the minimum perimeter polygonal approximation for digitized curves. The approximation restricts the maximum error to be no greater than a specified value. Our algorithms achieve a compression ratio of ten on a database of 8000 5-beat abnormal electrocardiograms sampled at 250 Hz and a compression ratio of eleven on a database of 600 3-beat normal electrocardiograms (different from the preceding database) sampled at 500 Hz.


title: A survey of maximum PPT techniques of PV systems

abstract: This paper introduces a survey of different maximum peak power tracking (MPPT) techniques used in the implementation of photovoltaic power systems. It will discuss different 30 techniques used in tracking maximum power in photovoltaic arrays. This paper can be considered as a completion, updating, and declaration of the good efforts made in [3], that discussed 19 MPPT techniques in PV systems, while summarizes additional 11 MPPT methods.


title: Contextual Ranking of Keywords Using Click Data

abstract: The problem of automatically extracting the most interesting and relevant keyword phrases in a document has been studied extensively as it is crucial for a number of applications. These applications include contextual advertising, automatic text summarization, and user-centric entity detection systems. All these applications can potentially benefit from a successful solution as it enables computational efficiency (by decreasing the input size), noise reduction, or overall improved user satisfaction.In this paper, we study this problem and focus on improving the overall quality of user-centric entity detection systems. First, we review our concept extraction technique, which relies on search engine query logs. We then define a new feature space to represent the interestingness of concepts, and describe a new approach to estimate their relevancy for a given context. We utilize click through data obtained from a large scale user-centric entity detection system - Contextual Shortcuts - to train a model to rank the extracted concepts, and evaluate the resulting model extensively again based on their click through data. Our results show that the learned model outperforms the baseline model, which employs similar features but whose weights are tuned carefully based on empirical observations, and reduces the error rate from 30.22% to 18.66%.


title: One-shot learning of manipulation skills with online dynamics adaptation and neural network priors

abstract: One of the key challenges in applying reinforcement learning to complex robotic control tasks is the need to gather large amounts of experience in order to find an effective policy for the task at hand. Model-based reinforcement learning can achieve good sample efficiency, but requires the ability to learn a model of the dynamics that is good enough to learn an effective policy. In this work, we develop a model-based reinforcement learning algorithm that combines prior knowledge from previous tasks with online adaptation of the dynamics model. These two ingredients enable highly sample-efficient learning even in regimes where estimating the true dynamics is very difficult, since the online model adaptation allows the method to locally compensate for unmodeled variation in the dynamics. We encode the prior experience into a neural network dynamics model, adapt it online by progressively refitting a local linear model of the dynamics, and use model predictive control to plan under these dynamics. Our experimental results show that this approach can be used to solve a variety of complex robotic manipulation tasks in just a single attempt, using prior data from other manipulation behaviors.


title: Expanded Parts Model for Semantic Description of Humans in Still Images

abstract: We introduce an Expanded Parts Model (EPM) for recognizing human attributes (e.g., young, short hair, wearing suits) and actions (e.g., running, jumping) in still images. An EPM is a collection of part templates which are learnt discriminatively to explain specific scale-space regions in the images (in human centric coordinates). This is in contrast to current models which consist of a relatively few (i.e., a mixture of) ‘average’ templates. EPM uses only a subset of the parts to score an image and scores the image sparsely in space, i.e., it ignores redundant and random background in an image. To learn our model, we propose an algorithm which automatically mines parts and learns corresponding discriminative templates together with their respective locations from a large number of candidate parts. We validate our method on three recent challenging datasets of human attributes and actions. We obtain convincing qualitative and state-of-the-art quantitative results on the three datasets.


title: Evaluating deep variational autoencoders trained on pan-cancer gene expression

abstract: Cancer is a heterogeneous disease with diverse molecular etiologies and outcomes. The Cancer Genome Atlas (TCGA) has released a large compendium of over 10,000 tumors with RNA-seq gene expression measurements. Gene expression captures the diverse molecular profiles of tumors and can be interrogated to reveal differential pathway activations. Deep unsupervised models, including Variational Autoencoders (VAE) can be used to reveal these underlying patterns. We compare a one-hidden layer VAE to two alternative VAE architectures with increased depth. We determine the additional capacity marginally improves performance. We train and compare the three VAE architectures to other dimensionality reduction techniques including principal components analysis (PCA), independent components analysis (ICA), non-negative matrix factorization (NMF), and analysis of gene expression by denoising autoencoders (ADAGE). We compare performance in a supervised learning task predicting gene inactivation pan-cancer and in a latent space analysis of high grade serous ovarian cancer (HGSC) subtypes. We do not observe substantial differences across algorithms in the classification task. VAE latent spaces offer biological insights into HGSC subtype biology.


title: ADef: an Iterative Algorithm to Construct Adversarial Deformations

abstract: While deep neural networks have proven to be a powerful tool for many recognition and classification tasks, their stability properties are still not well understood. In the past, image classifiers have been shown to be vulnerable to so-called adversarial attacks, which are created by additively perturbing the correctly classified image. ::: In this paper, we propose the ADef algorithm to construct a different kind of adversarial attack created by iteratively applying small deformations to the image, found through a gradient descent step. We demonstrate our results on MNIST with a convolutional neural network and on ImageNet with Inception-v3 and ResNet-101.


title: Intrusion Detection and Prevention for ZigBee-Based Home Area Networks in Smart Grids

abstract: In this paper, we present a novel intrusion detection and prevention system for ZigBee-based home area networks in smart grids, HANIDPS. HANIDPS employs a model-based intrusion detection mechanism as well as a machine learning-based intrusion prevention system to protect the network against a wide range of attack types. The detection module extracts network features and analyzes them to decide whether the network is in a normal state. We use smart energy profile 2.0 specification as well as IEEE 802.15.4 standard to precisely characterize the expected normal behavior. A set of defensive actions are defined for the prevention system which are effective in stopping various attack types. HANIDPS uses Q-learning and through interactions with environment learns the best strategy against an attack. Use of model-based approach for intrusion detection and dynamic learning for intrusion prevention, as well as employment of effective mechanisms to stop the attacks, provide a high performance for HANIDPS without the need for prior knowledge of the attacks. Soundness of the proposed method is evaluated through extensive analysis and experiments.


title: To Create What You Tell: Generating Videos from Captions

abstract: We are creating multimedia contents everyday and everywhere. While automatic content generation has played a fundamental challenge to multimedia community for decades, recent advances of deep learning have made this problem feasible. For example, the Generative Adversarial Networks (GANs) is a rewarding approach to synthesize images. Nevertheless, it is not trivial when capitalizing on GANs to generate videos. The difficulty originates from the intrinsic structure where a video is a sequence of visually coherent and semantically dependent frames. This motivates us to explore semantic and temporal coherence in designing GANs to generate videos. In this paper, we present a novel Temporal GANs conditioning on Captions, namely TGANs-C, in which the input to the generator network is a concatenation of a latent noise vector and caption embedding, and then is transformed into a frame sequence with 3D spatio-temporal convolutions. Unlike the naive discriminator which only judges pairs as fake or real, our discriminator additionally notes whether the video matches the correct caption. In particular, the discriminator network consists of three discriminators: video discriminator classifying realistic videos from generated ones and optimizes video-caption matching, frame discriminator discriminating between real and fake frames and aligning frames with the conditioning caption, and motion discriminator emphasizing the philosophy that the adjacent frames in the generated videos should be smoothly connected as in real ones. We qualitatively demonstrate the capability of our TGANs-C to generate plausible videos conditioning on the given captions on two synthetic datasets (SBMG and TBMG) and one real-world dataset (MSVD). Moreover, quantitative experiments on MSVD are performed to validate our proposal via Generative Adversarial Metric and human study.


title: Kalman filter based multiple objects detection-tracking algorithm robust to occlusion

abstract: Visual target tracking is one of the major fields in computer vision system. Object tracking has many practical applications such as automated surveillance system, military guidance, traffic management system, fault detection system, artificial intelligence and robot vision system. But it is difficult to track objects with image sensor. Especially, multiple objects tracking is harder than single object tracking. This paper proposes multiple objects tracking algorithm based on the Kalman filter. Our algorithm uses the Kalman filter as many as the number of moving objects in the image frame. If many moving objects exist in the image, however, we obtain multiple measurements. Therefore, precise data association is necessary in order to track multiple objects correctly. Another problem of multiple objects tracking is occlusion that causes merge and split. For solving these problems, this paper defines the cost function using some factors. Experiments using Matlab show that the performance of the proposed algorithm is appropriate for multiple objects tracking in real-time.


title: Memristive switching mechanism for metal/oxide/metal nanodevices

abstract: Nanoscale metal/oxide/metal switches have the potential to transform the market for nonvolatile memory and could lead to novel forms of computing. However, progress has been delayed by difficulties in understanding and controlling the coupled electronic and ionic phenomena that dominate the behaviour of nanoscale oxide devices. An analytic theory of the 'memristor' (memory-resistor) was first developed from fundamental symmetry arguments in 1971, and we recently showed that memristor behaviour can naturally explain such coupled electron-ion dynamics. Here we provide experimental evidence to support this general model of memristive electrical switching in oxide systems. We have built micro- and nanoscale TiO2 junction devices with platinum electrodes that exhibit fast bipolar nonvolatile switching. We demonstrate that switching involves changes to the electronic barrier at the Pt/TiO2 interface due to the drift of positively charged oxygen vacancies under an applied electric field. Vacancy drift towards the interface creates conducting channels that shunt, or short-circuit, the electronic barrier to switch ON. The drift of vacancies away from the interface annilihilates such channels, recovering the electronic barrier to switch OFF. Using this model we have built TiO2 crosspoints with engineered oxygen vacancy profiles that predictively control the switching polarity and conductance.


title: Memory Management Support for Multi-Programmed Remote Direct Memory Access (RDMA) Systems

abstract: Current operating systems offer basic support for network interface controllers (NICs) supporting remote direct memory access (RDMA). Such support typically consists of a device driver responsible for configuring communication channels between the device and user-level processes but not involved in data transfer. Unlike standard NICs, RDMA-capable devices incorporate significant memory resources for address translation purposes. In a multi-programmed operating system (OS) environment, these memory resources must be efficiently shareable by multiple processes. For such sharing to occur in a fair manner, the OS and the device must cooperate to arbitrate access to NIC memory, similar to the way CPUs and OSes cooperate to arbitrate access to translation lookaside buffers (TLBs) or physical memory. A problem with this approach is that today's RDMA NICs are not integrated into the functions provided by OS memory management systems. As a result, RDMA NIC hardware resources are often monopolized by a single application. In this paper, I propose two practical mechanisms to address this problem: (a) Use of RDMA only in kernel-resident I/O subsystems, transparent to user-level software; (b) An extended registration API and a kernel upcall mechanism delivering NIC TLB entry replacement notifications to user-level libraries. Both options are designed to re-instate the multiprogramming principles that are violated in early commercial RDMA systems


title: A linear time 5/3-approximation for the minimum strongly-connected spanning subgraph problem

abstract: A linear time 5/3-approximation algorithm is presented for the NP-hard problem of finding a minimum strongly-connected spanning subgraph. It is based on cycle contraction that was first introduced by Khuller, Raghavachari and Young [SIAM J. Comput. 24 (1995) 859-872]. We improve their result by contracting special cycles and utilizing a more efficient data structure.


title: 3D path planning for mobile robots using annealing neural network

abstract: This paper presents a novel potential field based 3D path planning technique for differential drive mobile robots, moving in known environment. The path planners are based on description of the obstacles by simulated annealing neural networks. The generated paths are piecewise linear with changing directions at the corners of the obstacles. The proposed planner can be successfully applied to snake robots, flying robots, and control of Gantry cranes. Several simulation results show the effectiveness of the proposed algorithm.


title: Automatic identification of whiteflies, aphids and thrips in greenhouse based on image analysis

abstract: Automatic identification of the selected pest insects in greenhouse, whiteflies, aphids, and thrips, was carried out on the specimens collected on yellow sticky traps. Algorithms for image processing were proposed, implemented and tested in this study. Various morphological features of specimens were extracted and analyzed. The size and color components of object were selected as the features for automatic identification. Aphids were easy to discriminate because they have small variation in color information and the body size is substantially different from other species. Identification error was reduced when the data for thrips and whiteflies were analyzed after the aphids were recognized. The reference values of size and color components sampled from 50 insects for each species were accordingly used to characterize the species and methods.


title: The relationship of loneliness and social anxiety with children’s and adolescents’ online communication

abstract: Children and adolescents now communicate online to form and/or maintain relationships with friends, family, and strangers. Relationships in “real life” are important for children’s and adolescents’ psychosocial development; however, they can be difficult for those who experience feelings of loneliness and/or social anxiety. The aim of this study was to investigate differences in usage of online communication patterns between children and adolescents with and without self-reported loneliness and social anxiety. Six hundred and twenty-six students aged between 10-16 years completed a survey on the amount of time they spent communicating online, the topics they discussed, the partners they engaged with, and their purposes for communicating over the Internet. Participants were administered a shortened version of the UCLA Loneliness Scale and an abbreviated sub-scale of the Social Anxiety Scale for Adolescents (SAS-A). Additionally, age and gender differences in usage of the aforementioned online communication patterns were examined across the entire sample. Findings revealed that children and adolescents who self-reported being lonely communicated online significantly more frequently about personal things and intimate topics than did those who did not self-report being lonely. The former were motivated to use online communication significantly more frequently to compensate for their weaker social skills to meet new people. Results suggest that Internet usage allows them to fulfill critical needs of social interactions, self-disclosure, and identity exploration. Future research, however, should explore whether or not the benefits derived from online communication may also facilitate lonely children’s and adolescents’ offline social relationships.


title: Anatomy of machine learning algorithm implementations in MPI, Spark, and Flink

abstract: With the ever-increasing need to analyze large amounts of data to get useful insights, it is essential to develop complex parallel machine learning algorithms that can scale with data and number of parallel processes. These algorithms need to run on large data sets as well as they need to be executed with minimal time in order to extract useful information in a time-constrained environment. Message passing interface MPI is a widely used model for developing such algorithms in high-performance computing paradigm, while Apache Spark and Apache Flink are emerging as big data platforms for large-scale parallel machine learning. Even though these big data frameworks are designed differently, they follow the data flow model for execution and user APIs. Data flow model offers fundamentally different capabilities than the MPI execution model, but the same type of parallelism can be used in applications developed in both models. This article presents three distinct machine learning algorithms implemented in MPI, Spark, and Flink and compares their performance and identifies strengths and weaknesses in each platform.


title: Contrasting information theoretic decompositions of modulatory and arithmetic interactions in neural information processing systems

abstract: Biological and artificial neural systems are composed of many local processors, and their capabilities depend upon the transfer function that relates each local processor's outputs to its inputs. This paper uses a recent advance in the foundations of information theory to study the properties of local processors that use contextual input to amplify or attenuate transmission of information about their driving inputs. This advance enables the information transmitted by processors with two distinct inputs to be decomposed into those components unique to each input, that shared between the two inputs, and that which depends on both though it is in neither, i.e. synergy. The decompositions that we report here show that contextual modulation has information processing properties that contrast with those of all four simple arithmetic operators, that it can take various forms, and that the form used in our previous studies of artificial neural nets composed of local processors with both driving and contextual inputs is particularly well-suited to provide the distinctive capabilities of contextual modulation under a wide range of conditions. We argue that the decompositions reported here could be compared with those obtained from empirical neurobiological and psychophysical data under conditions thought to reflect contextual modulation. That would then shed new light on the underlying processes involved. Finally, we suggest that such decompositions could aid the design of context-sensitive machine learning algorithms.